here is an Rmd script for performing integrative analysis of proteomic and methylation (array) analysis

mitch4macsue.Rmd

---
title: "Macsue proteomic/methylation mitch analysis"
author: "Mark Ziemann"
date: "`r Sys.Date()`"
output:
  html_document:
    toc: true
theme: cosmo
---

# Intro

Load library and input data.

```{r,begin}

library("mitch")

prot <- read.table("GeneSmart group analyses signif Prot PRE z-score.csv",sep=",",
                   header=TRUE,stringsAsFactors = FALSE)
colnames(prot)[1] <- "gene"
head(prot)
str(prot)

meth <- read.table("signif results z-score with gene names.csv",sep=",",header=TRUE,stringsAsFactors = FALSE)

dim(meth)

methg <- meth$genes
methg_spl <- strsplit(methg,";")
meth <- meth[which(lapply(methg_spl,length)>0),]

head(meth)
dim(meth)

x <- apply(meth,1,function(x) {
  GENES <- as.character(x[length(x)])
  GENES <- unlist(strsplit(GENES,";"))
  z <- lapply(GENES,function(G) {
    y <- x[1:(length(x)-1)] 
    y$Gene <- G
    y
  })
  do.call(rbind,z)
})

meth_fix <- do.call(rbind,x)
meth_fix2 <- as.data.frame(apply(meth_fix[,2:7],2,as.numeric))
meth_fix2$gene <- unlist(meth_fix[,"Gene"])

head(meth_fix2)
dim(meth_fix2)
```

Now import with mitch.

```{r,import}

prot <- prot[,c("gene","t")]
dim(prot)
meth <- meth_fix2[,c("gene","t")]
meth <- aggregate(. ~ gene, meth, sum)

dim(meth)
l <- list("prot"=prot,"meth"=meth)
m <- mitch_import(x = l,geneIDcol = "gene",DEtype = "limma")

```

# Fetch gene sets

```{r,reactome}

download.file("https://reactome.org/download/current/ReactomePathways.gmt.zip", destfile="ReactomePathways.gmt.zip")
unzip("ReactomePathways.gmt.zip")
genesets <- gmt_import("ReactomePathways.gmt")

```

## Calculate enrichment

```{r,enrich}

res <- mitch_calc(x=m,genesets = genesets ,priority = "effect",minsetsize = 5)

head(res$enrichment_result,50)

mitch_report(res = res, outfile = "myreport.html")

```


## Session information

For reproducibility

```{r,session}

sessionInfo()

```