I now have microarray data from different cell types and ideally the idea was to perform differential gene expression analysis. However, I was wondering if it would be possible to identify cell type specific marker genes for these cell types. Genes that are predominantly expressed in a particular cell type and not in other (of course when performing DE Analysis, one would get this contrast with respect to two cell types.) But in this case, when one has many cell types, my approach would be to contrast all combinations. My question is to find out if there are any direct method that is available to identify a set DEG's that explicitly belong a cell type from a given dataset. My dataset looks this way: Probes celltype1 celltype2 celltype3 celltype4 --------------------------------------------------- gene1 5.098 4.677 7.456 8.564 gene2 8.906 6.653 6.754 7.546 gene3 7.409 5.432 6.724 5.345 gene4 4.876 5.981 4.290 4.267 gene4 3.567 3.425 8.564 6.345 gene5 2.569 8.645 5.234 7.345 The dimension is 22810(genes) x 20(cell types). I tried to use the csSAM package in R. csSamWrapper(G, cc, y, nperms = 200,alternative = "two.sided", standardize = TRUE,medianCenter = TRUE, logRm = FALSE, logBase = 2,nonNeg = TRUE, fileName = "csSAMout.pdf") where G is my expression matrix where I have the genes as rows and cell types as columns. and cc stands for the Matrix of cell-frequency. (n by k, n samples, k cell-types). and y for A numeric vector of group association of each sample. Either 1 or 2. The example from the package had several samples. I am not clear as how to construct the cell frequency matrix because I have only one sample. Are there other packages which can be used to find DEG s from a single cell type without comparison? -- output of sessionInfo(): R version 2.15.2 (2012-10-26) Platform: i686-redhat-linux-gnu (32-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=C LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] limma_3.14.4 csSAM_1.2.1 GOstats_2.24.0 RSQLite_0.10.0 DBI_0.2-5 [6] graph_1.36.2 Category_2.22.0 AnnotationDbi_1.20.5 affy_1.36.1 Biobase_2.16.0 [11] BiocGenerics_0.4.0 R.utils_1.23.2 R.oo_1.13.0 R.methodsS3_1.4.2 loaded via a namespace (and not attached): [1] affyio_1.22.0 annotate_1.36.0 AnnotationForge_1.0.3 BiocInstaller_1.8.3 genefilter_1.40.0 [6] GO.db_2.8.0 GSEABase_1.18.0 IRanges_1.16.6 parallel_2.15.2 preprocessCore_1.18.0 [11] RBGL_1.34.0 splines_2.15.2 stats4_2.15.2 survival_2.36-14 tools_2.15.2 [16] XML_3.9-4 xtable_1.6-0 zlibbioc_1.4.0 -- Sent via the guest posting facility at bioconductor.org.