Hello everyone! I'm using edgeR to detect DE genes on my data. I have 2 control samples and 4 mutated samples. I understand why I should filter them and I know the command to use (the tutorials Drs. Mark Robinson, Davs McCarthy, Yunshun Chen and Gordon K.Smyth made are pretty self explanatory in everything). What I don't understand however is the filtering criteria. I named my DGE object as "d", so the command I'm using is: d <- d[rowSums(1e+06 * d$counts/expandAsMatrix(d$samples$lib.size, dim(d)) > 1) >= ?, ] Meaning that I'm filtering out genes that don't have at least one count per million on "?" samples. What value should I use for "?" given that I have 2 control and 4 mutated samples. Thank you in advance for your help! C [[alternative HTML version deleted]]

Hi Catarina, Comments in line: On Mon, Jul 22, 2013 at 8:07 AM, Catarina Almeida <catarina.fa at="" gmail.com=""> wrote: > Hello everyone! > > I'm using edgeR to detect DE genes on my data. I have 2 control samples and > 4 mutated samples. > I understand why I should filter them and I know the command to use (the > tutorials Drs. Mark Robinson, Davs McCarthy, Yunshun Chen and Gordon > K.Smyth made are pretty self explanatory in everything). What I don't > understand however is the filtering criteria. > > I named my DGE object as "d", so the command I'm using is: > d <- d[rowSums(1e+06 * d$counts/expandAsMatrix(d$samples$lib.size, dim(d)) >> 1) >= ?, ] Perhaps you'd like to simplify that to the more intuitive: d <- d[rowSums(cpm(d) >= 1) >= ?, ] > Meaning that I'm filtering out genes that don't have at least one count per > million on "?" samples. What value should I use for "?" given that I have 2 > control and 4 mutated samples. I believe the rule of thumb (if there is one) with this strategy would be to use the number that is the minimum of the number of samples for the conditions you have replicates in, so since you have one condition with 2 replicates and another with 4, you'd pick 2. HTH, -steve -- Steve Lianoglou Computational Biologist Bioinformatics and Computational Biology Genentech