9 Oct 09:42 2012

## Data filtering

Greetings friends! I seek help with data that I have : 3 time points, 3 genotypes, 3 replicates for each of these = 27 libraries The goal is to find genes that have different time expression profiles amongst 2 or more genotypes. After our 1st round of data analysis, (including TMM normalization), the time course graphs and box plots were so noisy in terms of high std error at each time point, that it was hard to say if expression profile of one genotype was overlapping or distinct from that for the other genotypes! R code attached at bottom of this post. So in short - we now need to employ data filters to check and reduce noise in our data. Some ideas are removing genes that have low expression (count) levels removing genes that have high variance across replicates removing genes that have low variance across time (constitutively expressed genes are biologically less interesting) So my question to you is what stage of my analysis do I employ these filters? On the raw data itself, prior to normalization? Or should I perform the TMM normalization, use the norm factors to transform my data to non-integer normalized counts and then filter (in which case I think I cannot fit them into negative binomial model, right?) <CODE> count = read.table("Input.txt", sep="\t", header=T) #$#$ read in raw count mapped data f.count = count[apply(count[,-c(1,ncol(count))],1,sum) > 27,] #$#$ filter ou genes with total read count < 27 across all libraries f.dat = f.count[,-c(1,ncol(count))] #$#$ select only read count, not rest of data frame S = factor(rep(c("gen1","gen2","gen3"),rep(9,3))) #$#$ define group Time = factor(rep(rep(c("0","10","20"),rep(3,3)),3)) #$#$ define time Time.rep = rep(1:3,9) #$#$ define replicate Group = paste(S,Time,Time.rep,sep="_") #$#$ define group_time_replicate library(edgeR) #$#$ load edgeR package f.factor = data.frame(files = names(f.dat), S = S , Time = Time, lib.size = c(apply(f.dat,2,sum)),norm.factors = calcNormFactors(as.matrix(f.dat))) #$#$ make data for edgeR method count.d = new("DGEList", list(samples = f.factor, counts = as.matrix(f.dat))) #$#$ make data for edgeR method design = model.matrix(~ Time + S) #$#$ make design data for edgeR method count.d = calcNormFactors(count.d) #$#$ Normalize TMM glmfit.d = glmFit(count.d, design, dispersion = 0.1) #$#$ Fit the Negative Binomial Gen Lin Models lrt.count = glmLRT(count.d, glmfit.d) #$#$ Likelihood ratio tests result.count = data.frame(f.count, lrt.count$table) #$#$ combining raw data and results from edgeR result.count$FDR = p.adjust(result.count$p.value,method="BH") #$#$ calculating the False Discovery Rate write.table(result.count, "edgeR.Medicago_count_WT_Mu3.txt",sep="\t",row.names=F) #$#$ saving the combined data set </CODE> -- output of sessionInfo(): . -- Sent via the guest posting facility at bioconductor.org. _______________________________________________ Bioconductor mailing list Bioconductor@... https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor