ksea_vis.Rmd

---
title: "Compare KSEA Results"
author: "Sara Gosline"
date: "3/18/2022"
output: html_document
---

Given the KSEA results, we want to visualize the kinases showing up in every genetic subtypes.

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Pull code

```{r pull code and get libraries}

library(dplyr)

library(amlresistancenetworks)
library(dplyr)
library(tidyr)
library(ggplot2)
syn = synapseLogin()

tab <- read.table(syn$get('syn27852574')$path,sep='\t',header=T)

tab%>%subset(adj_p_val<0.05)%>%
  ggplot(aes(x=pathway,y=enrichment,fill=mutation))+geom_bar(stat='identity')
        
ggsave('sigKinEnrichment0.05.pdf')


```

We dont have that many kinases enriched. Let's look at the peptides that are changing!!!

```{r peptides}

ptab<- read.table(syn$get('syn27842983')$path,header=T,sep='\t')

##first let's visualize all the peptides that are coming up
subset(ptab,adj_pval<0.05)%>%
  mutate(log10Pval=-log10(adj_pval))%>%
    ggplot(aes(x=reorder(feature,log10Pval),y=log10Pval,col=mutation))+
    geom_point(aes(alpha=0.5))+ 
                  theme(axis.text.x=element_blank())

ggsave('allPecoraResults.pdf')

##now let's plot specific proteins
ptab%>%subset(adj_pval<10e-8)%>%
  ggplot(aes(x=Protein,fill=mutation,y=n_peptides))+geom_bar(stat='identity',position='dodge')+
   theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

ggsave('sigPecoraResults.pdf')

```