spectraCounteBayes {DEqMS} | R Documentation |
This function is to calculate peptide/PSM count adjusted t-statistics, p-values.
spectraCounteBayes(fit, fit.method="loess", coef_col)
fit |
an list object produced by Limma eBayes function, it should have one additional attribute named count, which stored the peptide or PSM count quantified for the gene in label-free or isobaric labelled data |
fit.method |
the method used to fit prior variance against the number of peptides. Two available methods: "loess" and "nls". default "loess". |
coef_col |
an integer vector indicating the column(s) of fit$coefficients for which the function is to be performed. if not specified, all contrasts are calculated |
This function adjusts the T-statistics and p-values for quantitative MS proteomics experiment according to the number of peptides/PSMs used for quantification. The method is similar in nature to intensity-based Bayes method (Maureen A. Sartor et al BMC Bioinformatics 2006).
a list object with the additional attributes being: sca.t - Spectra Count Adjusted posterior t-value sca.p - Spectra Count Adjusted posterior p-value sca.dfprior - Spectra Count Adjusted prior degrees of freedom sca.priorvar- Spectra Count Adjusted estimated prior variance sca.postvar - Spectra Count Adjusted posterior variance loess.model - fitted loess model
Yafeng Zhu
library(ExperimentHub) eh = ExperimentHub() query(eh, "DEqMS") dat.psm = eh[["EH1663"]] dat.psm.log = dat.psm dat.psm.log[,3:12] = log2(dat.psm[,3:12]) dat.gene.nm = medianSweeping(dat.psm.log,group_col = 2) psm.count.table = as.data.frame(table(dat.psm$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)