NALM-16_v1.rmd

---
title: "NALM-16 TP53 Exp"
author: "Alex"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: 
html_document:
toc: TRUE
toc_float: FALSE
editor_options: 
  chunk_output_type: console
chunk_output_type: console
---


<style type="text/css">
.main-container {
max-width: 2500px;
margin-left: 5px;
margin-right: auto;
}
.toc-content {
max-width: 2500px;
margin-left: 50px;
margin-right: auto;
}

div {

margin-left: 5px;
}


hr.new1 {
border-top: 1px solid #84a8e0;
}


</style>

version: 1.0 <br />
Run at "`r format(Sys.time())`"

# Data {.tabset}

## Overview/Method:

  * GOAL, Study the role of p53 mutations in the context of hypodiploidy.
  * Cell lines derived from NALM-16. 
  * Hypothesis: Mutant p53 activates transcription and expression of genes being pro-oncogenic. This would indicate a gain of function of the mutant p53. This would then challenge the line of thought that the only effect of mutations on p53 is to be similar to the absence of p53  		


  
  
```{r setup, include=FALSE, echo=FALSE, message=FALSE, warning=FALSE, cache=F}

knitr::opts_chunk$set(include=F, echo=FALSE, message=FALSE, warning=FALSE, fig.show="asis", fig.keep="all",tidy.opts=list(width.cutoff=100),tidy=TRUE)
options(knitr.table.format = "latex")
options(width = 1600)


### global options 
options(scipen=999)
### standard libraries
library("RColorBrewer")
library(dplyr)
library(openxlsx)
library ( parallelDist)
library(forcats)
library ( factoextra)
library ( FactoMineR)
library ( ggplot2)
library ( ggplotify) 
library ( patchwork)
library ( limma)
library ( edgeR)
library(knitr)        
library ( kableExtra)    
library(tidyr)
library ( data.table )
library (DT)
library ( ComplexHeatmap)

library  (regioneR )  
library ( karyoploteR )


library ( car )
library ( CEMiTool ) 
getPalette = colorRampPalette(brewer.pal(9, "Set1")) # expand color pallete
# keeping things consistent
ss = 123
set.seed(ss)



## read in resources 
## 
main.data = "/data/"
resource = "/ehome/resource/"
resource.ext = "/ehome/"


source ( "/ehome/scripts/config.ext/ext_June.2022.R" )

# for info on genes
genecard  <- readRDS(  paste0( resource, "annotation/genecard.rds" )  )
genecard = genecard[ , c("InputTerm","EntrezGene")]
genecard = genecard[!is.na(genecard$EntrezGene), ]
colnames(genecard) = c("GENE","description")

## pathways 
pathways = readRDS( paste0( resource, "/gsea/limma/go_path.rds"))
# change column to make it work with profiler 
pathway.p = pathways
colnames ( pathway.p)[1] = c("geneID")

annt =  readRDS( paste0( resource, "/gsea/limma/go_path_withWeneName.rds"))
annt$GeneID = NULL 

colnames ( annt ) = c ( "gene"  , "term")
annt = annt [ ,  c ( "term"  , "gene") ]
pathgsea =  readRDS( paste0( resource, "/gsea/limma/go_path_fgsea_genename.rds"))


ens = readRDS( paste0( resource, "annotation/gene.detail.rds"))
goanadb = readRDS( paste0( resource, "/gsea/limma/go_path.rds"))

humangene = read.table( paste0(resource, "annotation/humangene.txt"), sep="\t", header=T, stringsAsFactors = FALSE,na.strings=".",  quote = "")

# loading up cancer genes
cosmic = read.csv("https://www.dropbox.com/s/ceo0ol2h9vt01c3/cosmic-cancer_gene_census.v85.csv?dl=1")
tsg = cosmic[ grepl("TSG", cosmic$Role.in.Cancer, ignore.case = T), ]
oncogene = cosmic[ grepl("oncog", cosmic$Role.in.Cancer, ignore.case = T), ]
cosmic$Gene.Symbol = as.character(cosmic$Gene.Symbol)

# load drug database 
drug.db = "https://www.dropbox.com/s/1k60wzvstbp0m7w/7.8.2020.xlsx?dl=1"
drug.db <- read.xlsx(drug.db , colNames = TRUE)
drug.db = drug.db[ grepl ( "antagonist|blocker|antibody|antisense|inhibit", drug.db$interaction_types), ]



# main excel output for later 
wb <- createWorkbook()

out.dir = "./results.out/"
dir.create(out.dir)


```

```{r}

```