Dear List, I'm starting to do limma analyses on a small timecourse loop design with 2-color cDNA chips as follows: 0h vs 6h 6h vs 24h 24h vs 0h Four biological replicates -> and then four biological replicates dye balanced <- My targets file begins like this (only the first two sets of three listed): US22502600_F82_S01.gpr A_0h A_24h US22502600_F65_S01.gpr A_24h A_6h US22502600_F153_S01.gpr A_6h A_0h US22502600_F85_S01.gpr F_0h F_6h US22502600_F60_S01.gpr F_24h F_0h US22502600_F72_S01.gpr F_6h F_24h ... with eight such sets of three. But then I also have some chips -> against our labs "standard" reference RNA: US22502600_F67_S01.gpr A_24h Ref US22502600_F83_S01.gpr F_24h Ref ... and six more For my limma analysis, I have three options: *a*: use only the minimal number of chips (ie each loop of three, and nothing to connect the loops). In this case, limma is unable to estimate one parameter in each small loop (eg the 6h timepoint). I can ask how many genes are differentially expressed between 24h and 0h: >design.noref = modelMatrix(targets.noref, ref="A_0h") >fit.noref = lmFit(MA.noref.p, design.noref) >cont.matrix= makeContrasts(T24_T0 = (A_24h+C_24h+F_24h+K_24h+N_24h +Q_24h+R_24h+T_24h -C_0h-F_0h-K_0h-N_0h-Q_0h-R_0h-T_0h)/8, levels=design.noref) >fit.noref2= contrasts.fit(fit.noref, cont.matrix) >fit.noref2=eBayes(fit.noref2) >summary(topTable(fit.noref2,n=10000)$adj.P.Val<=0.05) ---> I get 3668 differentially expressed spots. *b*: provide my "24h" vs Ref chips as well using ref="Ref" in my design and > cont.matrix= makeContrasts(T24_T0 = (A_24h+C_24h+F_24h+K_24h+N_24h +Q_24h+R_24h+T_24h -A_0h-C_0h-F_0h-K_0h-N_0h-Q_0h-R_0h-T_0h)/8, levels=design) ---> I get 3796 differentially expressed spots. *c*: use those in *b*, as well as eight additional chips done in parallel, that are XXX vs Ref. The XXX samples don't connect to anything other than Ref (they're superfluous). ---> I get 3583 differentially expressed spots. Searching the archives, several posts mentioned that providing more chips gives limma a better estimation of variance. Thus it makes sense to provide more. And doing so finds more differentially expressed genes in *b* than in *a*. But so would it be defendable to input all the chips I did in that batch to limma? All the chips I've ever done? And then I get a smaller number of differentially expressed spots in *c* than in *b*. Which surprises me, because using more chips should make my estimation of variance more precise. Comparing *b* with *c* leads me to conclude that the chips I've added to the analysis in *c* are funky because they increase estimates of variance, or that the chips in *b* show artificially low variance. Does this make sense? Obviously, in this analysis my numbers of differentially expressed genes are quite similar in these three cases, and 5% more or less significant spots probably won't make a difference. But it would be good to know what is most valid for future analyses as well. Thanks and regards, yannick -------------------------------------------- yannick . wurm @ unil . ch Ant Genomics, Ecology & Evolution @ Lausanne http://www.unil.ch/dee/page28685_fr.html