Dear all,
I have an RNASeq experiment of 12 samples for 2 conditions (6 samples per condition).
Reads are paired-end, 100 bp.
I performed differential expression analysis using DESeq2 and was surprised not to find a gene as significantly differentially expressed (it has been reported in an array and another RNASeq experiments studying the same conditions-but different samples).
Here is the DESeq2 procedure I followed:
and the sessionInfo
Here are the normalized counts and stats for my gene of interest:
The estimated log2FC is just above 1 but the adjusted p-value is very high, especially compare to this other gene, which is similar to my gene of interest, but with less magnitude and with low counts:
In condition 2, one value is 0, as in condition 1. But it is also the case for the second gene. It seems weird to me that this second gene is differential but not my gene of interest. I am probably missing something here.
Could somebody explain me what is going on please?
Thank you in advance for any help,
Jane
I have an RNASeq experiment of 12 samples for 2 conditions (6 samples per condition).
Reads are paired-end, 100 bp.
I performed differential expression analysis using DESeq2 and was surprised not to find a gene as significantly differentially expressed (it has been reported in an array and another RNASeq experiments studying the same conditions-but different samples).
Here is the DESeq2 procedure I followed:
Code:
library("DESeq2") DataFrame=data.frame(Nom,Fichier,HIST) ################################################ ### Model ### ################################################ dds_raw=DESeqDataSetFromHTSeqCount(DataFrame,"/home/Results/Data/26022016", design= ~HIST) str(colData(dds_raw)$HIST) dds_raw$HIST = relevel(dds_raw$HIST, ref="Cond1") dds <- DESeq(dds_raw, minReplicatesForReplace=6) ##################################### ### Differential expression Tests ### ##################################### resMLE <- results(dds, addMLE=TRUE, cooksCutoff=FALSE,alpha=0.05) summary(resMLE) resMLEOrdered <- resMLE[order(resMLE$padj),] write.table(resMLEOrdered, file="ResMLETable",quote=FALSE,row.names=TRUE, col.names=TRUE,sep="\t") write.table(counts(dds,normalized=TRUE), file="NormalizedTable",quote=FALSE,row.names=TRUE, col.names=TRUE,sep="\t")
Code:
> sessionInfo() R version 3.2.0 (2015-04-16) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 14.04.3 LTS locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=fr_FR.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] parallel stats4 stats graphics grDevices utils datasets methods base other attached packages: [1] DESeq2_1.10.1 RcppArmadillo_0.6.500.4.0 Rcpp_0.12.3 SummarizedExperiment_1.0.2 Biobase_2.30.0 GenomicRanges_1.22.3 [7] GenomeInfoDb_1.6.1 IRanges_2.4.6 S4Vectors_0.8.7 BiocGenerics_0.16.1 loaded via a namespace (and not attached): [1] RColorBrewer_1.1-2 futile.logger_1.4.1 plyr_1.8.3 XVector_0.10.0 futile.options_1.0.0 tools_3.2.0 zlibbioc_1.16.0 rpart_4.1-10 [9] RSQLite_1.0.0 annotate_1.48.0 gtable_0.1.2 lattice_0.20-33 DBI_0.3.1 gridExtra_2.0.0 genefilter_1.52.1 cluster_2.0.3 [17] locfit_1.5-9.1 grid_3.2.0 nnet_7.3-11 AnnotationDbi_1.32.3 XML_3.98-1.3 survival_2.38-3 BiocParallel_1.4.3 foreign_0.8-66 [25] latticeExtra_0.6-26 Formula_1.2-1 geneplotter_1.48.0 ggplot2_2.0.0 lambda.r_1.1.7 Hmisc_3.17-1 scales_0.3.0 splines_3.2.0 [33] xtable_1.8-0 colorspace_1.2-6 acepack_1.3-3.3 munsell_0.4.2
Code:
90.7033407596 0 0 2.3672116923 0 0 939.0444914676 404.8903299633 665.5848352517 0 1034.9196508454 438.2486305103 baseMean log2FoldChange lfcMLE lfcSE stat pvalue padj 297.9798742075 -1.0090565228 -5.2239343815 0.5743666929 -1.7568158725 0.0789491987 0.4351815966
Code:
4.1228791254 0 0.8533500773 0 0 0.7423895021 99.6303301923 89.0758725919 128.7791432768 0 69.6249931217 43.9908663202 baseMean log2FoldChange lfcMLE lfcSE stat pvalue padj 36.4016520173 -3.353879756 -6.1662095289 0.6214945118 -5.3964752585 0.000000068 0.000052119
Could somebody explain me what is going on please?
Thank you in advance for any help,
Jane
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