Section 10 Process Djuric et al. 2017 data

The data coming from Djuric et al. are not annotated with differential expression data. We can perform differential expression using the functions limma.diff.expr from the proteomicstools in-house package.

djuric <- read.csv("../Data/Dataset/original/Djuric_et_al.csv",header = T)

#limma diff expr
iPS <- c(4:6)
NPC <- c(7:9)
Neu <- c(10:12)

djuric <- limma.diff.expr(djuric,iPS,NPC,c("iPS","NPC"))

## Warning: Partial NA coefficients for 387 probe(s)

## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

djuric <- limma.diff.expr(djuric,iPS,Neu,c("iPS","Neu"))

## Warning: Partial NA coefficients for 494 probe(s)

## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

djuric <- limma.diff.expr(djuric,NPC,Neu,c("NPC","Neu"))

## Warning: Partial NA coefficients for 431 probe(s)

## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

djuric$condition1 <- "NPC.iPS"
djuric$condition2 <- "Neu.iPS"
djuric$condition3 <- "Neu.NPC"

Now we save the processed file

write.csv(djuric,"../Data/Dataset/processed/Djuric_et_al_2017_processed.csv",row.names = F)