# Moderated Pair-Wise t-test From Limma This function runs moderated t-test from package `limma` on each line of data. Note: This function requires the package [limma](https://bioconductor.org/packages/release/bioc/html/limma.html) from bioconductor being installed. The `limma` contrast-matrix has to be read by column, the lines in the contrast-matrix containing '+1' will be compared to the '-1' lines, eg grpA-grpB . Local false discovery rates (lfdr) estimations will be made using the CRAN-package [fdrtool](https://CRAN.R-project.org/package=fdrtool) (if available). ```r moderTest2grp( dat, grp, limmaOutput = TRUE, addResults = c("lfdr", "FDR", "Mval", "means"), testOrientation = "=", silent = FALSE, debug = FALSE, callFrom = NULL ) ``` ## Arguments - `dat`: matrix or data.frame with rows for multiple (independent) tests, use ONLY with 2 groups; assumed as log2-data - `grp`: (factor) describes column-relationship of 'dat' (1st factor is considered as reference -> orientation of M-values !!) - `limmaOutput`: (logical) return full (or extended) MArrayLM-object from limma or 'FALSE' for only the (uncorrected) p.values - `addResults`: (character) types of results to add besides basic limma-output, data are assumed to be log2 ! (eg "lfdr" using fdrtool-package, "FDR" or "BH" for BH-FDR, "BY" for BY-FDR, "bonferroni" for Bonferroni-correction, "qValue" for lfdr by qvalue, "Mval", "means" or "nonMod" for non-moderated test and he equivaent all (other) multiple testing corrections chosen here) - `testOrientation`: (character) for one-sided test (">","greater" or "<","less"), NOTE : 2nd grp is considered control/reference, '<' will identify grp1 < grp2 - `silent`: (logical) suppress messages - `debug`: (logical) additional messages for debugging - `callFrom`: (character) allow easier tracking of messages produced ## Returns This function returns a limma-type object of class `MArrayLM` ## Examples ```r set.seed(2017); t8 <- matrix(round(rnorm(1520,10,0.4),2), ncol=8, dimnames=list(paste("l",1:190),c("AA1","BB1","CC1","DD1","AA2","BB2","CC2","DD2"))) t8[3:6,1:2] <- t8[3:6,1:2] +3 # augment lines 3:6 for AA1&BB1 t8[5:8,5:6] <- t8[5:8,5:6] +3 # augment lines 5:8 for AA2&BB2 (c,d,g,h should be found) t4 <- log2(t8[,1:4]/t8[,5:8]) ## Two-sided testing fit4 <- moderTest2grp(t4,gl(2,2)) # If you have limma installed we can now see further if(requireNamespace("limma")) { limma::topTable(fit4, coef=1, n=5)} # effect for 3,4,7,8 ## One-sided testing fit4in <- moderTest2grp(t4,gl(2,2), testO="<") # If you have limma installed we can now see further if(requireNamespace("limma")) { limma::topTable(fit4in, coef=1, n=5) } ``` ## See Also `lmFit` and the `eBayes`-family of functions in package [limma](https://bioconductor.org/packages/release/bioc/html/limma.html), `p.adjust`

moderTest2grp function