VarianceScatterplot: generate a scatter plot of the variance

In DEqMS: a tool to perform statistical analysis of differential protein expression for quantitative proteomics data.

Description

This function is to draw a scatter plot of the variance against the number of quantified peptides/PSMs.Red curve indicate DEqMS prior variance.

Usage

VarianceScatterplot(fit, xlab="log2(count)",
ylab = "log(Variance)", main="")

Arguments

fit	an object returned from spectraCounteBayes function
xlab	the title for x axis
ylab	the title for y axis
main	the title for the figure

Value

return a plot graphic

Author(s)

Yafeng Zhu

Examples

library(ExperimentHub)
eh = ExperimentHub(localHub=TRUE)
query(eh, "DEqMS")
dat.psm = eh[["EH1663"]]

dat.psm.log = dat.psm
dat.psm.log[,3:12] =  log2(dat.psm[,3:12])

dat.gene.nm = medianSweeping(dat.psm.log,group_col = 2)
    
psm.count.table = as.data.frame(table(dat.psm$gene)) # generate PSM count table
rownames(psm.count.table)=psm.count.table$Var1
    
cond = c("ctrl","miR191","miR372","miR519","ctrl",
"miR372","miR519","ctrl","miR191","miR372")

sampleTable <- data.frame(
row.names = colnames(dat.psm)[3:12],
cond = as.factor(cond)
)
    
gene.matrix = as.matrix(dat.gene.nm)
design = model.matrix(~cond,sampleTable)

fit1 <- eBayes(lmFit(gene.matrix,design))
# add PSM count for each gene
fit1$count <- psm.count.table[rownames(fit1$coefficients),2]  

fit2 = spectraCounteBayes(fit1)
    
VarianceScatterplot(fit2,xlab="log2(PSM count)",main="TMT data PXD004163")

DEqMS documentation built on Nov. 8, 2020, 7:51 p.m.