Analysis of genome-scale count data in Bioconductor

Robinson and Oshlack Genome Biology 2010, 11:R25http://genomebiology.com/2010/11/3/R25Poisson replication induces avuvuzela-shaped “MA”-plotRobinson and Oshlack Genome Biology 2http://genomebiology.com/2010/11/3/R2The trimmed mean of M-values normalization methodThe total RNA production, S k ,cannotbeestimateddirectly, since we do not know the expression levels andAnd the theorytrue lengths of every gene. The However, trimmed mean the relative of M-values RNAnThe validates total RNA that production, thisproduction of two samples, f k =S k /S k’ , essentially a globalfold change, can more easily be determined. we do not We know pro-thS kdirectly, behaviour sinceshouldtrue exist: lengths M ispose an empirical strategy that equatesof everythegene.overallHowproduction essentially of a two logrelative-riskfold change, can more easilysamples, f k =expression levels of genes between samples under thebalassumption that the majority of them are not DE. Onepose an empirical strategy thsimple yet robust way to estimateexpressionthelevelsratioofofgenesRNA productionuses a weightedassumption trimmed that mean theof majority the log ofPower (to detect betwexpression ratios (trimmedchanges)simple meanofMvalues(TMM)).is higher atyet robust way to estimatFor sequencing data, we definehigherduction thecountsuses gene-wise a weighted log-foldchangesas:trimImplications forexpression ratios (trimmed meaFor downstream sequencing data, we defineYgk N k changes as:M g / analysis.log 2Ygk / N k Ygk N kM g /log 2and absolute expression levels:Y gk / N k and absolute expression levels:Ag 1 log Ygk Nk YgkNk Yg22’ for 0Ag 1 log Ygk Nk YgkN22To robustly summarize the observed M values, wetrim both the M values and the A values before taking