Hi, everyone We want to determine which genes are tissue-specific expression in RNA-Seq data set. There are 3 samples for each tissue and 10 tissues in total. For example, we have: Sample1, Sample2, Sample3 Tissue1, Tissue2,..., Tissue10 How can we use DESeq2 to determine which genes are tissue-specific expression? Can we take Tissue1 as CASE, other 9 tissues as CONTROL, to find tissue-specific genes (high expression in Tissue1) ? Thanks. Eman Lee -- output of sessionInfo(): R version 3.0.0 (2013-04-03) Platform: i386-w64-mingw32/i386 (32-bit) locale: [1] LC_COLLATE=Chinese_People's Republic of China.936 [2] LC_CTYPE=Chinese_People's Republic of China.936 [3] LC_MONETARY=Chinese_People's Republic of China.936 [4] LC_NUMERIC=C [5] LC_TIME=Chinese_People's Republic of China.936 attached base packages: [1] grid stats graphics grDevices utils datasets methods [8] base other attached packages: [1] ggplot2_0.9.3.1 plotrix_3.5-2 gplots_2.11.3 [4] MASS_7.3-29 KernSmooth_2.23-10 caTools_1.14 [7] gtools_3.1.0 gdata_2.13.2 loaded via a namespace (and not attached): [1] bitops_1.0-6 colorspace_1.2-4 dichromat_2.0-0 [4] digest_0.6.4 gtable_0.1.2 labeling_0.2 [7] munsell_0.4.2 plyr_1.8 proto_0.3-10 [10] RColorBrewer_1.0-5 reshape2_1.2.2 scales_0.2.3 [13] stringr_0.6.2 tools_3.0.0 -- Sent via the guest posting facility at bioconductor.org.

Hi On 20/12/13 04:03, Eman Lee [guest] wrote: > How can we use DESeq2 to determine which genes are tissue-specific expression? > Can we take Tissue1 as CASE, other 9 tissues as CONTROL, to find tissue-specific genes (high expression in Tissue1) ? In principle, yes. However, with this setting, DESeq2 would look for genes where Case differs from Control much more than the Control samples differ from each other, i.e., extreme cases where a gene has very similar expression in all but one tissues, and a very different one in this remaining tissue. If a gene sticks out in more than a single tissue (e.g., strong in two and weak in eight tissues), you wouldn't find it. The conventional way would be to do a likelihood ratio test to see whether the tissue effect is significant, i.e., compare the models "count ~ tissue" against "count ~ 1". You can then look at the shrunken log fold changes reported by DESeq2 for the indivdual tissues to find out which tissue(s) are different. Or you do Wald tests (DESeq2 offers both likelihood ratio tests and Wald tests) and use the Wald test p values to find tissues which differ significantly from the average for a gene. In standard linear modelling, you have to assign one of the tissues as your "base level". It gets absorbed into the model's intercept and all other tissues' expressions are reported relative to it, and the log fold changes get shrunken towards it (if you use DESeq2's coefficient shrinkage). This is undesirable as it makes one tissue special. To solve this, we have, very recently, implemented "expanded design matrices" in the devel version of DESeq2, and this might be quite useful for you. (The original motivation was also a search for tissue-specific usage, in that case of exons; see Reyes et al., PNAS 2013, 110:15377). Simon