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    <title>Li&#39;s Bioinfo-Blog</title>
    <link>https://lishensuo.github.io/</link>
    <description>Recent content on Li&#39;s Bioinfo-Blog</description>
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      <title>统计学基础--统计描述指标与常见统计分布</title>
      <link>https://lishensuo.github.io/posts/basic/001%E7%BB%9F%E8%AE%A1%E5%AD%A6%E5%9F%BA%E7%A1%80--%E7%BB%9F%E8%AE%A1%E6%8F%8F%E8%BF%B0%E6%8C%87%E6%A0%87%E4%B8%8E%E5%B8%B8%E8%A7%81%E7%BB%9F%E8%AE%A1%E5%88%86%E5%B8%83/</link>
      <pubDate>Mon, 18 Apr 2022 00:00:00 +0000</pubDate>
      
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      <description>统计的对象是数据，其中会涉及到不同类型的数据；统计的目的是描述，描述一组数据的分布特征、两组至多组数据之间的关系。 1、数据分类 根据数据特点，</description>
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      <title>Python基础-(1)初识</title>
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      <pubDate>Sun, 10 Apr 2022 00:00:00 +0000</pubDate>
      
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      <description>print()打印 1 2 3 4 5 6 7 8 print(&amp;#34;Hello, world!&amp;#34;) #打印多个字符串，默认用空格链接 print(&amp;#34;A&amp;#34;, &amp;#34;B&amp;#34;, &amp;#34;C&amp;#34;) print(&amp;#34;A&amp;#34;, &amp;#34;B&amp;#34;, &amp;#34;C&amp;#34;,sep=&amp;#34;,&amp;#34;) #`+`可直接拼接字符串 print(&amp;#34;A&amp;#34;+&amp;#34;B&amp;#34;,&amp;#34;C&amp;#34;) #AB C input()取用户输入 1 2</description>
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      <title>统计学基础--以T检验为例学习基于中心极限定理的假设检验</title>
      <link>https://lishensuo.github.io/posts/basic/002%E7%BB%9F%E8%AE%A1%E5%AD%A6%E5%9F%BA%E7%A1%80--%E4%BB%A5t%E6%A3%80%E9%AA%8C%E4%B8%BA%E4%BE%8B%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8E%E4%B8%AD%E5%BF%83%E6%9E%81%E9%99%90%E5%AE%9A%E7%90%86%E7%9A%84%E5%81%87%E8%AE%BE%E6%A3%80%E9%AA%8C/</link>
      <pubDate>Tue, 10 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/002%E7%BB%9F%E8%AE%A1%E5%AD%A6%E5%9F%BA%E7%A1%80--%E4%BB%A5t%E6%A3%80%E9%AA%8C%E4%B8%BA%E4%BE%8B%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8E%E4%B8%AD%E5%BF%83%E6%9E%81%E9%99%90%E5%AE%9A%E7%90%86%E7%9A%84%E5%81%87%E8%AE%BE%E6%A3%80%E9%AA%8C/</guid>
      <description>统计学常用样本数据推断总体结果，或者说使用样本统计量推断总体参数。包含如下意义： （1）对于样本数据，根据问题的不同，会有不同的形式。例如单纯</description>
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      <title>conda环境下载管理软件、R包</title>
      <link>https://lishensuo.github.io/posts/bioinfo/002conda%E7%8E%AF%E5%A2%83%E4%B8%8B%E8%BD%BD%E7%AE%A1%E7%90%86%E8%BD%AF%E4%BB%B6r%E5%8C%85/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
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      <description>conda开源包管理系统和环境管理系统 ，包括多种语言的包安装，运行，更新，删除，最重要的是可以解决包依赖问题。在做生信分析时，感觉就是一个软</description>
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      <title>Python基础-(2)列表、元组、字符串、字典</title>
      <link>https://lishensuo.github.io/posts/program/002python%E5%9F%BA%E7%A1%80-2%E5%88%97%E8%A1%A8%E5%85%83%E7%BB%84%E5%AD%97%E7%AC%A6%E4%B8%B2%E5%AD%97%E5%85%B8/</link>
      <pubDate>Sun, 10 Apr 2022 00:00:00 +0000</pubDate>
      
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      <description>关于序列 序列：有顺序的元素排列；第一个元素的索引为0 列表list，元组tuple，字符串str都属于序列；但一般情况下，只有list可修改，</description>
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      <title>统计学基础--方差分析与卡方检验</title>
      <link>https://lishensuo.github.io/posts/basic/003%E7%BB%9F%E8%AE%A1%E5%AD%A6%E5%9F%BA%E7%A1%80--%E6%96%B9%E5%B7%AE%E5%88%86%E6%9E%90%E4%B8%8E%E5%8D%A1%E6%96%B9%E6%A3%80%E9%AA%8C/</link>
      <pubDate>Fri, 13 May 2022 00:00:00 +0000</pubDate>
      
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      <description>方差分析(analysis of variance)可以简单的理解为多组间的均值比较。例如A班、B班、C班、D班的考试成绩均值是否存在显著差异。方</description>
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      <title>Python基础-(3)条件与循环语句</title>
      <link>https://lishensuo.github.io/posts/program/003python%E5%9F%BA%E7%A1%80-3%E6%9D%A1%E4%BB%B6%E4%B8%8E%E5%BE%AA%E7%8E%AF%E8%AF%AD%E5%8F%A5/</link>
      <pubDate>Sun, 10 Apr 2022 00:00:00 +0000</pubDate>
      
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      <description>一、条件语句 1、布尔值 在python中，这些值均为视为假：False, None, 0, &#39;&#39;,(), [], {}。其余所有可能的值都视为真 1 2 3 4 5 6 7 bool(None) # False bool(&amp;#39;&amp;#39;) # False bool(&amp;#34;anything&amp;#34;) # True</description>
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      <title>统计学基础--相关性与一致性</title>
      <link>https://lishensuo.github.io/posts/basic/004%E7%BB%9F%E8%AE%A1%E5%AD%A6%E5%9F%BA%E7%A1%80--%E7%9B%B8%E5%85%B3%E6%80%A7%E4%B8%8E%E4%B8%80%E8%87%B4%E6%80%A7/</link>
      <pubDate>Thu, 02 Jun 2022 00:00:00 +0000</pubDate>
      
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      <description>一、相关性 1、线性相关系数 （1）线性相关系数用于描述多个样本数据中两个变量之间的线性相关的程度。 变量通常为连续型变量； 两个变量一般为描述样本</description>
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      <title>服务器外网文件传输--奶牛快传、阿里云盘</title>
      <link>https://lishensuo.github.io/posts/bioinfo/004%E6%9C%8D%E5%8A%A1%E5%99%A8%E5%A4%96%E7%BD%91%E6%96%87%E4%BB%B6%E4%BC%A0%E8%BE%93--%E5%A5%B6%E7%89%9B%E5%BF%AB%E4%BC%A0%E9%98%BF%E9%87%8C%E4%BA%91%E7%9B%98/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
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      <description>1、奶牛快传 (1) linux服务器安装 https://github.com/Mikubill/cowtransfer-uploader/releases 选择一个最新的linux版本下载（如果wget下载太慢，就下载的本地电脑，再上传到linux） 解压即用 1</description>
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      <title>Python基础-(4)函数Function</title>
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      <pubDate>Sun, 10 Apr 2022 00:00:00 +0000</pubDate>
      
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      <description>1、定义函数 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 # def语句定义函数 def hello(name): print(&amp;#34;Hello,&amp;#34;, name) # 添加return语句有两个作用 ## (1) 表示函数的结束 ## (2)</description>
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    <item>
      <title>统计学基础--二分类模型评价指标</title>
      <link>https://lishensuo.github.io/posts/basic/005%E7%BB%9F%E8%AE%A1%E5%AD%A6%E5%9F%BA%E7%A1%80--%E4%BA%8C%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E8%AF%84%E4%BB%B7%E6%8C%87%E6%A0%87/</link>
      <pubDate>Thu, 09 Jun 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/005%E7%BB%9F%E8%AE%A1%E5%AD%A6%E5%9F%BA%E7%A1%80--%E4%BA%8C%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E8%AF%84%E4%BB%B7%E6%8C%87%E6%A0%87/</guid>
      <description>1、混淆矩阵相关指标 假设某医生对100个挂号的人进行某疾病的判断，具体情况如下 前提已知这100人的真实患病情况：60人患病，40人健康 医生根</description>
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    <item>
      <title>单细胞分析工具--cellranger</title>
      <link>https://lishensuo.github.io/posts/bioinfo/005%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--cellranger%E6%AF%94%E5%AF%B9/</link>
      <pubDate>Tue, 24 May 2022 00:00:00 +0000</pubDate>
      
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      <description>单细胞转录组测序技术历经10余年的发展，目前以高通量的Droplet技术广为应用。其中以10X Genomics公司开发的实验及分析系统最为流</description>
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      <title>Python基础-(5)类Class与实例Instance</title>
      <link>https://lishensuo.github.io/posts/program/005python%E5%9F%BA%E7%A1%80-5%E7%B1%BBclass%E4%B8%8E%E5%AE%9E%E4%BE%8Binstance/</link>
      <pubDate>Sun, 10 Apr 2022 00:00:00 +0000</pubDate>
      
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      <description>面向对象的设计思想是抽象出Class(类)，根据Class创建Instance(实例)。 类是一个相对抽象的概念，而实例是其具体化。例如将学生</description>
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      <title>统计学基础--重抽样Bootstrap与置换检验permutation test</title>
      <link>https://lishensuo.github.io/posts/basic/006%E7%BB%9F%E8%AE%A1%E5%AD%A6%E5%9F%BA%E7%A1%80--%E9%87%8D%E6%8A%BD%E6%A0%B7bootstrap%E4%B8%8E%E7%BD%AE%E6%8D%A2%E6%A3%80%E9%AA%8Cpermutation-test/</link>
      <pubDate>Sun, 10 Jul 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/006%E7%BB%9F%E8%AE%A1%E5%AD%A6%E5%9F%BA%E7%A1%80--%E9%87%8D%E6%8A%BD%E6%A0%B7bootstrap%E4%B8%8E%E7%BD%AE%E6%8D%A2%E6%A3%80%E9%AA%8Cpermutation-test/</guid>
      <description>重抽样本质上是从观测数据中反复抽取数据，有两种不同用法 1、Bootstrap （1）Bootstrap的核心是有放回的抽样，常用于估计统计量(</description>
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    <item>
      <title>单细胞分析工具--Seurat4基础流程</title>
      <link>https://lishensuo.github.io/posts/bioinfo/006%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--seurat4%E5%9F%BA%E7%A1%80%E6%B5%81%E7%A8%8B/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/006%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--seurat4%E5%9F%BA%E7%A1%80%E6%B5%81%E7%A8%8B/</guid>
      <description>1 2 3 4 5 6 packageVersion(&amp;#34;Seurat&amp;#34;) # [1] ‘4.0.6’ library(Seurat) library(tidyverse) library(ggplot2) library(clustree) 0、导入数据方式 （1）cellranger比对结果 对每个测序样本数据经cellranger上游比对，</description>
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      <title>Python基础-(6)捕捉异常Error</title>
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      <pubDate>Sun, 10 Apr 2022 00:00:00 +0000</pubDate>
      
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      <description>1、什么是异常 异常通常是指因各种原因的出错程序代码不能正常运行，从而返回报错信息，中断程序。 报错信息由两部分组成（1）Traceback：追</description>
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    <item>
      <title>统计学-生信中常见统计指标</title>
      <link>https://lishensuo.github.io/posts/basic/010%E7%BB%9F%E8%AE%A1%E5%AD%A6--%E7%94%9F%E4%BF%A1%E4%B8%AD%E5%B8%B8%E8%A7%81%E7%BB%9F%E8%AE%A1%E6%8C%87%E6%A0%87/</link>
      <pubDate>Sun, 10 Jul 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/010%E7%BB%9F%E8%AE%A1%E5%AD%A6--%E7%94%9F%E4%BF%A1%E4%B8%AD%E5%B8%B8%E8%A7%81%E7%BB%9F%E8%AE%A1%E6%8C%87%E6%A0%87/</guid>
      <description>1、KS统计量 Kolmogorov-Smirnov Tests Gene Set Enrichment Analysis https://github.com/franapoli/signed-ks-test 1 2 3 4 5 6 7 8 9 10 11 12 13 source(&amp;#34;signed-ks-test.R&amp;#34;) # c(&amp;#34;two.sided&amp;#34;, &amp;#34;less&amp;#34;, &amp;#34;greater&amp;#34;) ks = ks.test.2(c(2,10,11,20), 1:100, alternative = &amp;#34;greater&amp;#34;) # Two-sample Kolmogorov-Smirnov test # data: c(2, 10, 11, 20) and 1:100 # D^+ = 0.8, p-value = 0.007277 # alternative hypothesis: the CDF of x lies above that of y</description>
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    <item>
      <title>Python基础-(7)模块/包Module</title>
      <link>https://lishensuo.github.io/posts/program/007python%E5%9F%BA%E7%A1%80-7%E6%A8%A1%E5%9D%97%E5%9F%BA%E7%A1%80module/</link>
      <pubDate>Sun, 10 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/007python%E5%9F%BA%E7%A1%80-7%E6%A8%A1%E5%9D%97%E5%9F%BA%E7%A1%80module/</guid>
      <description>1、加载模块 模块简单来说是用来定义函数、类、变量的python脚本文件。 模块的分类 根据来源不同：可分为内置模块、第三方模块以及自建模块。 无论</description>
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    <item>
      <title>Python基础-(8)常用基础模块Module</title>
      <link>https://lishensuo.github.io/posts/program/008python%E5%9F%BA%E7%A1%80-8%E5%B8%B8%E7%94%A8%E5%86%85%E7%BD%AE%E6%A8%A1%E5%9D%97module/</link>
      <pubDate>Sun, 17 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/008python%E5%9F%BA%E7%A1%80-8%E5%B8%B8%E7%94%A8%E5%86%85%E7%BD%AE%E6%A8%A1%E5%9D%97module/</guid>
      <description>1. sys 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ##(1) sys.argv 给脚本传参 #如下定义一个sys.py脚本 #!/usr/bin/env python import sys print(sys.argv[0]) #第0个参数为脚本名 print(sys.argv[1]) python sys.py first # sys.py # first ##(2) sys.path 一个列表，包</description>
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    <item>
      <title>单细胞分析工具--monocle轨迹分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/008%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--monocle%E8%BD%A8%E8%BF%B9%E5%88%86%E6%9E%90/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/008%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--monocle%E8%BD%A8%E8%BF%B9%E5%88%86%E6%9E%90/</guid>
      <description>一、monocle 参考教程/笔记 （1）http://cole-trapnell-lab.github.io/monocle-release/</description>
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    <item>
      <title>Python基础-(9)文本读写与pickle保存</title>
      <link>https://lishensuo.github.io/posts/program/009python%E5%9F%BA%E7%A1%80-9%E6%96%87%E6%9C%AC%E8%AF%BB%E5%86%99%E4%B8%8Epickle%E4%BF%9D%E5%AD%98/</link>
      <pubDate>Sun, 17 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/009python%E5%9F%BA%E7%A1%80-9%E6%96%87%E6%9C%AC%E8%AF%BB%E5%86%99%E4%B8%8Epickle%E4%BF%9D%E5%AD%98/</guid>
      <description>1、打开文件 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ## (1) r 只能读取。若文件不存在，会报错。 f = open(&amp;#34;test.txt&amp;#34;, &amp;#34;r&amp;#34;) #由于默认mode=&amp;#34;r&amp;#34;， 等</description>
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    <item>
      <title>单细胞分析工具--CellChat细胞通讯分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/009%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--cellchat%E7%BB%86%E8%83%9E%E9%80%9A%E8%AE%AF%E5%88%86%E6%9E%90/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/009%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--cellchat%E7%BB%86%E8%83%9E%E9%80%9A%E8%AE%AF%E5%88%86%E6%9E%90/</guid>
      <description>主要参考资料： 1、https://htmlpreview.github.io/?https://github.com/sqjin/CellC</description>
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    <item>
      <title>Python-Dunder双下划线特殊方法</title>
      <link>https://lishensuo.github.io/posts/program/010python-dunder%E5%8F%8C%E4%B8%8B%E5%88%92%E7%BA%BF%E7%89%B9%E6%AE%8A%E6%96%B9%E6%B3%95/</link>
      <pubDate>Wed, 16 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/010python-dunder%E5%8F%8C%E4%B8%8B%E5%88%92%E7%BA%BF%E7%89%B9%E6%AE%8A%E6%96%B9%E6%B3%95/</guid>
      <description>特殊类变量 在定义类时，Python 提供了一些特殊方法（也称为“魔术方法”或“dunder 方法”，因为它们的名称前后都有双下划线 __, double under</description>
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    <item>
      <title>单细胞分析工具--pySCENIC转录因子分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/010%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--%E8%BD%AC%E5%BD%95%E5%9B%A0%E5%AD%90pyscenic/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/010%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--%E8%BD%AC%E5%BD%95%E5%9B%A0%E5%AD%90pyscenic/</guid>
      <description>SCENIC全称Single-Cell rEgulatory Network Inference and Clustering，即转录因子分析，用于构建GRN，gene regulatory network基因调控网络。2</description>
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    <item>
      <title>Python-装饰器Decorator</title>
      <link>https://lishensuo.github.io/posts/program/011python-%E8%A3%85%E9%A5%B0%E5%99%A8decorator/</link>
      <pubDate>Wed, 16 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/011python-%E8%A3%85%E9%A5%B0%E5%99%A8decorator/</guid>
      <description>在 Python 中，装饰器（decorator）是一种特殊的函数或类，用于在不修改原始代码的情况下，动态地修改或增强函数、方法或类的行为。 函数：独立的代</description>
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    <item>
      <title>单细胞分析工具--Palantir轨迹分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/011%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--palantir%E8%BD%A8%E8%BF%B9%E5%88%86%E6%9E%90-/</link>
      <pubDate>Thu, 12 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/011%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--palantir%E8%BD%A8%E8%BF%B9%E5%88%86%E6%9E%90-/</guid>
      <description>Paper：https://www.nature.com/articles/s41587-019-0068-4 github：https://</description>
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    <item>
      <title>Python-tdigest分位数统计</title>
      <link>https://lishensuo.github.io/posts/program/012python-tdigest%E5%88%86%E4%BD%8D%E6%95%B0%E7%BB%9F%E8%AE%A1/</link>
      <pubDate>Thu, 23 Jan 2025 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/012python-tdigest%E5%88%86%E4%BD%8D%E6%95%B0%E7%BB%9F%E8%AE%A1/</guid>
      <description>tdigest python工具 是一种用于高效计算近似分位数的数据结构和算法。相较于传统的分位数计算方法，tdigest可以实现以较小的内存开销和较快的计</description>
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    <item>
      <title>使用Seurat包分析空间转录组数据</title>
      <link>https://lishensuo.github.io/posts/bioinfo/015%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--seurat%E7%A9%BA%E9%97%B4%E8%BD%AC%E5%BD%95%E7%BB%84/</link>
      <pubDate>Sun, 05 Jun 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/015%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--seurat%E7%A9%BA%E9%97%B4%E8%BD%AC%E5%BD%95%E7%BB%84/</guid>
      <description>单细胞数据分析常用的Seurat包也集成了空间转录组的分析流程。 https://satijalab.org/seurat/articles/</description>
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    <item>
      <title>单细胞分析工具--hdWGCNA共表达网络</title>
      <link>https://lishensuo.github.io/posts/bioinfo/016%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--hdwgcna%E5%85%B1%E8%A1%A8%E8%BE%BE%E7%BD%91%E7%BB%9C/</link>
      <pubDate>Thu, 07 Jul 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/016%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--hdwgcna%E5%85%B1%E8%A1%A8%E8%BE%BE%E7%BD%91%E7%BB%9C/</guid>
      <description>WGCNA是适用于大批量样本的array或者Bulk RNAseq数据的加权基因共表达网络分析。由于单细胞数据的稀疏性，不适用于WGCNA直接</description>
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    <item>
      <title>单细胞分析工具--ECAUGT提取hECA数据</title>
      <link>https://lishensuo.github.io/posts/bioinfo/017%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--ecaugt%E6%8F%90%E5%8F%96heca%E6%95%B0%E6%8D%AE/</link>
      <pubDate>Thu, 19 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/017%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--ecaugt%E6%8F%90%E5%8F%96heca%E6%95%B0%E6%8D%AE/</guid>
      <description>基于先前的hECA文献笔记，学习使用python工具ECAUGHT高效提取特定类型的人类单细胞图谱数据。值得注意的是hECA对不同来源数据集</description>
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    <item>
      <title>Python-lambda函数表达式</title>
      <link>https://lishensuo.github.io/posts/program/015python-lambda%E5%87%BD%E6%95%B0%E8%A1%A8%E8%BE%BE%E5%BC%8F/</link>
      <pubDate>Sat, 17 Sep 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/015python-lambda%E5%87%BD%E6%95%B0%E8%A1%A8%E8%BE%BE%E5%BC%8F/</guid>
      <description>lambda表达式可用于定义简单的一行式函数，并且可搭配其它函数时有多种衍生用法。 https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html https://www.learncodewithmike.com/2019/12/python-lambda-functions.html https://pythonviz.com/pandas/3-ways-to-use-pandas-apply-in-python/ 1、基础用法 如下所示，lambda表达式有3个组分 关</description>
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    <item>
      <title>单细胞分析工具--细胞周期分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/020%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--%E7%BB%86%E8%83%9E%E5%91%A8%E6%9C%9F%E5%88%86%E6%9E%90/</link>
      <pubDate>Sun, 22 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/020%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--%E7%BB%86%E8%83%9E%E5%91%A8%E6%9C%9F%E5%88%86%E6%9E%90/</guid>
      <description>A cell cycle is a series of events that takes place in a cell as it grows and divides.即描述细胞生长、分裂整个过程中细胞变化过程。最重要的两个特点就是DNA复制、分裂成两个一</description>
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    <item>
      <title>Python-threading多线程</title>
      <link>https://lishensuo.github.io/posts/program/016python-threading%E5%A4%9A%E7%BA%BF%E7%A8%8B/</link>
      <pubDate>Mon, 19 Sep 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/016python-threading%E5%A4%9A%E7%BA%BF%E7%A8%8B/</guid>
      <description>python内置的threading模块可以实现多任务的多线程并行处理，如下简单记录一下用法。 参考链接 https://zhuanlan.zhihu.com/p/34004179 https://www.runoob.com/python3/python3-multithreading.html 1、threading 1 2 3 4 5 6</description>
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    <item>
      <title>单细胞分析工具--多样本批次校正</title>
      <link>https://lishensuo.github.io/posts/bioinfo/021%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--%E5%A4%9A%E6%A0%B7%E6%9C%AC%E6%89%B9%E6%AC%A1%E6%A0%A1%E6%AD%A3/</link>
      <pubDate>Sun, 22 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/021%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--%E5%A4%9A%E6%A0%B7%E6%9C%AC%E6%89%B9%E6%AC%A1%E6%A0%A1%E6%AD%A3/</guid>
      <description>当分析多个样本的单细胞数据集时，其中重要的一步是判断并校正潜在的批次效应。如下简单学习两种单细胞批次效应分析方法，分别基于Seurat与ha</description>
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    <item>
      <title>单细胞分析工具--SingleCellExperiment对象格式</title>
      <link>https://lishensuo.github.io/posts/bioinfo/022%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--singlecellexperiment%E5%AF%B9%E8%B1%A1%E6%A0%BC%E5%BC%8F/</link>
      <pubDate>Sun, 22 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/022%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--singlecellexperiment%E5%AF%B9%E8%B1%A1%E6%A0%BC%E5%BC%8F/</guid>
      <description>SingleCellExperiment是通过SingleCellExperiment包创建的单细胞数据分析对象，已有几十个单细胞R包支持。</description>
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    <item>
      <title>单细胞分析工具--infercnv拷贝数变异鉴定</title>
      <link>https://lishensuo.github.io/posts/bioinfo/018%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--infercnv%E6%8B%B7%E8%B4%9D%E6%95%B0%E5%8F%98%E5%BC%82%E9%89%B4%E5%AE%9A/</link>
      <pubDate>Fri, 20 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/018%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--infercnv%E6%8B%B7%E8%B4%9D%E6%95%B0%E5%8F%98%E5%BC%82%E9%89%B4%E5%AE%9A/</guid>
      <description>inferCNV包可根据肿瘤组织相关的单细胞表达数据，推测肿瘤细胞的拷贝数变异情况，从而完成恶性细胞的鉴定。 1 2 3 BiocManager::install(&amp;#34;infercnv&amp;#34;) library(infercnv) # 暂时在window安</description>
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    <item>
      <title>单细胞分析工具--scanpy基础用法(anndata,loom格式)</title>
      <link>https://lishensuo.github.io/posts/bioinfo/023%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--scanpy%E5%9F%BA%E7%A1%80%E7%94%A8%E6%B3%95/</link>
      <pubDate>Mon, 23 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/023%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--scanpy%E5%9F%BA%E7%A1%80%E7%94%A8%E6%B3%95/</guid>
      <description>scanpy是单细胞数据分析的python工具包，将数据以Anndata对象的格式进行储存。如下将学习Anndata对象操作以及scanpy</description>
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    <item>
      <title>单细胞分析工具--SingleR细胞类型注释</title>
      <link>https://lishensuo.github.io/posts/bioinfo/019%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--singler%E7%BB%86%E8%83%9E%E7%B1%BB%E5%9E%8B%E6%B3%A8%E9%87%8A/</link>
      <pubDate>Fri, 20 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/019%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--singler%E7%BB%86%E8%83%9E%E7%B1%BB%E5%9E%8B%E6%B3%A8%E9%87%8A/</guid>
      <description>SingleR包是在单细胞数据分析时用于细胞类型自动注释的常用工具。其基本原理是使用已有细胞标签的参考转录组数据集的表达谱，基于相似性原则注</description>
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    <item>
      <title>单细胞分析工具--RNAvelocity速率分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/024%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--rnavelocity%E9%80%9F%E7%8E%87%E5%88%86%E6%9E%90/</link>
      <pubDate>Thu, 26 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/024%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--rnavelocity%E9%80%9F%E7%8E%87%E5%88%86%E6%9E%90/</guid>
      <description>RNA velocity(RNA速率)分析是基于单细胞转录组测序数据分析细胞发育状态动力学的方法。简单来说，该模型根据测序read片段属于unsp</description>
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    <item>
      <title>Python数据科学-(1)IPython的特点</title>
      <link>https://lishensuo.github.io/posts/program/020python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6-ipython%E9%AD%94%E6%B3%95%E5%91%BD%E4%BB%A4/</link>
      <pubDate>Tue, 26 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/020python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6-ipython%E9%AD%94%E6%B3%95%E5%91%BD%E4%BB%A4/</guid>
      <description>Python有不少解释器，默认使用的是CPython。而IPython，interactive python可以提供交互式开发环境；是Jupy</description>
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    <item>
      <title>单细胞分析工具--CytoTRACE轨迹分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/025%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--cytotrace%E8%BD%A8%E8%BF%B9%E5%88%86%E6%9E%90/</link>
      <pubDate>Thu, 26 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/025%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--cytotrace%E8%BD%A8%E8%BF%B9%E5%88%86%E6%9E%90/</guid>
      <description>CytoTRACE是根据单细胞表达矩阵推断细胞分化轨迹的工具，目前提供网页端与R包两种形式。如下。简单学习其R包用法。 官方手册：https:</description>
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    <item>
      <title>Python数据科学-(2)Numpy数组(torch张量)</title>
      <link>https://lishensuo.github.io/posts/program/021python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6-numpy%E6%95%B0%E7%BB%84torch%E5%BC%A0%E9%87%8F/</link>
      <pubDate>Fri, 29 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/021python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6-numpy%E6%95%B0%E7%BB%84torch%E5%BC%A0%E9%87%8F/</guid>
      <description>NumPy,Numerical Python创建的ndarray数组在某些方面与Python中的list对象很相似；但是二者还是很多区别，比如数据类型一致性的要求、维度等</description>
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      <link>https://lishensuo.github.io/posts/bioinfo/026%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--cellphonedb%E7%BB%86%E8%83%9E%E9%80%9A%E8%AE%AF%E5%88%86%E6%9E%90/</link>
      <pubDate>Sat, 28 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/026%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--cellphonedb%E7%BB%86%E8%83%9E%E9%80%9A%E8%AE%AF%E5%88%86%E6%9E%90/</guid>
      <description>cellphonedb是基于配受体对表达水平，分析单细胞数据中不同细胞类型间相互作用的Python工具。其于2020年在nature prot</description>
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    <item>
      <title>Python数据科学-(3)Pandas表格</title>
      <link>https://lishensuo.github.io/posts/program/022python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6-pandas%E8%A1%A8%E6%A0%BC/</link>
      <pubDate>Tue, 03 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/022python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6-pandas%E8%A1%A8%E6%A0%BC/</guid>
      <description>Pandas相比Numpy数组支持行列标签、多种数据类型，类似R语言中data.frame数据框。 1、Pandas结构 1.1 Series：有索引</description>
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    <item>
      <title>单细胞分析工具--DoubletFinder识别双细胞</title>
      <link>https://lishensuo.github.io/posts/bioinfo/027%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--doubletfinder%E8%AF%86%E5%88%AB%E5%8F%8C%E7%BB%86%E8%83%9E/</link>
      <pubDate>Sat, 28 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/027%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--doubletfinder%E8%AF%86%E5%88%AB%E5%8F%8C%E7%BB%86%E8%83%9E/</guid>
      <description>R包DoubletFinder可用于检测基于Droplet单细胞测序技术(10X)的双细胞。如下简单学习识别原理以用法。 原始论文：https</description>
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    <item>
      <title>Python数据科学-(4)Matplotlib可视化</title>
      <link>https://lishensuo.github.io/posts/program/023python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6-matplotlib%E5%8F%AF%E8%A7%86%E5%8C%96/</link>
      <pubDate>Thu, 19 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/023python%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6-matplotlib%E5%8F%AF%E8%A7%86%E5%8C%96/</guid>
      <description>1、启动方式 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 import matplotlib.pyplot as plt %matplotlib inline import numpy as np ##(1)在普通python解释器/python脚本中 x = np.linspace(0,10,100) plt.plot(x, np.sin(x), &amp;#39;-&amp;#39;) plt.show() ##</description>
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      <link>https://lishensuo.github.io/posts/bioinfo/030%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--mast%E5%B7%AE%E5%BC%82%E5%9F%BA%E5%9B%A0%E5%88%86%E6%9E%90/</link>
      <pubDate>Fri, 10 Feb 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/030%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--mast%E5%B7%AE%E5%BC%82%E5%9F%BA%E5%9B%A0%E5%88%86%E6%9E%90/</guid>
      <description>MAST，全称Model-based Analysis of Single-cell Transcriptomics，是2015年于Genome Biology发表的R包工具，主要用于单</description>
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      <title>单细胞分析工具--Scissor表型相关亚群鉴定</title>
      <link>https://lishensuo.github.io/posts/bioinfo/031%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--scissor%E8%A1%A8%E5%9E%8B%E7%9B%B8%E5%85%B3%E4%BA%9A%E7%BE%A4%E9%89%B4%E5%AE%9A/</link>
      <pubDate>Sun, 19 Feb 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/031%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--scissor%E8%A1%A8%E5%9E%8B%E7%9B%B8%E5%85%B3%E4%BA%9A%E7%BE%A4%E9%89%B4%E5%AE%9A/</guid>
      <description>Scissor是2022年发表在Nature Biotechnology的一个单细胞数据分析工具包。该包通过纳入Bulk表达与表型数据，试图分</description>
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    <item>
      <title>单细胞分析工具--MARVEL单细胞可变剪切</title>
      <link>https://lishensuo.github.io/posts/bioinfo/032%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--marvel%E5%8D%95%E7%BB%86%E8%83%9E%E5%8F%AF%E5%8F%98%E5%89%AA%E5%88%87/</link>
      <pubDate>Mon, 06 Mar 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/032%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--marvel%E5%8D%95%E7%BB%86%E8%83%9E%E5%8F%AF%E5%8F%98%E5%89%AA%E5%88%87/</guid>
      <description>R包MARVEL是由牛津大学MRC Weatherall分子医学研究所团队开发的，用于分析单细胞水平的可变剪切事件。相关文章于2023年1月在</description>
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      <title>单细胞分析工具--scCustomize包改进Seurat可视化</title>
      <link>https://lishensuo.github.io/posts/bioinfo/033%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--sccustomize%E5%8C%85%E6%94%B9%E8%BF%9Bseurat%E5%8F%AF%E8%A7%86%E5%8C%96/</link>
      <pubDate>Sat, 11 Mar 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/033%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--sccustomize%E5%8C%85%E6%94%B9%E8%BF%9Bseurat%E5%8F%AF%E8%A7%86%E5%8C%96/</guid>
      <description>scCustomize包由来自波士顿儿童医院/哈佛医学院的博士后Samuel E. Marsh编写。该包基于Seurat，提供了若干便捷、高效的可</description>
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      <link>https://lishensuo.github.io/posts/bioinfo/034%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--asgard%E5%8D%95%E7%BB%86%E8%83%9E%E8%8D%AF%E7%89%A9%E9%87%8D%E5%AE%9A%E5%90%91/</link>
      <pubDate>Sat, 18 Mar 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/034%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--asgard%E5%8D%95%E7%BB%86%E8%83%9E%E8%8D%AF%E7%89%A9%E9%87%8D%E5%AE%9A%E5%90%91/</guid>
      <description>来自密歇根大学医学院的计算医学与生物信息学部的LanaX.Garmire(拉娜·加米尔)团队近日于Nature Communication发表</description>
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    <item>
      <title>单细胞分析工具--基于文献的细胞类型注释marker</title>
      <link>https://lishensuo.github.io/posts/bioinfo/028%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--%E5%9F%BA%E4%BA%8E%E6%96%87%E7%8C%AE%E7%9A%84%E7%BB%86%E8%83%9E%E7%B1%BB%E5%9E%8B%E6%B3%A8%E9%87%8Amarker/</link>
      <pubDate>Tue, 07 Feb 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/028%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--%E5%9F%BA%E4%BA%8E%E6%96%87%E7%8C%AE%E7%9A%84%E7%BB%86%E8%83%9E%E7%B1%BB%E5%9E%8B%E6%B3%A8%E9%87%8Amarker/</guid>
      <description>根据平时的文献阅读，汇总前人注释单细胞数据的细胞类型所使用的marker gene，以供后续研究参考。（持续更新） 1、Tumor B cell：C</description>
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    <item>
      <title>单细胞分析工具--scSTAR挖掘分组样本细胞亚型</title>
      <link>https://lishensuo.github.io/posts/bioinfo/035%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--scstar%E6%8C%96%E6%8E%98%E5%88%86%E7%BB%84%E6%A0%B7%E6%9C%AC%E7%BB%86%E8%83%9E%E4%BA%9A%E5%9E%8B/</link>
      <pubDate>Mon, 17 Apr 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/035%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--scstar%E6%8C%96%E6%8E%98%E5%88%86%E7%BB%84%E6%A0%B7%E6%9C%AC%E7%BB%86%E8%83%9E%E4%BA%9A%E5%9E%8B/</guid>
      <description>scSTAR是由复旦大学附属中山医院郝洁研究员等人开发的单细胞分析工具包，于2023年2月发表在Briefings in Bioinformati</description>
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    <item>
      <title>单细胞分析工具--NicheNet细胞通讯分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/029%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--nichenet%E7%BB%86%E8%83%9E%E9%80%9A%E8%AE%AF%E5%88%86%E6%9E%90/</link>
      <pubDate>Thu, 09 Feb 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/029%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--nichenet%E7%BB%86%E8%83%9E%E9%80%9A%E8%AE%AF%E5%88%86%E6%9E%90/</guid>
      <description>原始论文：https://www.nature.com/articles/s41592-019-0667-5 官方手册：https://git</description>
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    <item>
      <title>单细胞分析工具--celldancer细胞速率分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/036%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--celldancer%E7%BB%86%E8%83%9E%E9%80%9F%E7%8E%87%E5%88%86%E6%9E%90/</link>
      <pubDate>Sat, 22 Apr 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/036%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--celldancer%E7%BB%86%E8%83%9E%E9%80%9F%E7%8E%87%E5%88%86%E6%9E%90/</guid>
      <description>cellDancer是美国休斯顿卫理公会研究所的助理教授Guangyu Wang团队开发的RNA速率分析新工具，于2023年4月发表于natu</description>
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    <item>
      <title>Python-可视化-pandas绘图</title>
      <link>https://lishensuo.github.io/posts/program/030python-%E5%8F%AF%E8%A7%86%E5%8C%96-pandas%E7%BB%98%E5%9B%BE/</link>
      <pubDate>Sat, 30 Jul 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/030python-%E5%8F%AF%E8%A7%86%E5%8C%96-pandas%E7%BB%98%E5%9B%BE/</guid>
      <description>https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html pandas的dataframe自带一些绘图语法用以简单的表格数据可视化，适合于数据的初步探索、分析 1 import pandas as pd 1、barplot柱状图 1 2</description>
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    <item>
      <title>单细胞分析工具--sccode综合差异分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/037%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--sccode%E7%BB%BC%E5%90%88%E5%B7%AE%E5%BC%82%E5%88%86%E6%9E%90/</link>
      <pubDate>Sun, 30 Apr 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/037%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--sccode%E7%BB%BC%E5%90%88%E5%B7%AE%E5%BC%82%E5%88%86%E6%9E%90/</guid>
      <description>scCODE( single-cell consensus optimization of differentially expressed gene detection)是由复旦大学附属金山医院邹欣等人开发的R包工具，于2022年12月发表于Briefing in Bioinfo</description>
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    <item>
      <title>单细胞分析工具--ShinyCell交互式展示单细胞数据</title>
      <link>https://lishensuo.github.io/posts/bioinfo/040%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--shinycell%E4%BA%A4%E4%BA%92%E5%BC%8F%E5%B1%95%E7%A4%BA%E5%8D%95%E7%BB%86%E8%83%9E%E6%95%B0%E6%8D%AE/</link>
      <pubDate>Mon, 07 Aug 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/040%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--shinycell%E4%BA%A4%E4%BA%92%E5%BC%8F%E5%B1%95%E7%A4%BA%E5%8D%95%E7%BB%86%E8%83%9E%E6%95%B0%E6%8D%AE/</guid>
      <description>ShinyCell包是由杜克-新加坡国立大学医学院的John F. Ouyang团队开发的单细胞分析工具包，实现基于shiny网页交互式展示单细胞</description>
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    <item>
      <title>单细胞分析工具--Seurat5基础用法</title>
      <link>https://lishensuo.github.io/posts/bioinfo/041%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--seurat5%E5%9F%BA%E7%A1%80%E7%94%A8%E6%B3%95/</link>
      <pubDate>Tue, 17 Sep 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/041%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--seurat5%E5%9F%BA%E7%A1%80%E7%94%A8%E6%B3%95/</guid>
      <description>Seurat V5版本有一段时间了，由于时间原因未来得及了解。现根据其官方文档简单整理其用法，与V4比较类似的地方就不多叙述了。此外，V5的亮点之一还在</description>
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    <item>
      <title>单细胞分析工具--CELLxGENE数据库</title>
      <link>https://lishensuo.github.io/posts/bioinfo/042%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--cellxgene%E6%95%B0%E6%8D%AE%E5%BA%93/</link>
      <pubDate>Wed, 16 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/042%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--cellxgene%E6%95%B0%E6%8D%AE%E5%BA%93/</guid>
      <description>网站：https://cellxgene.cziscience.com/ API：https://chanzuckerberg.github.</description>
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    <item>
      <title>单细胞分析工具--scVI去除批次效应</title>
      <link>https://lishensuo.github.io/posts/bioinfo/043%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--scvi%E5%8E%BB%E9%99%A4%E6%89%B9%E6%AC%A1%E6%95%88%E5%BA%94/</link>
      <pubDate>Tue, 05 Nov 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/043%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--scvi%E5%8E%BB%E9%99%A4%E6%89%B9%E6%AC%A1%E6%95%88%E5%BA%94/</guid>
      <description>scVI (single-cell variational inference)是2018年发表的一项单细胞分析工具。它主要基于VAE变分自编码器的思想，计算去除批次效应后的细胞低维空间表示。而</description>
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    <item>
      <title>单细胞分析工具--sccoda细胞组成比较</title>
      <link>https://lishensuo.github.io/posts/bioinfo/038%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--sccoda%E7%BB%86%E8%83%9E%E7%BB%84%E6%88%90%E6%AF%94%E8%BE%83/</link>
      <pubDate>Sun, 30 Apr 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/038%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--sccoda%E7%BB%86%E8%83%9E%E7%BB%84%E6%88%90%E6%AF%94%E8%BE%83/</guid>
      <description>scCODA(single-cell compositional data analysis)是由德国环境健康研究中心计算生物学研究所M Büttner等人基于python开发的单细胞数据分析工具，于2021年1</description>
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      <title>单细胞分析工具--COSG鉴定marker基因</title>
      <link>https://lishensuo.github.io/posts/bioinfo/039%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--cosg%E9%89%B4%E5%AE%9Amarker%E5%9F%BA%E5%9B%A0/</link>
      <pubDate>Sat, 01 Jul 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/039%E5%8D%95%E7%BB%86%E8%83%9E%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7--cosg%E9%89%B4%E5%AE%9Amarker%E5%9F%BA%E5%9B%A0/</guid>
      <description>COSG(COSine similarity-based marker Gene identification)是由来自哈佛医学院和Broad研究所博后Ming Dai等人开发，旨在从余弦相似度的角度鉴定cluste</description>
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      <title>差异分析R包-DESeq2&#43;edgeR&#43;limma</title>
      <link>https://lishensuo.github.io/posts/bioinfo/045%E5%B7%AE%E5%BC%82%E5%88%86%E6%9E%90r%E5%8C%85-deseq2&#43;edger&#43;limma/</link>
      <pubDate>Sat, 11 Jun 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/045%E5%B7%AE%E5%BC%82%E5%88%86%E6%9E%90r%E5%8C%85-deseq2&#43;edger&#43;limma/</guid>
      <description>在对转录组数据分析时，分组比较的差异分析前提时获得测序表达矩阵。 根据测序技术分为两种，对应的分析R包如下所示。 RNAseq的原始count表</description>
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      <title>TCGAbiolinks包-TCGA数据下载与差异分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/046tcgabiolinks%E5%8C%85-tcga%E6%95%B0%E6%8D%AE%E4%B8%8B%E8%BD%BD%E4%B8%8E%E5%B7%AE%E5%BC%82%E5%88%86%E6%9E%90/</link>
      <pubDate>Sat, 30 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/046tcgabiolinks%E5%8C%85-tcga%E6%95%B0%E6%8D%AE%E4%B8%8B%E8%BD%BD%E4%B8%8E%E5%B7%AE%E5%BC%82%E5%88%86%E6%9E%90/</guid>
      <description>TCGAbiolinks包是一站式分析TCGA数据的R包工具，它集成了TCGA数据下载、分析、可视化的全部流程。此次系列笔记主要跟着 TCGA</description>
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      <title>表达矩阵TPM,FPKM标准化</title>
      <link>https://lishensuo.github.io/posts/bioinfo/047%E8%A1%A8%E8%BE%BE%E7%9F%A9%E9%98%B5tpmfpkm%E6%A0%87%E5%87%86%E5%8C%96/</link>
      <pubDate>Sat, 30 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/047%E8%A1%A8%E8%BE%BE%E7%9F%A9%E9%98%B5tpmfpkm%E6%A0%87%E5%87%86%E5%8C%96/</guid>
      <description>1、计算公式 sample 1 sample 2 &amp;hellip;&amp;hellip;.. sample k Gene 1 10 12 30 Gene 2 20 25 60 &amp;hellip;&amp;hellip; &amp;hellip; &amp;hellip; &amp;hellip; &amp;hellip; Gene n 0 0 &amp;hellip; 1 对于(n*k)表达矩阵中k个样本的n个基因的count表达数据。 在任意样</description>
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      <title>从GEO下载芯片或RNAseq测序数据</title>
      <link>https://lishensuo.github.io/posts/bioinfo/048%E4%BB%8Egeo%E4%B8%8B%E8%BD%BD%E8%8A%AF%E7%89%87%E6%88%96rnaseq%E6%B5%8B%E5%BA%8F%E6%95%B0%E6%8D%AE/</link>
      <pubDate>Sat, 30 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/048%E4%BB%8Egeo%E4%B8%8B%E8%BD%BD%E8%8A%AF%E7%89%87%E6%88%96rnaseq%E6%B5%8B%E5%BA%8F%E6%95%B0%E6%8D%AE/</guid>
      <description>挖掘GEO数据时，主要一方面是下载GEO的测序数据（包括基因芯片array与RNAseq两类）的表达矩阵。同时会涉及到一些细节问题，例如ar</description>
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      <title>下载测序数据SRR与fastq.gz方式</title>
      <link>https://lishensuo.github.io/posts/bioinfo/049%E4%B8%8B%E8%BD%BD%E6%B5%8B%E5%BA%8F%E6%95%B0%E6%8D%AEsrr%E4%B8%8Efastq.gz%E6%96%B9%E5%BC%8F/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/049%E4%B8%8B%E8%BD%BD%E6%B5%8B%E5%BA%8F%E6%95%B0%E6%8D%AEsrr%E4%B8%8Efastq.gz%E6%96%B9%E5%BC%8F/</guid>
      <description>1、准备conda环境与软件 1 2 3 4 5 6 7 8 # 准备download环境 conda create -n download conda activate download # 安装软件 conda install -c hcc aspera-cli conda install -c bioconda sra-tools conda install -c conda-forge pigz 以SRR1391</description>
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    <item>
      <title>050快捷键</title>
      <link>https://lishensuo.github.io/posts/program/050%E5%BF%AB%E6%8D%B7%E9%94%AE%E7%B3%BB%E5%88%97/</link>
      <pubDate>Tue, 26 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/050%E5%BF%AB%E6%8D%B7%E9%94%AE%E7%B3%BB%E5%88%97/</guid>
      <description>shell Ctrl + a : 将光标移到本行的开始处 Ctrl + e : 将光标移到本行的末尾处 Backsapce : 删除前一个字符 Ctrl + d : 删除后一个字符 Ctrl + k : 从光标开始剪切至行的末尾 Ctrl + y</description>
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      <title>Refgenie下载参考基因组</title>
      <link>https://lishensuo.github.io/posts/bioinfo/050refgenie%E4%B8%8B%E8%BD%BD%E5%8F%82%E8%80%83%E5%9F%BA%E5%9B%A0%E7%BB%84/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/050refgenie%E4%B8%8B%E8%BD%BD%E5%8F%82%E8%80%83%E5%9F%BA%E5%9B%A0%E7%BB%84/</guid>
      <description>refgenie：参考基因组（阿拉丁）商店 http://refgenie.databio.org/ Here we provide a web interface and a RESTful API to access genome assets for popular reference genome assemblies. 该平台由位于弗吉尼亚大学公共卫生基因组学中心的计算生物学和</description>
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      <title>STRINGdb包下载蛋白PPI数据</title>
      <link>https://lishensuo.github.io/posts/bioinfo/051stringdb%E5%8C%85%E4%B8%8B%E8%BD%BD%E8%9B%8B%E7%99%BDppi%E6%95%B0%E6%8D%AE/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/051stringdb%E5%8C%85%E4%B8%8B%E8%BD%BD%E8%9B%8B%E7%99%BDppi%E6%95%B0%E6%8D%AE/</guid>
      <description>1 2 3 4 5 if (!require(&amp;#34;BiocManager&amp;#34;, quietly = TRUE)) install.packages(&amp;#34;BiocManager&amp;#34;) BiocManager::install(&amp;#34;STRINGdb&amp;#34;) library(STRINGdb) 1、定义要使用的STRING版本、物种，以及PPI阈值分数 1 2 3 4 string_db &amp;lt;- STRINGdb$new(version=&amp;#34;11&amp;#34;, species=9606, score_threshold=200, input_directory=&amp;#34;&amp;#34;) 2、示例基因 1 2 3 4 5 6 7 8 9 10 11 12</description>
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      <title>使用clusterProfiler下载GO&amp;KEGG通路基因</title>
      <link>https://lishensuo.github.io/posts/bioinfo/052%E4%BD%BF%E7%94%A8clusterprofiler%E4%B8%8B%E8%BD%BDgokegg%E9%80%9A%E8%B7%AF%E5%9F%BA%E5%9B%A0/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/052%E4%BD%BF%E7%94%A8clusterprofiler%E4%B8%8B%E8%BD%BDgokegg%E9%80%9A%E8%B7%AF%E5%9F%BA%E5%9B%A0/</guid>
      <description>1、GO 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66</description>
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      <title>人类基因组基础知识与下载查询</title>
      <link>https://lishensuo.github.io/posts/bioinfo/053%E4%BA%BA%E7%B1%BB%E5%9F%BA%E5%9B%A0%E7%BB%84%E5%9F%BA%E7%A1%80%E7%9F%A5%E8%AF%86%E4%B8%8E%E4%B8%8B%E8%BD%BD%E6%9F%A5%E8%AF%A2/</link>
      <pubDate>Sat, 28 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/053%E4%BA%BA%E7%B1%BB%E5%9F%BA%E5%9B%A0%E7%BB%84%E5%9F%BA%E7%A1%80%E7%9F%A5%E8%AF%86%E4%B8%8E%E4%B8%8B%E8%BD%BD%E6%9F%A5%E8%AF%A2/</guid>
      <description>一、基因组大小 （1）人类基因组主要由细胞核的23对染色体组成(核基因组)，还包括线粒体中的小DNA分子(线粒体基因组)。 （2）单倍体基因组大</description>
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      <title>基因-蛋白-化合物ID转换</title>
      <link>https://lishensuo.github.io/posts/bioinfo/054%E5%9F%BA%E5%9B%A0-%E8%9B%8B%E7%99%BD-%E5%8C%96%E5%90%88%E7%89%A9id%E8%BD%AC%E6%8D%A2/</link>
      <pubDate>Sat, 28 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/054%E5%9F%BA%E5%9B%A0-%E8%9B%8B%E7%99%BD-%E5%8C%96%E5%90%88%E7%89%A9id%E8%BD%AC%E6%8D%A2/</guid>
      <description>1、不同基因ID转换 1.1 org.Hs.eg.db包 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 library(dplyr) library(org.Hs.eg.db) keytypes(org.Hs.eg.db) # [1] &amp;#34;ACCNUM&amp;#34; &amp;#34;ALIAS&amp;#34; &amp;#34;ENSEMBL&amp;#34; &amp;#34;ENSEMBLPROT&amp;#34; &amp;#34;ENSEMBLTRANS&amp;#34; &amp;#34;ENTREZID&amp;#34; # [7] &amp;#34;ENZYME&amp;#34; &amp;#34;EVIDENCE&amp;#34; &amp;#34;EVIDENCEALL&amp;#34;</description>
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    <item>
      <title>GSEA富集分析工具</title>
      <link>https://lishensuo.github.io/posts/bioinfo/055gsea%E5%AF%8C%E9%9B%86%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7/</link>
      <pubDate>Fri, 13 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/055gsea%E5%AF%8C%E9%9B%86%E5%88%86%E6%9E%90%E5%B7%A5%E5%85%B7/</guid>
      <description>以前通路富集分析直接使用clusterprofiler包，阅读文献发现GSEA分析及可视化较多使用Broad团队研发的工具，现简要学习其(w</description>
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    <item>
      <title>clusterProfiler包富集分析与可视化</title>
      <link>https://lishensuo.github.io/posts/bioinfo/056clusterprofiler%E5%8C%85%E5%AF%8C%E9%9B%86%E5%88%86%E6%9E%90%E4%B8%8E%E5%8F%AF%E8%A7%86%E5%8C%96/</link>
      <pubDate>Sat, 14 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/056clusterprofiler%E5%8C%85%E5%AF%8C%E9%9B%86%E5%88%86%E6%9E%90%E4%B8%8E%E5%8F%AF%E8%A7%86%E5%8C%96/</guid>
      <description>1、背景知识 （1）两种富集分析 基于超几何检验的ORA(over representation analysis)富集分析 ① 假设对转录组分组测序的10000个基因表达数据进</description>
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    <item>
      <title>富集通路可视化方式</title>
      <link>https://lishensuo.github.io/posts/bioinfo/057%E5%AF%8C%E9%9B%86%E9%80%9A%E8%B7%AF%E5%8F%AF%E8%A7%86%E5%8C%96%E6%96%B9%E5%BC%8F/</link>
      <pubDate>Sat, 14 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/057%E5%AF%8C%E9%9B%86%E9%80%9A%E8%B7%AF%E5%8F%AF%E8%A7%86%E5%8C%96%E6%96%B9%E5%BC%8F/</guid>
      <description>0、示例数据 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 library(clusterProfiler) library(org.Hs.eg.db) data(geneList, package=&amp;#34;DOSE&amp;#34;) str(geneList) # Named num [1:12495] 4.57 4.51 4.42 4.14 3.88 ... # - attr(*, &amp;#34;names&amp;#34;)= chr [1:12495] &amp;#34;4312&amp;#34; &amp;#34;8318&amp;#34; &amp;#34;10874&amp;#34; &amp;#34;55143&amp;#34; ... gene_ids&amp;lt;-AnnotationDbi::select(org.Hs.eg.db, keys=as.character(names(gene_list)), columns=&amp;#34;SYMBOL&amp;#34;, #目标格式 keytype=&amp;#34;ENTREZID&amp;#34;)</description>
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    <item>
      <title>WGCNA基因加权共表达网络分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/071wgcna%E5%9F%BA%E5%9B%A0%E5%8A%A0%E6%9D%83%E5%85%B1%E8%A1%A8%E8%BE%BE%E7%BD%91%E7%BB%9C%E5%88%86%E6%9E%90/</link>
      <pubDate>Mon, 25 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/071wgcna%E5%9F%BA%E5%9B%A0%E5%8A%A0%E6%9D%83%E5%85%B1%E8%A1%A8%E8%BE%BE%E7%BD%91%E7%BB%9C%E5%88%86%E6%9E%90/</guid>
      <description>1、关于WGCNA原理 1.1 建立共表达网络 在基因共表达网络中，节点node代表基因，边edge代表两个基因间共表达关系。 若一个基因同时与多个基因</description>
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    <item>
      <title>GSVA包单样本富集分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/058gsva%E5%8C%85%E5%8D%95%E6%A0%B7%E6%9C%AC%E5%AF%8C%E9%9B%86%E5%88%86%E6%9E%90/</link>
      <pubDate>Sun, 15 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/058gsva%E5%8C%85%E5%8D%95%E6%A0%B7%E6%9C%AC%E5%AF%8C%E9%9B%86%E5%88%86%E6%9E%90/</guid>
      <description>1、GSVA函数 1 2 3 4 5 6 7 # BiocManager::install(&amp;#34;GSVA&amp;#34;) library(GSVA) ?gsva gsva(expr = , #metrix格式表达矩阵(行--基因，列--样本) gset.idx.list = , #list格式基因集 method=c(&amp;#34;gsva&amp;#34;, &amp;#34;ssgsea&amp;#34;, &amp;#34;zscore&amp;#34;, &amp;#34;plage&amp;#34;), # defaul:gsva kcdf=c(&amp;#34;Gaussian&amp;#34;, &amp;#34;Poisson&amp;#34;, &amp;#34;none&amp;#34;))</description>
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    <item>
      <title>xCell与CIBERSORT等免疫浸润分析</title>
      <link>https://lishensuo.github.io/posts/bioinfo/072xcell%E4%B8%8Ecibersort%E7%AD%89%E5%85%8D%E7%96%AB%E6%B5%B8%E6%B6%A6%E5%88%86%E6%9E%90/</link>
      <pubDate>Mon, 25 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/072xcell%E4%B8%8Ecibersort%E7%AD%89%E5%85%8D%E7%96%AB%E6%B5%B8%E6%B6%A6%E5%88%86%E6%9E%90/</guid>
      <description>xCell xCell包 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 devtools::install_github(&amp;#39;dviraran/xCell&amp;#39;) library(xCell) data(&amp;#34;xCell.data&amp;#34;) ##查看支持的64种细胞类型，同下图 colnames(xCell.data$spill$K) ##预测函数的关键参数解释 ?xCellAnalysis() # expr = 交代表达矩阵； ##</description>
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    <item>
      <title>ClusterGVis包绘制基因表达矩阵热图</title>
      <link>https://lishensuo.github.io/posts/bioinfo/073clustergvis%E5%8C%85%E7%BB%98%E5%88%B6%E5%9F%BA%E5%9B%A0%E8%A1%A8%E8%BE%BE%E7%9F%A9%E9%98%B5%E7%83%AD%E5%9B%BE/</link>
      <pubDate>Sun, 19 Mar 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/073clustergvis%E5%8C%85%E7%BB%98%E5%88%B6%E5%9F%BA%E5%9B%A0%E8%A1%A8%E8%BE%BE%E7%9F%A9%E9%98%B5%E7%83%AD%E5%9B%BE/</guid>
      <description>ClusterGVis包是中国药科大学Jun Zhang博士开发的系列可视化工具包之一，可以基因表达矩阵进行高级的热图可视化分析。如下根据其g</description>
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    <item>
      <title>survival包生存分析及glmnet包lasso回归</title>
      <link>https://lishensuo.github.io/posts/bioinfo/059survival%E5%8C%85%E7%94%9F%E5%AD%98%E5%88%86%E6%9E%90%E5%8F%8Aglmnet%E5%8C%85lasso%E5%9B%9E%E5%BD%92/</link>
      <pubDate>Sat, 28 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/059survival%E5%8C%85%E7%94%9F%E5%AD%98%E5%88%86%E6%9E%90%E5%8F%8Aglmnet%E5%8C%85lasso%E5%9B%9E%E5%BD%92/</guid>
      <description>生存分析(survival analysis)的主要目的是发现与患者生存事件相关的指标因素，例如年龄性别、基因表达/突变等。如下学习相关基础知</description>
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    <item>
      <title>MuSiC包根据scRNAseq预测Bulk细胞组成</title>
      <link>https://lishensuo.github.io/posts/bioinfo/074music%E5%8C%85%E6%A0%B9%E6%8D%AEscrnaseq%E9%A2%84%E6%B5%8Bbulk%E7%BB%86%E8%83%9E%E7%BB%84%E6%88%90/</link>
      <pubDate>Sun, 26 Mar 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/074music%E5%8C%85%E6%A0%B9%E6%8D%AEscrnaseq%E9%A2%84%E6%B5%8Bbulk%E7%BB%86%E8%83%9E%E7%BB%84%E6%88%90/</guid>
      <description>MuSiC(MUlti-Subject SIngle Cell deconvolution)是来自宾夕法尼亚大学Biostatistics, Epidemiology and Informatics系的Mingyao Li课题组于2</description>
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      <link>https://lishensuo.github.io/posts/bioinfo/060%E4%BB%8Ernaseq%E7%9A%84fastq.gz%E6%8F%90%E5%8F%96%E8%A1%A8%E8%BE%BE%E7%9F%A9%E9%98%B5/</link>
      <pubDate>Sun, 29 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/060%E4%BB%8Ernaseq%E7%9A%84fastq.gz%E6%8F%90%E5%8F%96%E8%A1%A8%E8%BE%BE%E7%9F%A9%E9%98%B5/</guid>
      <description>RNA-seq数据比对流程主要分为三步（1）整理数据；（2）质控；（3）比对。其中每一步都涉及到若干软件的用法，如下简单整理基本的分析流程。</description>
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      <link>https://lishensuo.github.io/posts/bioinfo/075%E7%94%9F%E4%BF%A1%E7%9B%B8%E5%85%B3%E7%BD%91%E7%AB%99%E6%95%B0%E6%8D%AE%E5%BA%93%E9%9B%86%E9%94%A6/</link>
      <pubDate>Sun, 02 Apr 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/075%E7%94%9F%E4%BF%A1%E7%9B%B8%E5%85%B3%E7%BD%91%E7%AB%99%E6%95%B0%E6%8D%AE%E5%BA%93%E9%9B%86%E9%94%A6/</guid>
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      <link>https://lishensuo.github.io/posts/bioinfo/076tcga%E7%9A%84snv%E6%95%B0%E6%8D%AE%E4%B8%8B%E8%BD%BD%E4%B8%8Emaftools%E5%8F%AF%E8%A7%86%E5%8C%96/</link>
      <pubDate>Sun, 09 Apr 2023 00:00:00 +0000</pubDate>
      
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      <description>1、TCGAbiolinks下载数据 使用TCGAbiolinks下载特定肿瘤类型的SNV数据 https://bioconductor.org/packages/release/bioc/vignettes/TCGAbiolinks/inst/doc/mutation.html 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23</description>
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      <link>https://lishensuo.github.io/posts/bioinfo/070%E4%BD%BF%E7%94%A8isoformswitchanalyzer%E5%8C%85%E8%BF%9B%E8%A1%8C%E5%8F%AF%E5%8F%98%E5%89%AA%E5%88%87%E5%88%86%E6%9E%90/</link>
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      <link>https://lishensuo.github.io/posts/bioinfo/081iobr%E5%8C%85%E8%82%BF%E7%98%A4signature%E6%89%93%E5%88%86%E4%B8%8E%E5%85%8D%E7%96%AB%E6%B5%B8%E6%B6%A6%E5%88%86%E6%9E%90/</link>
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      <link>https://lishensuo.github.io/posts/bioinfo/100%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr31--%E5%88%86%E7%B1%BB--knn/</link>
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      <pubDate>Sun, 30 Oct 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/102%E6%96%87%E7%8C%AE--%E6%8C%96%E6%8E%98ad%E5%8D%95%E7%BB%86%E8%83%9E%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90nk%E6%B5%B8%E6%B6%A6/</guid>
      <description>Expression of Immune Related Genes and Possible Regulatory Mechanisms in Alzheimer’s Disease doi: 10.3389/fimmu.2021.768966 05 November 2021 核心结论：AD病人外周血中的NK细胞浸润到大脑中参与AD病理过程的免疫反应。 1、公共数</description>
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      <title>机器学习基于R包mlr3(3)--分类--LDA与QDA</title>
      <link>https://lishensuo.github.io/posts/bioinfo/102%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr33--%E5%88%86%E7%B1%BB--lda%E4%B8%8Eqda/</link>
      <pubDate>Sun, 10 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/102%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr33--%E5%88%86%E7%B1%BB--lda%E4%B8%8Eqda/</guid>
      <description>1、概述 LDA与QDA可以简单理解为有监督的降维，将多个预测变量信息压缩成少数（类别数-1）新的预测变量。 每一个新的预测变量称之为判别函数，</description>
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      <title>文献--基于肝细胞癌M2样肿瘤巨噬细胞建立预后模型</title>
      <link>https://lishensuo.github.io/posts/basic/103%E6%96%87%E7%8C%AE--%E5%9F%BA%E4%BA%8E%E8%82%9D%E7%BB%86%E8%83%9E%E7%99%8Cm2%E6%A0%B7%E8%82%BF%E7%98%A4%E5%B7%A8%E5%99%AC%E7%BB%86%E8%83%9E%E5%BB%BA%E7%AB%8B%E9%A2%84%E5%90%8E%E6%A8%A1%E5%9E%8B/</link>
      <pubDate>Sun, 13 Nov 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/103%E6%96%87%E7%8C%AE--%E5%9F%BA%E4%BA%8E%E8%82%9D%E7%BB%86%E8%83%9E%E7%99%8Cm2%E6%A0%B7%E8%82%BF%E7%98%A4%E5%B7%A8%E5%99%AC%E7%BB%86%E8%83%9E%E5%BB%BA%E7%AB%8B%E9%A2%84%E5%90%8E%E6%A8%A1%E5%9E%8B/</guid>
      <description>文章题目：M2-like tumor-associated macrophage-related biomarkers to construct a novel prognostic signature, reveal the immune landscape, and screen drugs in hepatocellular carcinoma 发表期刊及日期：Front Immunol， 2022 Sep 13 DOI：10.3389/fim</description>
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      <title>机器学习基于R包mlr3(4)--分类--朴素贝叶斯</title>
      <link>https://lishensuo.github.io/posts/bioinfo/103%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr34--%E5%88%86%E7%B1%BB--%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF/</link>
      <pubDate>Sun, 10 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/103%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr34--%E5%88%86%E7%B1%BB--%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF/</guid>
      <description>1、朴素贝叶斯简介 Naive Bayes：预测样本属于每一类别的概率，取概率最高的类别。包含四个概念：后验概率、似然、先验概率以及全概率。如下图示例 例</description>
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      <title>文献--椎间盘退行性疾病bulk与scRNA-seq数据挖掘</title>
      <link>https://lishensuo.github.io/posts/basic/104%E6%96%87%E7%8C%AE--%E6%A4%8E%E9%97%B4%E7%9B%98%E9%80%80%E8%A1%8C%E6%80%A7%E7%96%BE%E7%97%85bulk%E4%B8%8Escrna-seq%E6%95%B0%E6%8D%AE%E6%8C%96%E6%8E%98/</link>
      <pubDate>Thu, 01 Dec 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/104%E6%96%87%E7%8C%AE--%E6%A4%8E%E9%97%B4%E7%9B%98%E9%80%80%E8%A1%8C%E6%80%A7%E7%96%BE%E7%97%85bulk%E4%B8%8Escrna-seq%E6%95%B0%E6%8D%AE%E6%8C%96%E6%8E%98/</guid>
      <description>文章题目：Revealing the Key MSCs Niches and Pathogenic Genes in Influencing CEP Homeostasis: A Conjoint Analysis of Single-Cell and WGCNA 杂志/日期：Front. Immunol. / 27 June 2022 DOI：10.3389/fimmu.202</description>
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      <title>机器学习基于R包mlr3(5)--分类--SVM</title>
      <link>https://lishensuo.github.io/posts/bioinfo/104%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr35--%E5%88%86%E7%B1%BB--svm/</link>
      <pubDate>Sun, 10 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/104%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr35--%E5%88%86%E7%B1%BB--svm/</guid>
      <description>1、SVM相关 基本概念 超平面：比数据集的变量少一个维度的平面，也称为决策边界； 间隔：（对于硬间隔）训练数据中最接近决策边界的样本点与决策边界</description>
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      <link>https://lishensuo.github.io/posts/basic/105%E6%96%87%E7%8C%AE--%E5%B0%8F%E9%BC%A0%E7%BC%BA%E8%A1%80%E6%80%A7%E4%B8%AD%E9%A3%8E%E5%8D%95%E7%BB%86%E8%83%9E%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90/</link>
      <pubDate>Sun, 11 Dec 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/105%E6%96%87%E7%8C%AE--%E5%B0%8F%E9%BC%A0%E7%BC%BA%E8%A1%80%E6%80%A7%E4%B8%AD%E9%A3%8E%E5%8D%95%E7%BB%86%E8%83%9E%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90/</guid>
      <description>题目：Single-cell RNA-seq reveals the transcriptional landscape in ischemic stroke 期刊/日期：ournal of Cerebral Blood Flow &amp;amp; Metabolism / 24 May 2021 DOI：10.1177/0271678X211026</description>
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      <title>机器学习基于R包mlr3(6)--分类--决策树与随机森林</title>
      <link>https://lishensuo.github.io/posts/bioinfo/105%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr36--%E5%88%86%E7%B1%BB--%E5%86%B3%E7%AD%96%E6%A0%91%E4%B8%8E%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/105%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr36--%E5%88%86%E7%B1%BB--%E5%86%B3%E7%AD%96%E6%A0%91%E4%B8%8E%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97/</guid>
      <description>1、决策树基础 1.1 决策树的构成 （1）决策树由节点组成，可分为决策节点(Decision tree)与叶节点(leaf node)。 （2）从上到下的</description>
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      <title>文献--WGCNA发现的卵巢癌肿瘤相关成纤维细胞的预后模型</title>
      <link>https://lishensuo.github.io/posts/basic/106%E6%96%87%E7%8C%AE--wgcna%E5%8F%91%E7%8E%B0%E7%9A%84%E5%8D%B5%E5%B7%A2%E7%99%8C%E8%82%BF%E7%98%A4%E7%9B%B8%E5%85%B3%E6%88%90%E7%BA%A4%E7%BB%B4%E7%BB%86%E8%83%9E%E7%9A%84%E9%A2%84%E5%90%8E%E6%A8%A1%E5%9E%8B/</link>
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      <guid>https://lishensuo.github.io/posts/basic/106%E6%96%87%E7%8C%AE--wgcna%E5%8F%91%E7%8E%B0%E7%9A%84%E5%8D%B5%E5%B7%A2%E7%99%8C%E8%82%BF%E7%98%A4%E7%9B%B8%E5%85%B3%E6%88%90%E7%BA%A4%E7%BB%B4%E7%BB%86%E8%83%9E%E7%9A%84%E9%A2%84%E5%90%8E%E6%A8%A1%E5%9E%8B/</guid>
      <description>题目 ： Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer 期刊 | 日期 ： https://doi.org/10.3389/fimmu.2022.951582 | 06 June 2022 概述：发现6个基因（COL16A1, COL5A2, GREM1, LUM, SRPX, TIMP3）组成的卵巢癌肿瘤相关成纤维细</description>
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    <item>
      <title>文献--小鼠前列腺癌单细胞数据分析</title>
      <link>https://lishensuo.github.io/posts/basic/107%E6%96%87%E7%8C%AE--%E5%B0%8F%E9%BC%A0%E5%89%8D%E5%88%97%E8%85%BA%E7%99%8C%E5%8D%95%E7%BB%86%E8%83%9E%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90/</link>
      <pubDate>Tue, 10 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/107%E6%96%87%E7%8C%AE--%E5%B0%8F%E9%BC%A0%E5%89%8D%E5%88%97%E8%85%BA%E7%99%8C%E5%8D%95%E7%BB%86%E8%83%9E%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90/</guid>
      <description>题目：Defining cellular population dynamics at single-cell resolution during prostate cancer progression 期刊 | 日期 ：eLife | November 2022 DOI：https://doi.org/10.7554/eLife.7</description>
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    <item>
      <title>机器学习基于R包mlr3(8)--回归--线性回归</title>
      <link>https://lishensuo.github.io/posts/bioinfo/107%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr38--%E5%9B%9E%E5%BD%92--%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/</link>
      <pubDate>Fri, 22 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/107%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr38--%E5%9B%9E%E5%BD%92--%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/</guid>
      <description>1、关于线性回归 1.1 公式理解 由于实际问题很少遇到单变量线性回归，所以更常见的表示为通用线性模型： $$ y = \beta_0 + \beta_1x_1 + \beta_2x_2 + &amp;hellip;+\beta_kx_k + \epsilon $$ （1）β0表示截距，</description>
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    <item>
      <title>文献--hECA人类单细胞表达图谱平台</title>
      <link>https://lishensuo.github.io/posts/basic/108%E6%96%87%E7%8C%AE--heca%E4%BA%BA%E7%B1%BB%E5%8D%95%E7%BB%86%E8%83%9E%E8%A1%A8%E8%BE%BE%E5%9B%BE%E8%B0%B1%E5%B9%B3%E5%8F%B0/</link>
      <pubDate>Thu, 19 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/108%E6%96%87%E7%8C%AE--heca%E4%BA%BA%E7%B1%BB%E5%8D%95%E7%BB%86%E8%83%9E%E8%A1%A8%E8%BE%BE%E5%9B%BE%E8%B0%B1%E5%B9%B3%E5%8F%B0/</guid>
      <description>题目：hECA: The cell-centric assembly of a cell atlas 期刊 | 日期：iScience | May 20, 2022 DOI：https://doi.org/10.1016/j.isci.20</description>
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    <item>
      <title>机器学习基于R包mlr3(9)--回归--GAM非线性回归</title>
      <link>https://lishensuo.github.io/posts/bioinfo/108%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr39--%E5%9B%9E%E5%BD%92--gam%E9%9D%9E%E7%BA%BF%E5%9B%9E%E5%BD%92/</link>
      <pubDate>Wed, 27 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/108%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr39--%E5%9B%9E%E5%BD%92--gam%E9%9D%9E%E7%BA%BF%E5%9B%9E%E5%BD%92/</guid>
      <description>1、关于GAM非线性回归 (1) n阶多项式 如前所说，线性回归的假设是每个预测变量与输出变量之间为线性相关。即类似 y = ax + b。 当预测变量与输出变量之</description>
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    <item>
      <title>文献--immuCan肿瘤微环境单细胞数据库</title>
      <link>https://lishensuo.github.io/posts/basic/109%E6%96%87%E7%8C%AE--immucan%E8%82%BF%E7%98%A4%E5%BE%AE%E7%8E%AF%E5%A2%83%E5%8D%95%E7%BB%86%E8%83%9E%E6%95%B0%E6%8D%AE%E5%BA%93/</link>
      <pubDate>Thu, 02 Feb 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/109%E6%96%87%E7%8C%AE--immucan%E8%82%BF%E7%98%A4%E5%BE%AE%E7%8E%AF%E5%A2%83%E5%8D%95%E7%BB%86%E8%83%9E%E6%95%B0%E6%8D%AE%E5%BA%93/</guid>
      <description>题目：Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database 期刊/时间：Cancer Research / February 1, 2023 DOI：doi.org/10.1158/0008-54</description>
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    <item>
      <title>机器学习基于R包mlr3(10)--回归--岭回归&#43;LASSO回归&#43;弹性网络</title>
      <link>https://lishensuo.github.io/posts/bioinfo/109%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr310--%E5%9B%9E%E5%BD%92--%E5%B2%AD%E5%9B%9E%E5%BD%92&#43;lasson%E5%9B%9E%E5%BD%92&#43;%E5%BC%B9%E6%80%A7%E7%BD%91%E7%BB%9C/</link>
      <pubDate>Sat, 07 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/109%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr310--%E5%9B%9E%E5%BD%92--%E5%B2%AD%E5%9B%9E%E5%BD%92&#43;lasson%E5%9B%9E%E5%BD%92&#43;%E5%BC%B9%E6%80%A7%E7%BD%91%E7%BB%9C/</guid>
      <description>在建立通用线性模型时，当模型参数即斜率值绝对值过大时，容易存在过拟合的风险。可通过下面介绍的3种正则化方法将每个预测变量的斜率参数缩小为0或</description>
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      <title>文献--克罗恩肠炎疾病的大型单细胞图谱</title>
      <link>https://lishensuo.github.io/posts/basic/110%E6%96%87%E7%8C%AE--%E5%85%8B%E7%BD%97%E6%81%A9%E8%82%A0%E7%82%8E%E7%96%BE%E7%97%85%E7%9A%84%E5%A4%A7%E5%9E%8B%E5%8D%95%E7%BB%86%E8%83%9E%E5%9B%BE%E8%B0%B1/</link>
      <pubDate>Sun, 05 Feb 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/110%E6%96%87%E7%8C%AE--%E5%85%8B%E7%BD%97%E6%81%A9%E8%82%A0%E7%82%8E%E7%96%BE%E7%97%85%E7%9A%84%E5%A4%A7%E5%9E%8B%E5%8D%95%E7%BB%86%E8%83%9E%E5%9B%BE%E8%B0%B1/</guid>
      <description>题目：The landscape of immune dysregulation in Crohn’s disease revealed through single-cell transcriptomic profiling in the ileum and colon 期刊 | 时间：Immunity | January 30, 2023 DOI：https://doi.org/10.</description>
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      <title>机器学习基于R包mlr3(11)--回归--kNN&#43;随机森林&#43;XGBboost</title>
      <link>https://lishensuo.github.io/posts/bioinfo/110%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr311--%E5%9B%9E%E5%BD%92--knn&#43;%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97&#43;xgboost/</link>
      <pubDate>Sun, 08 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/110%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr311--%E5%9B%9E%E5%BD%92--knn&#43;%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97&#43;xgboost/</guid>
      <description>1 2 library(mlr3verse) library(tidyverse) 0、示例数据 1 2 3 4 5 6 7 8 9 10 11 12 13 data(Iowa, package = &amp;#34;lasso2&amp;#34;) head(Iowa) # Year Rain0 Temp1 Rain1 Temp2 Rain2 Temp3 Rain3 Temp4 Yield # 1 1930 17.75 60.2 5.83 69.0 1.49 77.9 2.42 74.4 34.0 # 2 1931 14.76 57.5 3.83 75.0 2.72 77.2 3.30 72.6 32.9 # 3 1932 27.99 62.3 5.17 72.0</description>
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    <item>
      <title>文献-数据挖掘-肾癌与凝血相关基因</title>
      <link>https://lishensuo.github.io/posts/basic/111%E6%96%87%E7%8C%AE-%E6%95%B0%E6%8D%AE%E6%8C%96%E6%8E%98-%E8%82%BE%E7%99%8C%E4%B8%8E%E5%87%9D%E8%A1%80%E7%9B%B8%E5%85%B3%E5%9F%BA%E5%9B%A0/</link>
      <pubDate>Sat, 01 Apr 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/111%E6%96%87%E7%8C%AE-%E6%95%B0%E6%8D%AE%E6%8C%96%E6%8E%98-%E8%82%BE%E7%99%8C%E4%B8%8E%E5%87%9D%E8%A1%80%E7%9B%B8%E5%85%B3%E5%9F%BA%E5%9B%A0/</guid>
      <description>题目: Integrated analysis to identify the prognostic and immunotherapeutic roles of coagulation-associated gene signature in clear cell renal cell carcinoma 期刊 | 日期：Frontiers in Immunology | 17 March 2023 DOI：https://doi.org/10.3389</description>
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      <title>机器学习基于R包mlr3(12)--降维--PCA</title>
      <link>https://lishensuo.github.io/posts/bioinfo/111%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr312--%E9%99%8D%E7%BB%B4--pca/</link>
      <pubDate>Thu, 12 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/111%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr312--%E9%99%8D%E7%BB%B4--pca/</guid>
      <description>降维是指在保留尽可能多原始数据条件下，将许多变量(成百上千)转换为少数的、不相关的变量，从而有利于后序的数据分析与可视化。而主成分分析(PC</description>
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    <item>
      <title>文献-数据挖掘-肺腺癌相关基因对</title>
      <link>https://lishensuo.github.io/posts/basic/112%E6%96%87%E7%8C%AE-%E6%95%B0%E6%8D%AE%E6%8C%96%E6%8E%98-%E8%82%BA%E8%85%BA%E7%99%8C%E7%9B%B8%E5%85%B3%E5%9F%BA%E5%9B%A0%E5%AF%B9/</link>
      <pubDate>Sat, 01 Apr 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/112%E6%96%87%E7%8C%AE-%E6%95%B0%E6%8D%AE%E6%8C%96%E6%8E%98-%E8%82%BA%E8%85%BA%E7%99%8C%E7%9B%B8%E5%85%B3%E5%9F%BA%E5%9B%A0%E5%AF%B9/</guid>
      <description>题目：BTG2 and SerpinB5, a novel gene pair to evaluate the prognosis of lung adenocarcinoma 期刊 | 日期：Frontiers in Immunology | 17 March 2023 DOI：https://doi.org/10.3389/f</description>
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    <item>
      <title>机器学习基于R包mlr3(13)--降维t-SNE与UMAP</title>
      <link>https://lishensuo.github.io/posts/bioinfo/112%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr313--%E9%99%8D%E7%BB%B4t-sne%E4%B8%8Eumap/</link>
      <pubDate>Sat, 21 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/112%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr313--%E9%99%8D%E7%BB%B4t-sne%E4%B8%8Eumap/</guid>
      <description>算法简介 t-SNE 正态分布密度函数 ，其中σ表示标准差，μ表示均值 第一步：计算高维空间中任意两样本点的欧几里得距离。 第二步：对于任一特定节点，将其余节</description>
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    <item>
      <title>机器学习基于R包mlr3(14)--聚类-k均值</title>
      <link>https://lishensuo.github.io/posts/bioinfo/113%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr314--%E8%81%9A%E7%B1%BB-k%E5%9D%87%E5%80%BC/</link>
      <pubDate>Wed, 06 Jul 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/113%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr314--%E8%81%9A%E7%B1%BB-k%E5%9D%87%E5%80%BC/</guid>
      <description>1、算法简介 1.1 不同种K均值算法 k均值是常用的聚类算法之一。 （1）首先需要预先定义样本集中存在多少种聚类（假设为k），即数据集中处在K个真正意</description>
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    <item>
      <title>机器学习基于R包mlr3(15)--聚类-层次聚类</title>
      <link>https://lishensuo.github.io/posts/bioinfo/114%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr315--%E8%81%9A%E7%B1%BB-%E5%B1%82%E6%AC%A1%E8%81%9A%E7%B1%BB/</link>
      <pubDate>Sat, 18 Jun 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/114%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr315--%E8%81%9A%E7%B1%BB-%E5%B1%82%E6%AC%A1%E8%81%9A%E7%B1%BB/</guid>
      <description>1、层次聚类简介 1.1 计算步骤 层次聚类hierarchical clustering常用的是自下而上的聚合法(Agglomerative)。与之相</description>
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    <item>
      <title>机器学习基于R包mlr3(16)--聚类-EM混合分布</title>
      <link>https://lishensuo.github.io/posts/bioinfo/115%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr316--%E8%81%9A%E7%B1%BB-em%E6%B7%B7%E5%90%88%E5%88%86%E5%B8%83/</link>
      <pubDate>Sat, 18 Jun 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/115%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Er%E5%8C%85mlr316--%E8%81%9A%E7%B1%BB-em%E6%B7%B7%E5%90%88%E5%88%86%E5%B8%83/</guid>
      <description>1、算法与工具简介 1.1 EM算法 EM， Expectation-Maximization 期望最大化算法 混合分布：来自两种或两种以上概率分布(高斯分布最典型)的随机数据组成的一组混合数据所形成的</description>
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    <item>
      <title>机器学习基于sklearn(1)--sklearn基础</title>
      <link>https://lishensuo.github.io/posts/bioinfo/116%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Esklearn1--sklearn%E5%9F%BA%E7%A1%80/</link>
      <pubDate>Tue, 23 Aug 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/116%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Esklearn1--sklearn%E5%9F%BA%E7%A1%80/</guid>
      <description>一、数据预处理 1. 数据拆分 sklearn.model_selection.train_test_split() 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split import pandas as pd wine = load_wine() # 字典 feats = wine[&amp;#34;data&amp;#34;] feats_name = wine[&amp;#34;feature_names&amp;#34;] feats_df = pd.DataFrame(feats, columns=feats_name) targets</description>
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    <item>
      <title>机器学习基于sklearn(2)--交叉验证与超参数优化</title>
      <link>https://lishensuo.github.io/posts/bioinfo/117%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Esklearn2--%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E4%B8%8E%E8%B6%85%E5%8F%82%E6%95%B0%E4%BC%98%E5%8C%96/</link>
      <pubDate>Sun, 04 Sep 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/117%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Esklearn2--%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E4%B8%8E%E8%B6%85%E5%8F%82%E6%95%B0%E4%BC%98%E5%8C%96/</guid>
      <description>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 import pandas as pd from sklearn.datasets import load_wine from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier tree_classif = DecisionTreeClassifier() wine = load_wine() # 字典 feats = wine[&amp;#34;data&amp;#34;] feats_name = wine[&amp;#34;feature_names&amp;#34;] feats_df = pd.DataFrame(feats, columns=feats_name) targets = wine[&amp;#34;target&amp;#34;].reshape((-1,1)) #feats_df.shape, targets.shape train_X, test_X, train_y, test_y = train_test_split(feats_df, targets, test_size=0.2, random_state=42) train_X.shape,</description>
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      <title>机器学习基于sklearn(3)--常见分类任务学习器</title>
      <link>https://lishensuo.github.io/posts/bioinfo/118%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Esklearn3--%E5%B8%B8%E8%A7%81%E5%88%86%E7%B1%BB%E4%BB%BB%E5%8A%A1%E5%AD%A6%E4%B9%A0%E5%99%A8/</link>
      <pubDate>Sun, 04 Sep 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/118%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Esklearn3--%E5%B8%B8%E8%A7%81%E5%88%86%E7%B1%BB%E4%BB%BB%E5%8A%A1%E5%AD%A6%E4%B9%A0%E5%99%A8/</guid>
      <description>目的：演示常见几种分类器的使用方法，对其超参数调优候选超参数的选择 0、示例数据 1 2 3 4 5 6 7 8 9 10 11 12 from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.model_selection import cross_val_score from sklearn.model_selection import</description>
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      <title>机器学习基于sklearn(4)--常见回归任务学习器</title>
      <link>https://lishensuo.github.io/posts/bioinfo/119%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Esklearn4--%E5%B8%B8%E8%A7%81%E5%9B%9E%E5%BD%92%E4%BB%BB%E5%8A%A1%E5%AD%A6%E4%B9%A0%E5%99%A8/</link>
      <pubDate>Sun, 04 Sep 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/119%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E4%BA%8Esklearn4--%E5%B8%B8%E8%A7%81%E5%9B%9E%E5%BD%92%E4%BB%BB%E5%8A%A1%E5%AD%A6%E4%B9%A0%E5%99%A8/</guid>
      <description>目的：演示常见几种回归器的使用方法，对其超参数调优候选超参数的选择 0、示例数据 1 2 3 4 5 6 7 8 9 10 11 12 from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.model_selection import cross_val_score from sklearn.model_selection import</description>
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      <title>文献--综合多种分析建立TNBC预后模型</title>
      <link>https://lishensuo.github.io/posts/basic/120%E6%96%87%E7%8C%AE--%E7%BB%BC%E5%90%88%E5%A4%9A%E7%A7%8D%E5%88%86%E6%9E%90%E5%BB%BA%E7%AB%8Btnbc%E9%A2%84%E5%90%8E%E6%A8%A1%E5%9E%8B/</link>
      <pubDate>Fri, 06 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/120%E6%96%87%E7%8C%AE--%E7%BB%BC%E5%90%88%E5%A4%9A%E7%A7%8D%E5%88%86%E6%9E%90%E5%BB%BA%E7%AB%8Btnbc%E9%A2%84%E5%90%8E%E6%A8%A1%E5%9E%8B/</guid>
      <description>A network approach reveals driver genes associated with survival of patients with triple-negative breast cancer April 19, 2021; IF=5.4 https://doi.org/10.1016/j.isci.2021.102451 1、WGCNA分析 （1）对773个来自TCGA的BRCA(包括92个TNBC)的31338个基因表达</description>
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    <item>
      <title>机器学习--自动机器学习工具autogluon</title>
      <link>https://lishensuo.github.io/posts/bioinfo/130%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0--%E8%87%AA%E5%8A%A8%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%B7%A5%E5%85%B7autogluon/</link>
      <pubDate>Sat, 17 Sep 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/130%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0--%E8%87%AA%E5%8A%A8%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%B7%A5%E5%85%B7autogluon/</guid>
      <description>最先在李沐大神在B站的分享中了解到autogluon，它是一个自动机器学习工具，可用于文本图片识别、表格任务等。据说效果非常不错&amp;ndash</description>
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    <item>
      <title>数据库--药物与药物靶点TTD</title>
      <link>https://lishensuo.github.io/posts/bioinfo/150%E6%95%B0%E6%8D%AE%E5%BA%93--%E8%8D%AF%E7%89%A9%E4%B8%8E%E8%8D%AF%E7%89%A9%E9%9D%B6%E7%82%B9ttd/</link>
      <pubDate>Tue, 03 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/150%E6%95%B0%E6%8D%AE%E5%BA%93--%E8%8D%AF%E7%89%A9%E4%B8%8E%E8%8D%AF%E7%89%A9%E9%9D%B6%E7%82%B9ttd/</guid>
      <description>1、TTD数据库简介 首先关于靶点的生物学定义是： 生物学靶点（英語：Biological target）是指位于生物体内，能够被其他物质（配体、</description>
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    <item>
      <title>文献--四逆散与非酒精性脂肪肝网药分析</title>
      <link>https://lishensuo.github.io/posts/basic/150%E6%96%87%E7%8C%AE--%E5%9B%9B%E9%80%86%E6%95%A3%E4%B8%8E%E9%9D%9E%E9%85%92%E7%B2%BE%E6%80%A7%E8%84%82%E8%82%AA%E8%82%9D%E7%BD%91%E8%8D%AF%E5%88%86%E6%9E%90/</link>
      <pubDate>Sun, 10 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/150%E6%96%87%E7%8C%AE--%E5%9B%9B%E9%80%86%E6%95%A3%E4%B8%8E%E9%9D%9E%E9%85%92%E7%B2%BE%E6%80%A7%E8%84%82%E8%82%AA%E8%82%9D%E7%BD%91%E8%8D%AF%E5%88%86%E6%9E%90/</guid>
      <description>Network Pharmacology-Based Analysis on the Potential Biological Mechanisms of Sinisan Against Non-Alcoholic Fatty Liver Disease（四逆散&amp;ndash;非酒精性脂肪肝） 27 August 2021 Front Pharmacol, IF=5.8 DOI: 10.3389/fphar.2021.693701 分析思路 文章的流程图 1、数据收集 1.1 TCM数据</description>
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    <item>
      <title>基于PPI网络的靶点群距离预测药物组合</title>
      <link>https://lishensuo.github.io/posts/basic/151%E6%96%87%E7%8C%AE--%E5%9F%BA%E4%BA%8Eppi%E7%BD%91%E7%BB%9C%E7%9A%84%E9%9D%B6%E7%82%B9%E7%BE%A4%E8%B7%9D%E7%A6%BB%E9%A2%84%E6%B5%8B%E8%8D%AF%E7%89%A9%E7%BB%84%E5%90%88/</link>
      <pubDate>Fri, 29 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/151%E6%96%87%E7%8C%AE--%E5%9F%BA%E4%BA%8Eppi%E7%BD%91%E7%BB%9C%E7%9A%84%E9%9D%B6%E7%82%B9%E7%BE%A4%E8%B7%9D%E7%A6%BB%E9%A2%84%E6%B5%8B%E8%8D%AF%E7%89%A9%E7%BB%84%E5%90%88/</guid>
      <description>Network-based prediction of drug combinations Nature communication 2019 / 03 IF=15 https://doi.org/10.1038/s41467-019-09186-x 文章使用数据 1、PPI high-quality protein-protein interactions (PPIs) 原文说有243,603 PPIs connecting 16,677 unique proteins ，但是根据文章附件链接只有217160 PPIs, 涉及15970</description>
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    <item>
      <title>CMap数据库整理与使用方法</title>
      <link>https://lishensuo.github.io/posts/bioinfo/151%E6%95%B0%E6%8D%AE%E5%BA%93--%E8%8D%AF%E7%89%A9%E5%B9%B2%E6%89%B0%E8%BD%AC%E5%BD%95%E7%BB%84cmap/</link>
      <pubDate>Thu, 21 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/151%E6%95%B0%E6%8D%AE%E5%BA%93--%E8%8D%AF%E7%89%A9%E5%B9%B2%E6%89%B0%E8%BD%AC%E5%BD%95%E7%BB%84cmap/</guid>
      <description>Cmap LINCS计划采用L1000技术进行大规模的细胞系干扰实验测序，得到差异基因。具体可分为Phase-1，Phase-2两个阶段。数据已整理</description>
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    <item>
      <title>矩阵相似性计算样本通路一致性</title>
      <link>https://lishensuo.github.io/posts/basic/152%E6%96%87%E7%8C%AE--%E7%9F%A9%E9%98%B5%E7%9B%B8%E4%BC%BC%E6%80%A7%E8%AE%A1%E7%AE%97%E6%A0%B7%E6%9C%AC%E9%80%9A%E8%B7%AF%E4%B8%80%E8%87%B4%E6%80%A7/</link>
      <pubDate>Fri, 13 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/152%E6%96%87%E7%8C%AE--%E7%9F%A9%E9%98%B5%E7%9B%B8%E4%BC%BC%E6%80%A7%E8%AE%A1%E7%AE%97%E6%A0%B7%E6%9C%AC%E9%80%9A%E8%B7%AF%E4%B8%80%E8%87%B4%E6%80%A7/</guid>
      <description>本次的两篇文章属于同一团队，第一篇文章侧重提出计算方法；第二篇文章侧重于应用方法，发现生物学规律 Paper1：提出方法 SIGN: similarity identification in gene expression Bioinformatics 2019 / 2 IF</description>
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    <item>
      <title>MsigDB基因集数据库</title>
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      <link>https://lishensuo.github.io/posts/bioinfo/201%E9%87%8D%E5%90%AF%E9%9A%8F%E6%9C%BA%E6%B8%B8%E8%B5%B0%E7%AE%97%E6%B3%95%E4%B8%8Erandomwalkrestartmh%E5%8C%85/</link>
      <pubDate>Tue, 19 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/201%E9%87%8D%E5%90%AF%E9%9A%8F%E6%9C%BA%E6%B8%B8%E8%B5%B0%E7%AE%97%E6%B3%95%E4%B8%8Erandomwalkrestartmh%E5%8C%85/</guid>
      <description>1、关于RWR 1.1 算法简介 Random Walk with Restart，RWR重启随机游走算法 在给定的一个由节点和边组成的网络结构中（下面均已PPI蛋白相互作用网络为</description>
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      <title>Gene2vec算法根据基因对计算基因表示</title>
      <link>https://lishensuo.github.io/posts/bioinfo/202gene2vec%E7%AE%97%E6%B3%95%E6%A0%B9%E6%8D%AE%E5%9F%BA%E5%9B%A0%E5%AF%B9%E8%AE%A1%E7%AE%97%E5%9F%BA%E5%9B%A0%E8%A1%A8%E7%A4%BA/</link>
      <pubDate>Thu, 23 Jan 2025 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/202gene2vec%E7%AE%97%E6%B3%95%E6%A0%B9%E6%8D%AE%E5%9F%BA%E5%9B%A0%E5%AF%B9%E8%AE%A1%E7%AE%97%E5%9F%BA%E5%9B%A0%E8%A1%A8%E7%A4%BA/</guid>
      <description>学习Genecompass时，了解参考到可以基因调控网络信息（Gene pair），计算Gene的嵌入表示（Embedding） https://github.com/jingcheng-du/Gene2vec 关键是要在p</description>
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      <title>文献--单细胞组学大模型之scGPT</title>
      <link>https://lishensuo.github.io/posts/basic/202%E6%96%87%E7%8C%AE--%E5%8D%95%E7%BB%86%E8%83%9E%E7%BB%84%E5%AD%A6%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B9%8Bscgpt/</link>
      <pubDate>Sun, 29 Sep 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/202%E6%96%87%E7%8C%AE--%E5%8D%95%E7%BB%86%E8%83%9E%E7%BB%84%E5%AD%A6%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B9%8Bscgpt/</guid>
      <description>文献： scGPT: toward building a foundation model for single-cell multi-omics using generative AI 时间：2024 Feb. (Published) 期刊：Nature Method DOI：https://doi.org/10.1038/s41592-0</description>
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      <title>文献--单细胞组学大模型之scBERT</title>
      <link>https://lishensuo.github.io/posts/basic/203%E6%96%87%E7%8C%AE--%E5%8D%95%E7%BB%86%E8%83%9E%E7%BB%84%E5%AD%A6%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B9%8Bscbert/</link>
      <pubDate>Sat, 09 Nov 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/203%E6%96%87%E7%8C%AE--%E5%8D%95%E7%BB%86%E8%83%9E%E7%BB%84%E5%AD%A6%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B9%8Bscbert/</guid>
      <description>标题: scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data 期刊|日期：nature machine intelligence, 2022/09 DOI：https://doi.org/10.1038/s42256-0</description>
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      <title>文献--单细胞组学大模型之CellPLM</title>
      <link>https://lishensuo.github.io/posts/basic/204%E6%96%87%E7%8C%AE--%E5%8D%95%E7%BB%86%E8%83%9E%E7%BB%84%E5%AD%A6%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B9%8Bcellplm/</link>
      <pubDate>Sat, 16 Nov 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/204%E6%96%87%E7%8C%AE--%E5%8D%95%E7%BB%86%E8%83%9E%E7%BB%84%E5%AD%A6%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B9%8Bcellplm/</guid>
      <description>标题 CELLPLM: PRE-TRAINING OF CELL LANGUAGE MODEL BEYOND SINGLE CELLS https://openreview.net/forum?id=BKXvPDekud 发表 ICLR (The International Conference on Learning Representations) 2024 通讯 Jiliang Tang | Computer science and engineering department | Michigan State University | https://www.cse.msu.edu/~tangjili/ 1. 简介 （1）scRNA-seq data与natural langu</description>
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      <title>文献--单细胞组学大模型之Geneformer</title>
      <link>https://lishensuo.github.io/posts/basic/205%E6%96%87%E7%8C%AE--%E5%8D%95%E7%BB%86%E8%83%9E%E7%BB%84%E5%AD%A6%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B9%8Bgeneformer/</link>
      <pubDate>Sun, 15 Dec 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/205%E6%96%87%E7%8C%AE--%E5%8D%95%E7%BB%86%E8%83%9E%E7%BB%84%E5%AD%A6%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B9%8Bgeneformer/</guid>
      <description>Transfer learning enables predictions in network biology | Nature https://doi.org/10.1038/s41586-023-06139-9 Received: March 2022 / Accepted: 27 April 2023 Data &amp;amp; Code: https://huggingface.co/ctheodoris 通讯作者：Patrick T. Ellinor, M.D., Ph.D. https://www.ellinorlab.org/team/ 1. 预训练模型 1.1 scRNA-seq数据 数据收集：Genecorp</description>
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      <title>文献--单细胞组学大模型之Genecompass</title>
      <link>https://lishensuo.github.io/posts/basic/206%E6%96%87%E7%8C%AE--%E5%8D%95%E7%BB%86%E8%83%9E%E7%BB%84%E5%AD%A6%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B9%8Bgenecompass/</link>
      <pubDate>Sun, 12 Jan 2025 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/206%E6%96%87%E7%8C%AE--%E5%8D%95%E7%BB%86%E8%83%9E%E7%BB%84%E5%AD%A6%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B9%8Bgenecompass/</guid>
      <description>GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model Cell research | 2024.10 https://doi.org/10.1038/s41422-024-01034-y https://github.com/xCompass-AI/GeneCompass (最后)通讯作者 Xin Li 0. 模型预训练 0.1 数据规模 初始收集126M，经QC后，101M细胞用于训练(53M 人源 /</description>
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      <title>文献--单细胞组学大模型之Geneformer V2[预印本]</title>
      <link>https://lishensuo.github.io/posts/basic/207%E6%96%87%E7%8C%AE--%E5%8D%95%E7%BB%86%E8%83%9E%E7%BB%84%E5%AD%A6%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B9%8Bgeneformer-v2%E9%A2%84%E5%8D%B0%E6%9C%AC/</link>
      <pubDate>Sat, 18 Jan 2025 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/207%E6%96%87%E7%8C%AE--%E5%8D%95%E7%BB%86%E8%83%9E%E7%BB%84%E5%AD%A6%E5%A4%A7%E6%A8%A1%E5%9E%8B%E4%B9%8Bgeneformer-v2%E9%A2%84%E5%8D%B0%E6%9C%AC/</guid>
      <description>Quantized multi-task learning for context-specific representations of gene network dynamics 2024.8.19, bioRxiv 【预印本】 https://doi.org/10.1101/2024.08.16.608180 通讯作者：Christina Theodoris （2021 Geneformer的一作，现在应该是独立的PI了） https://gladstone.org/people/christina-theodoris 背景知识 算</description>
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      <title>R-数据分析-dplyr表格操作</title>
      <link>https://lishensuo.github.io/posts/program/210r-%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90-dplyr%E8%A1%A8%E6%A0%BC%E6%93%8D%E4%BD%9C/</link>
      <pubDate>Sun, 08 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/210r-%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90-dplyr%E8%A1%A8%E6%A0%BC%E6%93%8D%E4%BD%9C/</guid>
      <description>1 2 3 4 5 6 7 8 9 library(tidyverse) # -- Attaching packages ----------------------------------------------------- tidyverse 1.3.1 -- # √ ggplot2 3.3.5 √ purrr 0.3.4 # √ tibble 3.1.2 √ dplyr 1.0.7 # √ tidyr 1.1.3 √ stringr 1.4.0 # √ readr 2.0.0 √ forcats 0.5.1 # -- Conflicts -------------------------------------------------------- tidyverse_conflicts() -- # x dplyr::filter() masks stats::filter() # x dplyr::lag() masks stats::lag() 1、表格筛选 1.1 select</description>
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      <title>R-数据分析-reshape2表格长短转换</title>
      <link>https://lishensuo.github.io/posts/program/211r-%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90-reshape2%E8%A1%A8%E6%A0%BC%E9%95%BF%E7%9F%AD%E8%BD%AC%E6%8D%A2/</link>
      <pubDate>Sat, 25 Jun 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/211r-%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90-reshape2%E8%A1%A8%E6%A0%BC%E9%95%BF%E7%9F%AD%E8%BD%AC%E6%8D%A2/</guid>
      <description>1 2 library(tidyverse) library(reshape2) 1、matrix 1 2 3 4 5 6 7 8 9 10 11 12 set.seed(123) scores_mt = matrix(round(rnorm(40, mean = 80, sd=10)), nrow = 10, ncol = 4, dimnames = list(paste0(&amp;#34;Stu&amp;#34;,1:10), paste0(&amp;#34;Subject-&amp;#34;,LETTERS[1:4]))) class(scores_mt) # [1] &amp;#34;matrix&amp;#34; &amp;#34;array&amp;#34; head(scores_mt) # Subject-A Subject-B Subject-C Subject-D # Stu1 74 92 69 84 # Stu2 78 84 78 77 # Stu3 96 84 70</description>
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      <title>R-可视化-ggplot2绘图基础</title>
      <link>https://lishensuo.github.io/posts/program/212r-%E5%8F%AF%E8%A7%86%E5%8C%96-ggplot2%E7%BB%98%E5%9B%BE%E5%9F%BA%E7%A1%80/</link>
      <pubDate>Sun, 03 Jul 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/212r-%E5%8F%AF%E8%A7%86%E5%8C%96-ggplot2%E7%BB%98%E5%9B%BE%E5%9F%BA%E7%A1%80/</guid>
      <description>ggplot2包一方面可以实现多种形式的数据可视化、比如箱图、柱状图等；另一方面也可以从多个角度进行美化、修饰。对于前者，之前对ggplot</description>
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      <title>R-可视化-ggpubr包快速绘制点图、线图与柱状图</title>
      <link>https://lishensuo.github.io/posts/program/213r-%E5%8F%AF%E8%A7%86%E5%8C%96-ggpubr%E5%8C%85%E5%BF%AB%E9%80%9F%E7%BB%98%E5%88%B6%E7%82%B9%E5%9B%BE%E7%BA%BF%E5%9B%BE%E4%B8%8E%E6%9F%B1%E7%8A%B6%E5%9B%BE/</link>
      <pubDate>Sat, 23 Jul 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/213r-%E5%8F%AF%E8%A7%86%E5%8C%96-ggpubr%E5%8C%85%E5%BF%AB%E9%80%9F%E7%BB%98%E5%88%B6%E7%82%B9%E5%9B%BE%E7%BA%BF%E5%9B%BE%E4%B8%8E%E6%9F%B1%E7%8A%B6%E5%9B%BE/</guid>
      <description>参考教程：http://www.sthda.com/english/articles/24-ggpubr-publication-ready</description>
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      <title>R-可视化-拼图patchwork</title>
      <link>https://lishensuo.github.io/posts/program/214r-%E5%8F%AF%E8%A7%86%E5%8C%96-%E6%8B%BC%E5%9B%BEpatchwork/</link>
      <pubDate>Sun, 04 Sep 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/214r-%E5%8F%AF%E8%A7%86%E5%8C%96-%E6%8B%BC%E5%9B%BEpatchwork/</guid>
      <description>一、ggplot2组图 0、安装包及示例图 1 2 3 4 5 6 7 8 9 10 # #install.packages(&amp;#34;devtools&amp;#34;) # devtools::install_github(&amp;#34;thomasp85/patchwork&amp;#34;) library(ggplot2) library(patchwork) p1 &amp;lt;- ggplot(mtcars) + geom_point(aes(mpg, disp)) p2 &amp;lt;- ggplot(mtcars) + geom_boxplot(aes(gear, disp, group = gear)) p3 &amp;lt;- ggplot(mtcars) + geom_bar(aes(gear)) + facet_wrap(~cyl) p4 &amp;lt;- ggplot(mtcars) + geom_bar(aes(carb)) p5 &amp;lt;- ggplot(mtcars) + geom_violin(aes(cyl, mpg, group</description>
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      <title>R-可视化-韦恩图venn</title>
      <link>https://lishensuo.github.io/posts/program/215r-%E5%8F%AF%E8%A7%86%E5%8C%96-%E9%9F%A6%E6%81%A9%E5%9B%BEvenn/</link>
      <pubDate>Sat, 10 Sep 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/215r-%E5%8F%AF%E8%A7%86%E5%8C%96-%E9%9F%A6%E6%81%A9%E5%9B%BEvenn/</guid>
      <description>示例数据 3个基因集列表，为list格式 1 2 3 4 5 genes &amp;lt;- paste0(&amp;#34;gene&amp;#34;,1:1000) set.seed(20210302) gene_list &amp;lt;- list(A = sample(genes,100), B = sample(genes,200), C = sample(genes,300)) 一、VennDiagram VennDiagram包是绘制韦恩图</description>
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      <title>R-可视化-ggplot&#43;ggpubr绘制箱图boxplot</title>
      <link>https://lishensuo.github.io/posts/program/216ggplot&#43;ggpubr%E7%BB%98%E5%88%B6%E7%AE%B1%E5%9B%BEboxplot/</link>
      <pubDate>Sat, 05 Nov 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/216ggplot&#43;ggpubr%E7%BB%98%E5%88%B6%E7%AE%B1%E5%9B%BEboxplot/</guid>
      <description>一、ggplot绘制基础箱图 0、示例数据 1 2 3 4 5 6 7 8 9 10 11 12 13 library(ggplot2) library(patchwork) #组别名最好是字符型；如果是数值类型，最好转为因子化 ToothGrowth$dose = factor(ToothGrowth$dose) summary(ToothGrowth) # len supp dose #</description>
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      <title>R-可视化-ggplot绘制常规柱状图barplot</title>
      <link>https://lishensuo.github.io/posts/program/217ggplot%E7%BB%98%E5%88%B6%E5%B8%B8%E8%A7%84%E6%9F%B1%E7%8A%B6%E5%9B%BEbarplot/</link>
      <pubDate>Sat, 05 Nov 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/217ggplot%E7%BB%98%E5%88%B6%E5%B8%B8%E8%A7%84%E6%9F%B1%E7%8A%B6%E5%9B%BEbarplot/</guid>
      <description>1、示例数据 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 library(ggplot2) library(patchwork) library(carData) #示例数据 head(Salaries) #教职工资情况 # rank discipline yrs.since.phd yrs.service sex salary # 1 Prof B 19 18 Male 139750 # 2 Prof B 20 16 Male 173200 # 3 AsstProf B 4 3 Male 79750 #</description>
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      <title>R-可视化-绘图颜色与画板的选择</title>
      <link>https://lishensuo.github.io/posts/program/218%E7%BB%98%E5%9B%BE%E9%A2%9C%E8%89%B2%E4%B8%8E%E7%94%BB%E6%9D%BF%E7%9A%84%E9%80%89%E6%8B%A9/</link>
      <pubDate>Sat, 05 Nov 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/218%E7%BB%98%E5%9B%BE%E9%A2%9C%E8%89%B2%E4%B8%8E%E7%94%BB%E6%9D%BF%E7%9A%84%E9%80%89%E6%8B%A9/</guid>
      <description>1、R中颜色的表示方式 1.1 颜色的名字 R内置了657种颜色的名字可供调用 http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf 1 2 3 4 5 6 7 8 9 10 11 str(colors()) # chr [1:657] &amp;#34;white&amp;#34; &amp;#34;aliceblue&amp;#34; &amp;#34;antiquewhite&amp;#34; ... head(colors()[1:10]) # [1] &amp;#34;white&amp;#34; &amp;#34;aliceblue&amp;#34; &amp;#34;antiquewhite&amp;#34; # [4] &amp;#34;antiquewhite1&amp;#34; &amp;#34;antiquewhite2&amp;#34; &amp;#34;antiquewhite3&amp;#34; set.seed(111) cols = sample(colors(), 5)</description>
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      <title>可视化-R--热图heatmap</title>
      <link>https://lishensuo.github.io/posts/program/219r-%E5%8F%AF%E8%A7%86%E5%8C%96--%E7%83%AD%E5%9B%BEheatmap/</link>
      <pubDate>Sun, 12 Jun 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/219r-%E5%8F%AF%E8%A7%86%E5%8C%96--%E7%83%AD%E5%9B%BEheatmap/</guid>
      <description>简单整理两个绘制热图R包的用法，分别是基础的pheatmap包与复杂的ComplexHeatmap包。 pheatmap 1 2 3 4 5 # install.packages(&amp;#34;pheatmap&amp;#34;) library(pheatmap) packageVersion(&amp;#34;pheatmap&amp;#34;) # [1] ‘1.0.12</description>
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      <title>R-可视化--弦图chordDiagram</title>
      <link>https://lishensuo.github.io/posts/program/220r-%E5%8F%AF%E8%A7%86%E5%8C%96--%E5%BC%A6%E5%9B%BEchorddiagram/</link>
      <pubDate>Mon, 16 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/220r-%E5%8F%AF%E8%A7%86%E5%8C%96--%E5%BC%A6%E5%9B%BEchorddiagram/</guid>
      <description>弦图常用于表示两组或多组成员之间的连接关系。如下简单学习circlize包ChordDiagram()绘制弦图的基础用法 参考教程：https</description>
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      <title>R-可视化--桑基(sankey)与冲击(alluvial)图ggsankey</title>
      <link>https://lishensuo.github.io/posts/program/221r-%E5%8F%AF%E8%A7%86%E5%8C%96--%E6%A1%91%E5%9F%BAsankey%E4%B8%8E%E5%86%B2%E5%87%BBalluvial%E5%9B%BEggsankey/</link>
      <pubDate>Mon, 16 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/221r-%E5%8F%AF%E8%A7%86%E5%8C%96--%E6%A1%91%E5%9F%BAsankey%E4%B8%8E%E5%86%B2%E5%87%BBalluvial%E5%9B%BEggsankey/</guid>
      <description>桑基图(sankey plot)是一种特定类型的流图，用于描述一组值到另一组值的流向；理论上来表示前一组值与后一组值存在一定的逻辑关系。从泛化</description>
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      <title>R-可视化--饼图与环图</title>
      <link>https://lishensuo.github.io/posts/program/222r-%E5%8F%AF%E8%A7%86%E5%8C%96--%E9%A5%BC%E5%9B%BE%E4%B8%8E%E7%8E%AF%E5%9B%BE/</link>
      <pubDate>Sat, 11 Mar 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/222r-%E5%8F%AF%E8%A7%86%E5%8C%96--%E9%A5%BC%E5%9B%BE%E4%B8%8E%E7%8E%AF%E5%9B%BE/</guid>
      <description>1、pie() pie()是基础的绘制饼图函数，用法简单 1 2 3 4 5 6 7 8 pie(Prop, labels = LETTERS[1:5], main = &amp;#34;Test Piechart&amp;#34;) # labels：设置标签 # radius：设置半径，de</description>
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    <item>
      <title>R-可视化--ggplot标注文本标签text&amp;label</title>
      <link>https://lishensuo.github.io/posts/program/223r-%E5%8F%AF%E8%A7%86%E5%8C%96--ggplot%E6%A0%87%E6%B3%A8%E6%96%87%E6%9C%AC%E6%A0%87%E7%AD%BEtextlabel/</link>
      <pubDate>Sat, 11 Mar 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/223r-%E5%8F%AF%E8%A7%86%E5%8C%96--ggplot%E6%A0%87%E6%B3%A8%E6%96%87%E6%9C%AC%E6%A0%87%E7%AD%BEtextlabel/</guid>
      <description>1、ggplot geom_text()通过交代文本的坐标位置与内容绘制，通常与点图联用。相关参数包括： color 标签的颜色 alpha 标签的透明度 check_overlap 若有重叠的</description>
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      <title>R-可视化--corrplot相关性绘图</title>
      <link>https://lishensuo.github.io/posts/program/224r-%E5%8F%AF%E8%A7%86%E5%8C%96--corrplot%E7%9B%B8%E5%85%B3%E6%80%A7%E7%BB%98%E5%9B%BE/</link>
      <pubDate>Sat, 01 Apr 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/224r-%E5%8F%AF%E8%A7%86%E5%8C%96--corrplot%E7%9B%B8%E5%85%B3%E6%80%A7%E7%BB%98%E5%9B%BE/</guid>
      <description>1 2 3 4 5 6 # install.packages(&amp;#34;corrplot&amp;#34;) library(corrplot) # 计算相关性矩阵 M&amp;lt;-cor(mtcars) dim(M) # [1] 11 11 1、可视化R系数方式 method参数提供了如下6种可视化方式 circle(default), square, ellipse, number, shade, color, pie 1 corrplot(M, method=&amp;#34;ellipse&amp;#34;) 2、对称布局方</description>
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      <title>R-可视化--ggplot绘制散点图添加边际图</title>
      <link>https://lishensuo.github.io/posts/program/225r-%E5%8F%AF%E8%A7%86%E5%8C%96--ggplot%E6%B7%BB%E5%8A%A0%E5%9B%9B%E5%91%A8%E8%BE%B9%E9%99%85%E5%9B%BE/</link>
      <pubDate>Sun, 09 Apr 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/225r-%E5%8F%AF%E8%A7%86%E5%8C%96--ggplot%E6%B7%BB%E5%8A%A0%E5%9B%9B%E5%91%A8%E8%BE%B9%E9%99%85%E5%9B%BE/</guid>
      <description>示例数据 1 2 3 4 5 6 7 8 9 10 11 12 data(&amp;#34;mtcars&amp;#34;) df &amp;lt;- mtcars df$name &amp;lt;- rownames(df) df$cyl &amp;lt;- as.factor(df$cyl) head(df[,c(&amp;#34;name&amp;#34;,&amp;#34;cyl&amp;#34;,&amp;#34;mpg&amp;#34;,&amp;#34;wt&amp;#34;)]) # name cyl mpg wt # Mazda RX4 Mazda RX4 6 21.0 2.620 # Mazda RX4 Wag Mazda RX4 Wag 6 21.0 2.875 # Datsun 710 Datsun 710 4 22.8 2.320 # Hornet 4 Drive Hornet 4 Drive 6 21.4 3.215 # Hornet</description>
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      <title>R-可视化--ggbeeswarm绘制蜂群图</title>
      <link>https://lishensuo.github.io/posts/program/226r-%E5%8F%AF%E8%A7%86%E5%8C%96--ggbeeswarm%E7%BB%98%E5%88%B6%E8%9C%82%E7%BE%A4%E5%9B%BE/</link>
      <pubDate>Sat, 22 Apr 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/226r-%E5%8F%AF%E8%A7%86%E5%8C%96--ggbeeswarm%E7%BB%98%E5%88%B6%E8%9C%82%E7%BE%A4%E5%9B%BE/</guid>
      <description>1 2 3 library(ggplot2) # install.packages(&amp;#34;ggbeeswarm&amp;#34;) library(ggbeeswarm) 0、示例数据 1 2 3 4 5 6 7 8 9 10 11 12 13 14 set.seed(1995) y &amp;lt;- round(rnorm(200), 1) df &amp;lt;- data.frame(y = y, group = sample(c(&amp;#34;G1&amp;#34;, &amp;#34;G2&amp;#34;, &amp;#34;G3&amp;#34;), size = 200, replace = TRUE)) head(df) # y group # 1 1.1 G3 # 2 -0.3 G2 # 3 0.1 G2 # 4 0.4 G1 # 5</description>
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      <title>R-可视化--ggsignif注释显著性</title>
      <link>https://lishensuo.github.io/posts/program/227r-%E5%8F%AF%E8%A7%86%E5%8C%96--ggsignif%E6%B3%A8%E9%87%8A%E6%98%BE%E8%91%97%E6%80%A7/</link>
      <pubDate>Sat, 22 Apr 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/227r-%E5%8F%AF%E8%A7%86%E5%8C%96--ggsignif%E6%B3%A8%E9%87%8A%E6%98%BE%E8%91%97%E6%80%A7/</guid>
      <description>Github tutorial：https://github.com/const-ae/ggsignif 1 2 library(ggplot2) library(ggsignif) 1、comparisons设置分组，计算</description>
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      <title>R-可视化--funkyheatmap绘制table热图</title>
      <link>https://lishensuo.github.io/posts/program/228r-%E5%8F%AF%E8%A7%86%E5%8C%96--funkyheatmap%E7%BB%98%E5%88%B6table%E7%83%AD%E5%9B%BE/</link>
      <pubDate>Tue, 13 Jun 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/228r-%E5%8F%AF%E8%A7%86%E5%8C%96--funkyheatmap%E7%BB%98%E5%88%B6table%E7%83%AD%E5%9B%BE/</guid>
      <description>https://github.com/funkyheatmap/funkyheatmap 1 2 3 4 5 6 7 8 # install.packages(&amp;#34;funkyheatmap&amp;#34;) library(funkyheatmap) library(tidyverse) data(&amp;#34;mtcars&amp;#34;) vignette(&amp;#34;dynbenchmark&amp;#34;, &amp;#34;funkyheatmap&amp;#34;) #vignette(&amp;#34;mtcars&amp;#34;, &amp;#34;funkyheatmap&amp;#34;) 1、简单用法 对于文本列，直接显示相应文本；对于数值列，使用几何图形映射数值大小。 1 2 3 4 5 6 7 8 9 10 11 12</description>
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      <title>R-数据分析--tidyr表格操作</title>
      <link>https://lishensuo.github.io/posts/program/229r-%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90--tidyr%E8%A1%A8%E6%A0%BC%E6%93%8D%E4%BD%9C/</link>
      <pubDate>Sat, 30 Nov 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/229r-%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90--tidyr%E8%A1%A8%E6%A0%BC%E6%93%8D%E4%BD%9C/</guid>
      <description>tidyr是tidyverse系列的组成工具包之一，其主要功能侧重于表格的reshaping and organizing. 下面简要学习其几个比较常用的函数。 https://tidyr.tidyverse.org/ https://github.com/rstudio/cheatsheets/blob/main/tidyr.pdf 1 library(tidyverse) 1.</description>
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    <item>
      <title>R语言RSelenium爬虫.md</title>
      <link>https://lishensuo.github.io/posts/program/250r%E8%AF%AD%E8%A8%80rselenium%E7%88%AC%E8%99%AB/</link>
      <pubDate>Tue, 10 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/250r%E8%AF%AD%E8%A8%80rselenium%E7%88%AC%E8%99%AB/</guid>
      <description>1、html基础与xpath语法 1.1 html基础 大多数网页内容都是html结构组织的，在我理解就是有众多节点(node)组成的树状结构； 而我们</description>
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    <item>
      <title>基于Rstudio创建R包</title>
      <link>https://lishensuo.github.io/posts/program/252%E5%9F%BA%E4%BA%8Erstudio%E5%88%9B%E5%BB%BAr%E5%8C%85/</link>
      <pubDate>Sun, 22 Jan 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/252%E5%9F%BA%E4%BA%8Erstudio%E5%88%9B%E5%BB%BAr%E5%8C%85/</guid>
      <description>总结一下目前学习的，基于Rstudio的创建R包流程。 Part1：创建R包基本流程 step1：建立开发环境 目的是创建一个文件夹，用于储存编写</description>
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    <item>
      <title>shiny包开发网页APP基础</title>
      <link>https://lishensuo.github.io/posts/program/251shiny%E5%8C%85%E5%BC%80%E5%8F%91%E7%BD%91%E9%A1%B5app/</link>
      <pubDate>Sun, 22 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/251shiny%E5%8C%85%E5%BC%80%E5%8F%91%E7%BD%91%E9%A1%B5app/</guid>
      <description>主要参考教程 ：https://shiny.rstudio.com/tutorial/written-tutorial/lesson1/ 1、基</description>
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    <item>
      <title>R包purrr的函数编程</title>
      <link>https://lishensuo.github.io/posts/program/253r%E5%8C%85purrr%E7%9A%84%E5%87%BD%E6%95%B0%E7%BC%96%E7%A8%8B/</link>
      <pubDate>Sun, 09 Jul 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/253r%E5%8C%85purrr%E7%9A%84%E5%87%BD%E6%95%B0%E7%BC%96%E7%A8%8B/</guid>
      <description>purrr包是tidyverse系列中用于函数编程的工具，很多用法类似R base的apply系列函数。现参考相关资料，学习其基本用法。 https://jennybc.github.io/purrr-tutorial/index.html https://shixiangwang.github.io/home/cn/post/purrr-loop/ p</description>
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      <title>shiny包开发网页APP进阶</title>
      <link>https://lishensuo.github.io/posts/program/254shiny%E5%8C%85%E5%BC%80%E5%8F%91%E7%BD%91%E9%A1%B5app%E8%BF%9B%E9%98%B6/</link>
      <pubDate>Sun, 13 Aug 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/254shiny%E5%8C%85%E5%BC%80%E5%8F%91%E7%BD%91%E9%A1%B5app%E8%BF%9B%E9%98%B6/</guid>
      <description>shiny中server部分的render×××系列函数是生成reactive output的主要方式。只要其内部的任一input$×××输入</description>
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    <item>
      <title>Shiny-basic-1-IO</title>
      <link>https://lishensuo.github.io/posts/program/255shiny-basic-1-io/</link>
      <pubDate>Sun, 31 Mar 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/255shiny-basic-1-io/</guid>
      <description>Shiny Basic系列: 参考教程：https://mastering-shiny.org/ （1）IO控件 （2）Layout布局 （3）Reactiv</description>
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    <item>
      <title>Shiny-basic-2-Layout</title>
      <link>https://lishensuo.github.io/posts/program/256shiny-basic-2-layout/</link>
      <pubDate>Sun, 31 Mar 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/256shiny-basic-2-layout/</guid>
      <description>Shiny Basic系列: 参考教程：https://mastering-shiny.org/ （1）IO控件 （2）Layout布局 （3）Reactiv</description>
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    <item>
      <title>Shiny-basic-3-Reactive</title>
      <link>https://lishensuo.github.io/posts/program/257shiny-basic-3-reactive/</link>
      <pubDate>Sun, 31 Mar 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/257shiny-basic-3-reactive/</guid>
      <description>Shiny Basic系列: 参考教程：https://mastering-shiny.org/ （1）IO控件 （2）Layout布局 （3）Reactiv</description>
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    <item>
      <title>Shiny-basic-4-Feedback</title>
      <link>https://lishensuo.github.io/posts/program/258shiny-basic-4-feedback/</link>
      <pubDate>Sun, 31 Mar 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/258shiny-basic-4-feedback/</guid>
      <description>Shiny Basic系列: 参考教程：https://mastering-shiny.org/ （1）IO控件 （2）Layout布局 （3）Reactiv</description>
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    <item>
      <title>Shiny-basic-5-Module</title>
      <link>https://lishensuo.github.io/posts/program/259shiny-basic-5-module/</link>
      <pubDate>Sun, 31 Mar 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/259shiny-basic-5-module/</guid>
      <description>Shiny Basic系列: 参考教程：https://mastering-shiny.org/ （1）IO控件 （2）Layout布局 （3）Reactiv</description>
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    <item>
      <title>Shiny-pkg-1-shinyWidgets</title>
      <link>https://lishensuo.github.io/posts/program/260shiny-pkg-1-shinywidgets/</link>
      <pubDate>Sun, 31 Mar 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/260shiny-pkg-1-shinywidgets/</guid>
      <description>Shiny Basic系列: 参考教程：https://mastering-shiny.org/ （1）IO控件 （2）Layout布局 （3）Reactiv</description>
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    <item>
      <title>Shiny-pkg-2-shinyJS</title>
      <link>https://lishensuo.github.io/posts/program/261shiny-pkg-2-shinyjs/</link>
      <pubDate>Sun, 31 Mar 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/261shiny-pkg-2-shinyjs/</guid>
      <description>Shiny Basic系列: 参考教程：https://mastering-shiny.org/ （1）IO控件 （2）Layout布局 （3）Reactiv</description>
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    <item>
      <title>Shiny-pkg-3-shinydashboard</title>
      <link>https://lishensuo.github.io/posts/program/262shiny-pkg-3-shinydashboard/</link>
      <pubDate>Sun, 31 Mar 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/262shiny-pkg-3-shinydashboard/</guid>
      <description>Shiny Basic系列: 参考教程：https://mastering-shiny.org/ （1）IO控件 （2）Layout布局 （3）Reactiv</description>
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    <item>
      <title>Shiny-pkg-4-shinydashboardPlus</title>
      <link>https://lishensuo.github.io/posts/program/263shiny-pkg-4-shinydashboardplus/</link>
      <pubDate>Sun, 31 Mar 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/263shiny-pkg-4-shinydashboardplus/</guid>
      <description>Shiny Basic系列: 参考教程：https://mastering-shiny.org/ （1）IO控件 （2）Layout布局 （3）Reactiv</description>
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    <item>
      <title>Shiny-pkg-5-bslib</title>
      <link>https://lishensuo.github.io/posts/program/264shiny-pkg-5-bslib/</link>
      <pubDate>Sun, 31 Mar 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/264shiny-pkg-5-bslib/</guid>
      <description>Shiny Basic系列: 参考教程：https://mastering-shiny.org/ （1）IO控件 （2）Layout布局 （3）Reactiv</description>
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    <item>
      <title>Shiny-pkg-6-others</title>
      <link>https://lishensuo.github.io/posts/program/265shiny-pkg-6-others/</link>
      <pubDate>Sun, 31 Mar 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/265shiny-pkg-6-others/</guid>
      <description>Shiny Basic系列: 参考教程：https://mastering-shiny.org/ （1）IO控件 （2）Layout布局 （3）Reactiv</description>
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    <item>
      <title>renv包环境可重复性</title>
      <link>https://lishensuo.github.io/posts/program/266renv%E5%8C%85%E7%8E%AF%E5%A2%83%E5%8F%AF%E9%87%8D%E5%A4%8D%E6%80%A7/</link>
      <pubDate>Sat, 06 Apr 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/266renv%E5%8C%85%E7%8E%AF%E5%A2%83%E5%8F%AF%E9%87%8D%E5%A4%8D%E6%80%A7/</guid>
      <description>https://rstudio.github.io/renv 记录一个R项目所需的R包来源及其相应的版本号，方便对于其他用户的可重复性分析。 1. 初始化项目 在当前目录下创建一个文件夹，是包含一个R proj</description>
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    <item>
      <title>rhinoverse系列-rhino包</title>
      <link>https://lishensuo.github.io/posts/program/267rhinoverse%E7%B3%BB%E5%88%97-rhino%E5%8C%85/</link>
      <pubDate>Sat, 06 Apr 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/267rhinoverse%E7%B3%BB%E5%88%97-rhino%E5%8C%85/</guid>
      <description>rhinoverse系列 rhinoverse系列-rhino包； rhinoverse系列-shiny.router包； https://github.com/Appsilon/rhino/ https://appsilon.github.io/rhino/ 1 2 3 4 5 6 7 install.packages(&amp;#34;rhino&amp;#34;) packageVersion(&amp;#34;rhino&amp;#34;)</description>
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    <item>
      <title>rhinoverse系列-shiny.router包</title>
      <link>https://lishensuo.github.io/posts/program/268rhinoverse%E7%B3%BB%E5%88%97-shiny.router%E5%8C%85/</link>
      <pubDate>Sat, 06 Apr 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/268rhinoverse%E7%B3%BB%E5%88%97-shiny.router%E5%8C%85/</guid>
      <description>rhinoverse系列 rhinoverse系列-rhino包； rhinoverse系列-shiny.router包； https://appsilon.github.io/shiny.router/ https://github.com/Appsilon/shiny.router 在网页设计和开发中</description>
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    <item>
      <title>UCSCXenaShiny development tutorials(231119)</title>
      <link>https://lishensuo.github.io/posts/program/300ucscxenashiny-dev-tutorials--231119/</link>
      <pubDate>Sun, 19 Nov 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/300ucscxenashiny-dev-tutorials--231119/</guid>
      <description>Recently, we have added some new modules for general TCGA data analysis and visualization. Here, we will provide the tutorials for easy use. Please note it is not the latest version and we are still in the stage of development. Therefore, if you have any question, please do not hesitate to contact us on GitHub or email (lishensuo@163.com) which could greatly contribute to UCSCXenaShiny V2.0 process. The temporary tutorial is</description>
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    <item>
      <title>文献--前列腺癌基础学习</title>
      <link>https://lishensuo.github.io/posts/basic/301%E7%94%9F%E7%89%A9-%E5%89%8D%E5%88%97%E8%85%BA%E7%99%8C%E5%9F%BA%E7%A1%80%E5%AD%A6%E4%B9%A0/</link>
      <pubDate>Thu, 19 Sep 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/basic/301%E7%94%9F%E7%89%A9-%E5%89%8D%E5%88%97%E8%85%BA%E7%99%8C%E5%9F%BA%E7%A1%80%E5%AD%A6%E4%B9%A0/</guid>
      <description>前列腺癌（Prostate cancer, PCa）肿瘤生物学基础的学习笔记 课程链接：https://www.bilibili.com/video/BV1D</description>
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      <title>shell基础--变量名字符串操作</title>
      <link>https://lishensuo.github.io/posts/program/400shell%E5%9F%BA%E7%A1%80--%E5%8F%98%E9%87%8F%E5%90%8D%E5%AD%97%E7%AC%A6%E4%B8%B2%E6%93%8D%E4%BD%9C/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/400shell%E5%9F%BA%E7%A1%80--%E5%8F%98%E9%87%8F%E5%90%8D%E5%AD%97%E7%AC%A6%E4%B8%B2%E6%93%8D%E4%BD%9C/</guid>
      <description>1 2 3 4 5 6 7 8 test=&amp;#34;https://www.baidu.com&amp;#34; #打印字符串内容 echo ${test} #https://www.baidu.com #打印字符串长度 echo ${#test} #21 1、截取 （1）指定截取字符串的起始位置与长度 对于字符串的起始位置定义：如果是从</description>
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    <item>
      <title>shell基础--find查找文件</title>
      <link>https://lishensuo.github.io/posts/program/401shell%E5%9F%BA%E7%A1%80--find%E6%9F%A5%E6%89%BE%E6%96%87%E4%BB%B6/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/401shell%E5%9F%BA%E7%A1%80--find%E6%9F%A5%E6%89%BE%E6%96%87%E4%BB%B6/</guid>
      <description>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # 第一个参数交待查找路径，默认为当前路径 # -name 参数指定需要查找的文件名，支持正则表达式 find -name &amp;#39;*.txt&amp;#39; find ./dir1/ -name &amp;#39;*.txt&amp;#39; #</description>
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    <item>
      <title>shell基础--tar.gz相关、gzip压缩与pigz多线程</title>
      <link>https://lishensuo.github.io/posts/program/402sehll%E5%9F%BA%E7%A1%80--gzip%E5%8E%8B%E7%BC%A9%E4%B8%8Epigz%E5%A4%9A%E7%BA%BF%E7%A8%8B/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/402sehll%E5%9F%BA%E7%A1%80--gzip%E5%8E%8B%E7%BC%A9%E4%B8%8Epigz%E5%A4%9A%E7%BA%BF%E7%A8%8B/</guid>
      <description>tar相关 1 2 3 4 5 6 7 8 9 10 11 #(1) 将多个文件打包为 .tar文件 tar -cvf test.tar dir1 file4 #(2) 将多个文件打包并压缩为 .tar.gz tar -czvf test.tar.gz dir1 file4 #(3) 将 .tar文件拆包 tar -xvf test.tar #(4) 将 .</description>
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      <title>shell基础--grep等文本处理命令</title>
      <link>https://lishensuo.github.io/posts/program/403shell%E5%9F%BA%E7%A1%80--grep%E7%AD%89%E6%96%87%E6%9C%AC%E5%A4%84%E7%90%86%E5%91%BD%E4%BB%A4/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/403shell%E5%9F%BA%E7%A1%80--grep%E7%AD%89%E6%96%87%E6%9C%AC%E5%A4%84%E7%90%86%E5%91%BD%E4%BB%A4/</guid>
      <description>1、grep 主要功能：查询符合条件的行 1 2 3 4 5 6 7 8 9 10 11 12 13 14 #返回符合条件的行,可使用正则表达式 who | grep &amp;#34;wuyt&amp;#34; # -n参数：返回符合条件的行，</description>
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      <title>linux进程管理与后台运行</title>
      <link>https://lishensuo.github.io/posts/program/450linux%E8%BF%9B%E7%A8%8B%E7%AE%A1%E7%90%86%E4%B8%8E%E5%90%8E%E5%8F%B0%E8%BF%90%E8%A1%8C/</link>
      <pubDate>Sat, 14 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/450linux%E8%BF%9B%E7%A8%8B%E7%AE%A1%E7%90%86%E4%B8%8E%E5%90%8E%E5%8F%B0%E8%BF%90%E8%A1%8C/</guid>
      <description>1、关于CPU与线程 1.1 简单理解进程与线程 进程：计算机执行的一个命令或者程序 线程：一般来说一个进程对应一个线程。但是如果进程任务可以分为若干独</description>
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      <title>初识slurm基础操作</title>
      <link>https://lishensuo.github.io/posts/program/451%E5%88%9D%E8%AF%86slurm%E5%9F%BA%E7%A1%80%E6%93%8D%E4%BD%9C/</link>
      <pubDate>Sat, 10 Sep 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/451%E5%88%9D%E8%AF%86slurm%E5%9F%BA%E7%A1%80%E6%93%8D%E4%BD%9C/</guid>
      <description>现阶段在学习深度学习超参数优化过程中涉及到SLURM相关知识。这几天探索了一番，简单记录一下目前简单认识。 SLURM，Simple Linux Utility for Resource M</description>
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      <title>Snakemake流程搭建</title>
      <link>https://lishensuo.github.io/posts/program/452snakemake%E6%B5%81%E7%A8%8B%E6%90%AD%E5%BB%BA/</link>
      <pubDate>Sun, 22 Sep 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/452snakemake%E6%B5%81%E7%A8%8B%E6%90%AD%E5%BB%BA/</guid>
      <description>https://snakemake.readthedocs.io/en/stable/tutorial/tutorial.html https://github.com/snakemake/snakemake 1. 简介 1.1 工具介绍 Snakemake 是一个用于创建可重现、模块化、自动化的工作流管理工具，主要用于处理复杂的数据分析流程；在生物信息学领域中有较多的应用。</description>
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      <title>window系统快速安装ubuntu子系统</title>
      <link>https://lishensuo.github.io/posts/program/500window%E7%B3%BB%E7%BB%9F%E5%BF%AB%E9%80%9F%E5%AE%89%E8%A3%85ubuntu%E5%AD%90%E7%B3%BB%E7%BB%9F/</link>
      <pubDate>Fri, 22 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/500window%E7%B3%BB%E7%BB%9F%E5%BF%AB%E9%80%9F%E5%AE%89%E8%A3%85ubuntu%E5%AD%90%E7%B3%BB%E7%BB%9F/</guid>
      <description>最近有在window系统安装linux子系统(WSL, Windows Subsystem for Linux)的需求，搜索发现Microsoft最近(4月8号)提供了更加简便的安</description>
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      <title>obabel化学小分子格式转换</title>
      <link>https://lishensuo.github.io/posts/bioinfo/500obabel%E5%8C%96%E5%AD%A6%E5%B0%8F%E5%88%86%E5%AD%90%E6%A0%BC%E5%BC%8F%E8%BD%AC%E6%8D%A2/</link>
      <pubDate>Sat, 16 Apr 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/500obabel%E5%8C%96%E5%AD%A6%E5%B0%8F%E5%88%86%E5%AD%90%E6%A0%BC%E5%BC%8F%E8%BD%AC%E6%8D%A2/</guid>
      <description>conda 安装 1 2 3 4 conda install -c conda-forge openbabel obabel # Open Babel 3.1.0 -- Nov 2 2021 -- 08:43:45 查看支持的格式 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 obabel -L # charges # descriptors # fingerprints # forcefields # formats # loaders # ops obabel -L formats | head</description>
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    <item>
      <title>化合物指纹与描述符生成系列工具</title>
      <link>https://lishensuo.github.io/posts/bioinfo/501%E5%8C%96%E5%90%88%E7%89%A9%E6%8C%87%E7%BA%B9%E4%B8%8E%E6%8F%8F%E8%BF%B0%E7%AC%A6%E7%94%9F%E6%88%90%E7%B3%BB%E5%88%97%E5%B7%A5%E5%85%B7/</link>
      <pubDate>Mon, 20 Mar 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/501%E5%8C%96%E5%90%88%E7%89%A9%E6%8C%87%E7%BA%B9%E4%B8%8E%E6%8F%8F%E8%BF%B0%E7%AC%A6%E7%94%9F%E6%88%90%E7%B3%BB%E5%88%97%E5%B7%A5%E5%85%B7/</guid>
      <description>1、rdkit 1 2 3 4 5 6 # conda install -c conda-forge rdkit from rdkit import Chem from rdkit.Chem import MACCSkeys from rdkit import DataStructs from rdkit.Chem import Draw 1.1 指纹编码式 （1）Topological Fingerprints 1 2 3 4 5 6 7 8 m = Chem.MolFromSmiles(&amp;#39;CCOC&amp;#39;) # Chem.MolToSmiles(mol) fp</description>
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    <item>
      <title>docker初学</title>
      <link>https://lishensuo.github.io/posts/program/501docker%E5%88%9D%E5%AD%A6/</link>
      <pubDate>Sat, 14 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/501docker%E5%88%9D%E5%AD%A6/</guid>
      <description>docker介绍 docker可以理解为轻量级的虚拟机，创建一个独立的环境，应用于特定的分析流程。 docker的镜像是一个定制的环境模板，而容</description>
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    <item>
      <title>化合物敏感度数据库GDSC_CTRL</title>
      <link>https://lishensuo.github.io/posts/bioinfo/502%E6%95%B0%E6%8D%AE%E5%BA%93--%E5%8C%96%E5%90%88%E7%89%A9%E6%95%8F%E6%84%9F%E5%BA%A6gdsc_ctrl/</link>
      <pubDate>Sun, 09 Oct 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/502%E6%95%B0%E6%8D%AE%E5%BA%93--%E5%8C%96%E5%90%88%E7%89%A9%E6%95%8F%E6%84%9F%E5%BA%A6gdsc_ctrl/</guid>
      <description>一、GDSC GDSC : https://www.cancerrxgene.org/，已上传至阿里云盘 1、原始数据整理 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16</description>
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    <item>
      <title>在Linux系统里部署Jupyter环境</title>
      <link>https://lishensuo.github.io/posts/program/502%E5%9C%A8linux%E7%B3%BB%E7%BB%9F%E9%87%8C%E9%83%A8%E7%BD%B2jupyter%E7%8E%AF%E5%A2%83/</link>
      <pubDate>Sun, 15 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/502%E5%9C%A8linux%E7%B3%BB%E7%BB%9F%E9%87%8C%E9%83%A8%E7%BD%B2jupyter%E7%8E%AF%E5%A2%83/</guid>
      <description>之前就了解过Jupyter notebook，是调试python代码的简洁、高效软件。现在大致整理下目前学到的在Linux系统里部署Jupyt</description>
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    <item>
      <title>ChemmineR处理化合物信息的基础工具R包</title>
      <link>https://lishensuo.github.io/posts/bioinfo/503chemminer%E5%A4%84%E7%90%86%E5%8C%96%E5%90%88%E7%89%A9%E4%BF%A1%E6%81%AF%E7%9A%84%E5%9F%BA%E7%A1%80%E5%B7%A5%E5%85%B7r%E5%8C%85/</link>
      <pubDate>Thu, 20 Jul 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/503chemminer%E5%A4%84%E7%90%86%E5%8C%96%E5%90%88%E7%89%A9%E4%BF%A1%E6%81%AF%E7%9A%84%E5%9F%BA%E7%A1%80%E5%B7%A5%E5%85%B7r%E5%8C%85/</guid>
      <description>ChemmineR是使用R语言实现化合物基础操作的工具包，现根据其官方文档学习其主要用法如下： https://www.bioconductor.org/packages/release/bioc/vignettes/ChemmineR/inst/doc/ChemmineR.html 1 2 3 4 5 6 if (!requireNamespace(&amp;#34;BiocManager&amp;#34;, quietly=TRUE)) install.packages(&amp;#34;BiocManager&amp;#34;) BiocManager::install(&amp;#34;ChemmineR&amp;#34;) library(&amp;#34;ChemmineR&amp;#34;) # library(&amp;#34;ChemmineOB&amp;#34;) 1. SDFset格</description>
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    <item>
      <title>Linux的parallel并行方法</title>
      <link>https://lishensuo.github.io/posts/program/503linux%E7%9A%84parallel%E5%B9%B6%E8%A1%8C%E6%96%B9%E6%B3%95/</link>
      <pubDate>Sun, 15 May 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/503linux%E7%9A%84parallel%E5%B9%B6%E8%A1%8C%E6%96%B9%E6%B3%95/</guid>
      <description>我对于parallel并行的简单理解即同时执行多个命令语句。尽管目前很多生信软件支持多线程运行(一条命令语句内)，但并不是设置的越多就越快，</description>
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    <item>
      <title>Shell_Python_R脚本基础</title>
      <link>https://lishensuo.github.io/posts/program/504shell_python_r%E8%84%9A%E6%9C%AC%E5%9F%BA%E7%A1%80/</link>
      <pubDate>Sat, 25 Mar 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/504shell_python_r%E8%84%9A%E6%9C%AC%E5%9F%BA%E7%A1%80/</guid>
      <description>1、shell脚本 1.1 脚本执行 （1）命令行直接执行 如下示例：脚本第一行设置脚本解释器的路径，如写错或没写，系统会调用默认解释器执行。然后在运行</description>
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    <item>
      <title>Json文件格式</title>
      <link>https://lishensuo.github.io/posts/program/505json%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F/</link>
      <pubDate>Tue, 05 Nov 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/505json%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F/</guid>
      <description>JSON (JavaScript Object Notation)：一种轻量级的数据交换格式，易于人类阅读和编写，同时也易于机器解析和生成。它广泛用于网络数据传输和配置文件。 JSON</description>
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    <item>
      <title>MySQL初学</title>
      <link>https://lishensuo.github.io/posts/program/600mysql%E5%88%9D%E5%AD%A6/</link>
      <pubDate>Sat, 22 Oct 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/program/600mysql%E5%88%9D%E5%AD%A6/</guid>
      <description>MySQL是一个常用的数据库管理系统，关于数据库可以简单理解多个有关联的二维表组成。 其中SQL(Structured Query Language)指操</description>
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    <item>
      <title>深度学习D2L--01--线性回归</title>
      <link>https://lishensuo.github.io/posts/bioinfo/701%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0d2l--01--%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/</link>
      <pubDate>Sun, 31 Jul 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/701%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0d2l--01--%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/</guid>
      <description>深度学习组成要素 线性回归可以认为是最简单的一层深度神经网络 一、从零实现 1 2 3 import numpy as np import torch import random 1、示例数据 模拟样本特征与标签数据，并分成小批量</description>
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    <item>
      <title>深度学习D2L--02--softmax多分类</title>
      <link>https://lishensuo.github.io/posts/bioinfo/702%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0d2l--02--softmax%E5%A4%9A%E5%88%86%E7%B1%BB/</link>
      <pubDate>Sun, 31 Jul 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/702%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0d2l--02--softmax%E5%A4%9A%E5%88%86%E7%B1%BB/</guid>
      <description>一、从零实现 1 2 3 4 import torch import torchvision from torch.utils import data from torchvision import transforms 1、示例数据 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ##数据集为fashion_minist，10类</description>
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      <title>深度学习D2L--03--K折交叉验证的torch训练基础流程</title>
      <link>https://lishensuo.github.io/posts/bioinfo/703%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0d2l--03--k%E6%8A%98%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E7%9A%84torch%E8%AE%AD%E7%BB%83%E5%9F%BA%E7%A1%80%E6%B5%81%E7%A8%8B/</link>
      <pubDate>Tue, 23 Aug 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/703%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0d2l--03--k%E6%8A%98%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E7%9A%84torch%E8%AE%AD%E7%BB%83%E5%9F%BA%E7%A1%80%E6%B5%81%E7%A8%8B/</guid>
      <description>1、加载库 1 2 3 4 5 6 7 import pandas as pd import torch from torch import nn from torch.nn import functional as F from torch.utils import data import itertools 2、示例数据 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 # http://d2l-data.s3-accelerate.amazonaws.com/kaggle_house_pred_train.csv # http://d2l-data.s3-accelerate.amazonaws.com/kaggle_house_pred_test.csv train_data = pd.read_csv(&amp;#34;../data/kaggle_house_pred_train.csv&amp;#34;) test_data = pd.read_csv(&amp;#34;../data/kaggle_house_pred_test.csv&amp;#34;) train_data.shape,</description>
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    <item>
      <title>深度学习--VAE变分自动编码器</title>
      <link>https://lishensuo.github.io/posts/bioinfo/704%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0--vae%E5%8F%98%E5%88%86%E8%87%AA%E5%8A%A8%E7%BC%96%E7%A0%81%E5%99%A8/</link>
      <pubDate>Tue, 21 Feb 2023 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/704%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0--vae%E5%8F%98%E5%88%86%E8%87%AA%E5%8A%A8%E7%BC%96%E7%A0%81%E5%99%A8/</guid>
      <description>自动编码器（AE, autoencoder）是应用神经网络进行数据降维的有效方，其结构分为编码器（encoder）与解码器（decoder）两</description>
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    <item>
      <title>D2L--第二章预备知识</title>
      <link>https://lishensuo.github.io/posts/bioinfo/705d2l-%E7%AC%AC%E4%BA%8C%E7%AB%A0%E9%A2%84%E5%A4%87%E7%9F%A5%E8%AF%86/</link>
      <pubDate>Sun, 21 Jul 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/705d2l-%E7%AC%AC%E4%BA%8C%E7%AB%A0%E9%A2%84%E5%A4%87%E7%9F%A5%E8%AF%86/</guid>
      <description>1. 数据操作 1.1 入门 张量：具有多个维度（轴）的数组。 具有一个轴的张量，对应数学上的向量； 具有两个轴的张量，对应数学上的矩阵。 创建张量 1 2 3 4 5 6</description>
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    <item>
      <title>D2L--第三章线性神经网络</title>
      <link>https://lishensuo.github.io/posts/bioinfo/706d2l-%E7%AC%AC%E4%B8%89%E7%AB%A0%E7%BA%BF%E6%80%A7%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/</link>
      <pubDate>Sun, 21 Jul 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/706d2l-%E7%AC%AC%E4%B8%89%E7%AB%A0%E7%BA%BF%E6%80%A7%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/</guid>
      <description>1. 线性回归 1.1 线性回归的基本元素 线性模型：目标(y)可以表示为输入特征的加权和，参数包括权重向量w和偏置b 损失函数：表示目标的实际值与预测值之</description>
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    <item>
      <title>D2L--第四章多层感知机</title>
      <link>https://lishensuo.github.io/posts/bioinfo/707d2l-%E7%AC%AC%E5%9B%9B%E7%AB%A0%E5%A4%9A%E5%B1%82%E6%84%9F%E7%9F%A5%E6%9C%BA/</link>
      <pubDate>Sun, 28 Jul 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/707d2l-%E7%AC%AC%E5%9B%9B%E7%AB%A0%E5%A4%9A%E5%B1%82%E6%84%9F%E7%9F%A5%E6%9C%BA/</guid>
      <description>1. 多层感知机 1.1 隐藏层 之前所学的线性模型意味着单调假设，并不适用于更复杂的建模问题，例如体温与疾病；图片某个像素点的强度与猫或狗的关系等； 多层</description>
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    <item>
      <title>D2L--第五章深度学习计算</title>
      <link>https://lishensuo.github.io/posts/bioinfo/708d2l-%E7%AC%AC%E4%BA%94%E7%AB%A0%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%AE%A1%E7%AE%97/</link>
      <pubDate>Sun, 28 Jul 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/708d2l-%E7%AC%AC%E4%BA%94%E7%AB%A0%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E8%AE%A1%E7%AE%97/</guid>
      <description>1. 层和块 1.1 自定义块 块/模块（block）可以描述单个层、由多个层（lay）组成的组件或整个神经网络模型本身。 复杂的模块也可以由简单的模块组成</description>
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    <item>
      <title>D2L--第六章卷积神经网络</title>
      <link>https://lishensuo.github.io/posts/bioinfo/709d2l-%E7%AC%AC%E5%85%AD%E7%AB%A0%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/</link>
      <pubDate>Sun, 04 Aug 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/709d2l-%E7%AC%AC%E5%85%AD%E7%AB%A0%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/</guid>
      <description>1. 从全连接层到卷积 1.1 不变性 假设一个场景：需要制作一个检测器，在一张图片中检测一种特定物体。需要满足两个性质： 平移不变性：无论该物品在图片的哪</description>
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    <item>
      <title>D2L--第七章现代卷积神经网络</title>
      <link>https://lishensuo.github.io/posts/bioinfo/710d2l-%E7%AC%AC%E4%B8%83%E7%AB%A0%E7%8E%B0%E4%BB%A3%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/</link>
      <pubDate>Sun, 04 Aug 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/710d2l-%E7%AC%AC%E4%B8%83%E7%AB%A0%E7%8E%B0%E4%BB%A3%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/</guid>
      <description>1. 深度卷积神经网络(AlexNet) 1.1 学习表征 LeNet提出后，卷积神经网络并未占据主流，而是往往由其它机器学习方法所超越，如SVM。一个主</description>
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    <item>
      <title>D2L--第八章循环神经网络</title>
      <link>https://lishensuo.github.io/posts/bioinfo/711d2l-%E7%AC%AC%E5%85%AB%E7%AB%A0%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/</link>
      <pubDate>Sun, 11 Aug 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/711d2l-%E7%AC%AC%E5%85%AB%E7%AB%A0%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/</guid>
      <description>1. 序列模型 1.1 自回归模型 （1）自回归模型：对于一个包含T个&amp;rsquo;时间&amp;rsquo;节点的输入序列，若预测其中的第t个数据，则依赖于该节</description>
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    <item>
      <title>D2L--第九章现代循环神经网络</title>
      <link>https://lishensuo.github.io/posts/bioinfo/712d2l-%E7%AC%AC%E4%B9%9D%E7%AB%A0%E7%8E%B0%E4%BB%A3%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/</link>
      <pubDate>Sun, 11 Aug 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/712d2l-%E7%AC%AC%E4%B9%9D%E7%AB%A0%E7%8E%B0%E4%BB%A3%E5%BE%AA%E7%8E%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/</guid>
      <description>1. 门控循环单元(GRU) 传统的RNN在处理长序列时会遇到梯度消失或梯度爆炸的问题。为了解决这些问题，引入了门控机制的变种，如长短时记忆网络（</description>
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      <title>D2L--第十章注意力机制与Transformer</title>
      <link>https://lishensuo.github.io/posts/bioinfo/713d2l-%E7%AC%AC%E5%8D%81%E7%AB%A0%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6/</link>
      <pubDate>Sat, 17 Aug 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/713d2l-%E7%AC%AC%E5%8D%81%E7%AB%A0%E6%B3%A8%E6%84%8F%E5%8A%9B%E6%9C%BA%E5%88%B6/</guid>
      <description>1. 注意力提示 1.1 生物学的注意力提示 如下的观察实验： 受试者的注意力往往首先被颜色鲜艳的红色咖啡杯吸引（非自主性）； 客观存在的，对于观察者的吸引特</description>
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    <item>
      <title>D2L--第十四及十五章BERT模型</title>
      <link>https://lishensuo.github.io/posts/bioinfo/714d2l-%E7%AC%AC%E5%8D%81%E5%9B%9B%E5%8F%8A%E5%8D%81%E4%BA%94%E7%AB%A0bert%E6%A8%A1%E5%9E%8B/</link>
      <pubDate>Sat, 17 Aug 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/714d2l-%E7%AC%AC%E5%8D%81%E5%9B%9B%E5%8F%8A%E5%8D%81%E4%BA%94%E7%AB%A0bert%E6%A8%A1%E5%9E%8B/</guid>
      <description>1. 基础介绍 BERT（来自Transformers的双向编码器表示）基于Transformer编码器进行预训练(Pre-train)，从而对输</description>
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      <title>D2L--第十一及十二章优化算法&amp;多GPU并行</title>
      <link>https://lishensuo.github.io/posts/bioinfo/715d2l-%E7%AC%AC%E5%8D%81%E4%B8%80%E5%8F%8A%E5%8D%81%E4%BA%8C%E7%AB%A0%E4%BC%98%E5%8C%96%E7%AE%97%E6%B3%95%E5%A4%9Agpu%E5%B9%B6%E8%A1%8C/</link>
      <pubDate>Sat, 24 Aug 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/715d2l-%E7%AC%AC%E5%8D%81%E4%B8%80%E5%8F%8A%E5%8D%81%E4%BA%8C%E7%AB%A0%E4%BC%98%E5%8C%96%E7%AE%97%E6%B3%95%E5%A4%9Agpu%E5%B9%B6%E8%A1%8C/</guid>
      <description>在深度学习中，优化算法是训练模型的关键部分，它们用于更新网络的参数以最小化损失函数。 由于优化算法的目标函数通常是基于训练数据集的损失函数，因</description>
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    <item>
      <title>深度学习--GAN基础</title>
      <link>https://lishensuo.github.io/posts/bioinfo/716%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0--gan%E5%9F%BA%E7%A1%80/</link>
      <pubDate>Tue, 03 Sep 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/716%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0--gan%E5%9F%BA%E7%A1%80/</guid>
      <description>2014年论文：https://proceedings.neurips.cc/paper_files/paper/2014/file/5ca</description>
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    <item>
      <title>Hugging Face(1)-NLP basic</title>
      <link>https://lishensuo.github.io/posts/bioinfo/717hugging-face1-nlp-basic/</link>
      <pubDate>Wed, 16 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/717hugging-face1-nlp-basic/</guid>
      <description>Hugging Face是一家专注于自然语言处理（NLP）和人工智能（AI）的公司，可以认为是AI领域的Github。(下面简称HF) 一方面整理、收集了N</description>
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    <item>
      <title>Hugging Face(3)-Tokenizer库</title>
      <link>https://lishensuo.github.io/posts/bioinfo/718hugging-face3-tokenizers%E5%BA%93/</link>
      <pubDate>Wed, 16 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/718hugging-face3-tokenizers%E5%BA%93/</guid>
      <description>https://huggingface.co/docs/tokenizers/quicktour tokenizer库的处理流程一般包括如下四步—— Normalizers: 文本句预处理，使规范化 Pre-tokenizers: 将句子初步拆分为单元词 Model：得到最终的分词token结果</description>
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    <item>
      <title>Hugging Face(2)-Datasets库</title>
      <link>https://lishensuo.github.io/posts/bioinfo/719hugging-face2-datasets%E5%BA%93/</link>
      <pubDate>Wed, 16 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/719hugging-face2-datasets%E5%BA%93/</guid>
      <description>https://huggingface.co/docs/datasets/index A Dataset provides fast random access to the rows, and memory-mapping so that loading even large datasets only uses a relatively small amount of device memory. But for really, really big datasets ( &amp;gt; 100G) that won’t even fit on disk or in memory, an IterableDataset allows you to access and use the dataset without waiting for it to download completely! https://huggingface.co/learn/nlp-course/chapter5/4?fw=pt#streaming-datasets https://huggingface.co/docs/datasets/about_mapstyle_vs_iterable 1. 读</description>
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    <item>
      <title>Torchtext构建Vocab词表</title>
      <link>https://lishensuo.github.io/posts/bioinfo/720torchtext%E6%9E%84%E5%BB%BAvocab%E8%AF%8D%E8%A1%A8/</link>
      <pubDate>Wed, 16 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/720torchtext%E6%9E%84%E5%BB%BAvocab%E8%AF%8D%E8%A1%A8/</guid>
      <description>torchtext.vocab 1 from torchtext.vocab import vocab 1. 定义词汇表 基于词元的频率统计表，OrderedDict 对象 1 2 3 4 vocab(ordered_dict = , #一个 OrderedDict 对象，包含词汇和它们的频率。 min_freq = 1, #指定词汇表</description>
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    <item>
      <title>Pytorch-Dataset与Dataloader的使用.</title>
      <link>https://lishensuo.github.io/posts/bioinfo/721pytorch-dataset%E4%B8%8Edataloader%E7%9A%84%E4%BD%BF%E7%94%A8/</link>
      <pubDate>Wed, 16 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/721pytorch-dataset%E4%B8%8Edataloader%E7%9A%84%E4%BD%BF%E7%94%A8/</guid>
      <description>在 PyTorch 中，Dataset、DataLoader 和 Sampler 是用于数据加载和处理的核心组件。它们相互配合，使得数据的加载和批处理更加高效和灵活。 Dataset 是一个</description>
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    <item>
      <title>Pytorch-常用神经网络层-torch.nn</title>
      <link>https://lishensuo.github.io/posts/bioinfo/722pytorch-%E5%B8%B8%E7%94%A8%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%B1%82/</link>
      <pubDate>Wed, 16 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/722pytorch-%E5%B8%B8%E7%94%A8%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%B1%82/</guid>
      <description>1 2 import torch import torch.nn as nn 1. 线性层 1 2 3 4 5 input = torch.randn(3, 4) #3个样本，每个样本4个特征 linear = nn.Linear(in_features=4, out_features=2) linear(input).shape # torch.Size([3, 2]) 2. 正则化层 2.1 dropout 1 2 3 4 5 6 7 dropout = nn.Dropout(p=0.5) input = torch.randn(3, 4) dropout(input) # tensor([[ 0.0000, -0.0000, -0.5670,</description>
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    <item>
      <title>GPU,CUDA以及Pytorch之间的关系</title>
      <link>https://lishensuo.github.io/posts/bioinfo/723gpucuda%E4%BB%A5%E5%8F%8Apytorch%E4%B9%8B%E9%97%B4%E7%9A%84%E5%85%B3%E7%B3%BB/</link>
      <pubDate>Mon, 28 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/723gpucuda%E4%BB%A5%E5%8F%8Apytorch%E4%B9%8B%E9%97%B4%E7%9A%84%E5%85%B3%E7%B3%BB/</guid>
      <description>https://pytorch.org/get-started/locally/ 1. 概念 GPU（图形处理单元）是执行并行计算的硬件。 具有不同的型号，例如GeForce RTX 3080、Tesla V100等 https://www.topcpu.net/gpu-r/fp32-float CUDA是NVIDI</description>
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    <item>
      <title>深度学习常规Config训练配置</title>
      <link>https://lishensuo.github.io/posts/bioinfo/724%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%B8%B8%E8%A7%84config%E8%AE%AD%E7%BB%83%E9%85%8D%E7%BD%AE/</link>
      <pubDate>Mon, 28 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/724%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%B8%B8%E8%A7%84config%E8%AE%AD%E7%BB%83%E9%85%8D%E7%BD%AE/</guid>
      <description>1. 优化器optimizer Adam：自适应学习率优化器 1 optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) AdamW：Adam 的变体，加入了权重衰减来改善正则化效果，在 Transformer 类模型中表现</description>
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    <item>
      <title>Flash Attention注意力优化</title>
      <link>https://lishensuo.github.io/posts/bioinfo/725flash-attention%E6%B3%A8%E6%84%8F%E5%8A%9B%E4%BC%98%E5%8C%96/</link>
      <pubDate>Mon, 28 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/725flash-attention%E6%B3%A8%E6%84%8F%E5%8A%9B%E4%BC%98%E5%8C%96/</guid>
      <description>注意力计算 注意力计算的三要素分别是：Query， Key，Value。而在自注意力计算中，三者则是等价的。 结合如下图示例：一个序列有2个词元，</description>
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    <item>
      <title>机器学习日志指标log记录</title>
      <link>https://lishensuo.github.io/posts/bioinfo/726%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E6%97%A5%E5%BF%97%E6%8C%87%E6%A0%87log%E8%AE%B0%E5%BD%95/</link>
      <pubDate>Mon, 28 Oct 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/726%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E6%97%A5%E5%BF%97%E6%8C%87%E6%A0%87log%E8%AE%B0%E5%BD%95/</guid>
      <description>在深度/机器学习模型训练时，有必要展示或者记录每个batch/epoch的各种损失以及精度信息。除了最简单的print方式，目前有多种库提供</description>
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    <item>
      <title>torch张量的维度操作</title>
      <link>https://lishensuo.github.io/posts/bioinfo/727torch%E5%BC%A0%E9%87%8F%E7%9A%84%E7%BB%B4%E5%BA%A6%E6%93%8D%E4%BD%9C/</link>
      <pubDate>Tue, 05 Nov 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/727torch%E5%BC%A0%E9%87%8F%E7%9A%84%E7%BB%B4%E5%BA%A6%E6%93%8D%E4%BD%9C/</guid>
      <description>在深度学习的前向传播中，最重要的是理解每个计算步骤的输入前与输入后的维度形状。与之对应的时需要熟悉一些常见的维度操作方法，根据项目的学习总结</description>
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    <item>
      <title>单机多卡torchrun分布式训练</title>
      <link>https://lishensuo.github.io/posts/bioinfo/728%E5%8D%95%E6%9C%BA%E5%A4%9A%E5%8D%A1torchrun%E5%88%86%E5%B8%83%E5%BC%8F%E8%AE%AD%E7%BB%83/</link>
      <pubDate>Sat, 09 Nov 2024 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/728%E5%8D%95%E6%9C%BA%E5%A4%9A%E5%8D%A1torchrun%E5%88%86%E5%B8%83%E5%BC%8F%E8%AE%AD%E7%BB%83/</guid>
      <description>1. 背景 DDP分布式训练与DP并行训练 在之前了解多GPU训练时，学习过一种数据并行方式DataParallel (DP)。其核心将模型复制到每个</description>
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      <title>torch模型组成模块参数查询、管理、保存</title>
      <link>https://lishensuo.github.io/posts/bioinfo/729torch%E6%A8%A1%E5%9E%8B%E7%BB%84%E6%88%90%E6%A8%A1%E5%9D%97%E5%8F%82%E6%95%B0%E6%9F%A5%E8%AF%A2%E7%AE%A1%E7%90%86%E4%BF%9D%E5%AD%98/</link>
      <pubDate>Sat, 14 Dec 2024 00:00:00 +0000</pubDate>
      
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      <description>1. 示例模型 两层MLP的神经网络 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 import torch import torch.nn as nn import torch.nn.functional as F class SimpleMLP(nn.Module): def __init__(self, input_size, hidden_size, output_size): super(SimpleMLP, self).__init__() self.fc1 = nn.Linear(input_size, hidden_size) self.fc2 = nn.Linear(hidden_size, output_size) def forward(self, x): x = F.relu(self.fc1(x)) x</description>
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      <title>Hugging face(4) Bert模型及Collator与Trainer</title>
      <link>https://lishensuo.github.io/posts/bioinfo/730hugging-face4-bert%E6%A8%A1%E5%9E%8B%E5%8F%8Acollator%E4%B8%8Etrainer/</link>
      <pubDate>Fri, 24 Jan 2025 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/730hugging-face4-bert%E6%A8%A1%E5%9E%8B%E5%8F%8Acollator%E4%B8%8Etrainer/</guid>
      <description>1. Collator数据处理 目的：将dataset的初始数据进行规范化批量处理，用以后续的前向计算 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22</description>
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      <title>图神经网络DGL-01-DGL基础</title>
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      <pubDate>Sun, 31 Jul 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/801%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9Cdgl-01dgl%E5%9F%BA%E7%A1%80/</guid>
      <description>homogeneous graph ： 一种节点，一种边 heterogeneous graph: 多种节点，多种边 bipartite graph : 两种节点，一种边 1、DGLgraph结构 1.1 同构图 Cora论文关系图&amp;ndash;同构图(ho</description>
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      <title>图神经网络DGL-02同构图_节点分类</title>
      <link>https://lishensuo.github.io/posts/bioinfo/802%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9Cdgl-02%E5%90%8C%E6%9E%84%E5%9B%BE_%E8%8A%82%E7%82%B9%E5%88%86%E7%B1%BB/</link>
      <pubDate>Sun, 28 Aug 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/802%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9Cdgl-02%E5%90%8C%E6%9E%84%E5%9B%BE_%E8%8A%82%E7%82%B9%E5%88%86%E7%B1%BB/</guid>
      <description>1 2 3 4 5 6 7 8 import dgl import dgl.nn as dglnn import torch import torch.nn as nn import torch.nn.functional as F import numpy as np 0、预测任务与数据 预测论文属于哪一种类别，即为多分类问题 1 2 3 4 5 6 7 8 9 10 11 12 13 14</description>
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    <item>
      <title>图神经网络DGL-03同构图_边回归</title>
      <link>https://lishensuo.github.io/posts/bioinfo/803%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9Cdgl-03%E5%90%8C%E6%9E%84%E5%9B%BE_%E8%BE%B9%E5%9B%9E%E5%BD%92/</link>
      <pubDate>Sun, 28 Aug 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/803%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9Cdgl-03%E5%90%8C%E6%9E%84%E5%9B%BE_%E8%BE%B9%E5%9B%9E%E5%BD%92/</guid>
      <description>（1）边回归问题，对图中已存在的边的定量(回归)/定性(分类)，结合训练得到的边的embedding做GNN神经网络预测。 （2）边的embe</description>
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    <item>
      <title>图神经网络DGL-03同构图_边预测</title>
      <link>https://lishensuo.github.io/posts/bioinfo/804%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9Cdgl-04%E5%90%8C%E6%9E%84%E5%9B%BE_%E8%BE%B9%E9%A2%84%E6%B5%8B/</link>
      <pubDate>Sun, 28 Aug 2022 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/posts/bioinfo/804%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9Cdgl-04%E5%90%8C%E6%9E%84%E5%9B%BE_%E8%BE%B9%E9%A2%84%E6%B5%8B/</guid>
      <description>边的预测问题即预测两个节点间是否可能存在边，可以视为二分类问题。 在训练时，首先同样要先更新节点信息，然后计算边的特征&amp;mdash; 将图中已知</description>
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      <title></title>
      <link>https://lishensuo.github.io/about/</link>
      <pubDate>Sat, 08 Jan 2022 23:33:37 +0800</pubDate>
      
      <guid>https://lishensuo.github.io/about/</guid>
      <description>李申锁，男，在读研究生，学习生信ing 此前以小贝学生信为昵称在简书平台写一些学习心得。于2022年4月10日创建此博客，记录今后学习生信的过</description>
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      <title></title>
      <link>https://lishensuo.github.io/search.fr/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      
      <guid>https://lishensuo.github.io/search.fr/</guid>
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