mCIRCrna

The goal is to integrate circadian transcriptomes with muscle snRNA-seq data to identify age and cell type dependent circadian gene signatures that demonstrate tissue chronicity in muscle.

Table of Contents

• mCIRCrna
  • Background
  • Data
  • Usage
    • Installation
    • Requirements Can be named Dependencies as well
    • Activate conda environment Optional
    • Steps to run Optional depending on project
      • Step-1
      • Step-2
  • Results Optional depending on project
  • Team Members

Background

Goal is to identify circadian gene signatures that demonstrate tissue chronicity (2 fold expression changes over time) compared to age and cell type in muscle.

Data

CircAge: https://circaage.shinyapps.io/circaage/ MyoAtlas: https://research.cchmc.org/myoatlas/

Usage

❗ How will someone not involved in your project be able to run the code or use it. ❗

Installation

❗ If installation is required, please mention how to do so here. ❗

Installation simply requires fetching the source code. Following are required:

• Git

To fetch source code, change in to directory of your choice and run:

git clone -b main \
    [email protected]:u-brite/team-repo-template.git

Requirements

❗ Note any software used (including Python or R packages), operating system requirements, etc. and its version so that your project is reproducible. It does not have to be in the below format ❗

OS:

Currently works only in Linux OS. Docker versions may need to be explored later to make it useable in Mac (and potentially Windows).

Tools:

• Anaconda3
  • Tested with version: 2020.02

Activate conda environment

❗ Optional: Depends on project. ❗

Change in to root directory and run the commands below:

# create conda environment. Needed only the first time.
conda env create --file configs/environment.yaml

# if you need to update existing environment
conda env update --file configs/environment.yaml

# activate conda environment
conda activate testing

Steps to run

❗ Optional: Depends on project. ❗

Step 1

python src/data_prep.py -i path/to/file.tsv -O path/to/output_directory

Step 2

python src/model.py -i path/to/parsed_file.tsv -O path/to/output_directory

Output from this step includes -

output_directory/
├── parsed_file.tsv               <--- used for model
├── plot.pdf- Plot to visualize data
└── columns.csv - columns before and after filtering step

Note: The is an example note with a link.

Results

❗ If your project yielded or intends to yield some novel analysis, please include them in your readme. It can be named something other than results as well. ❗

Team Members

Shufan Zhang | [email protected] | Team Leader