[BioC] question about how to understand exon usage coefficient value
Ou, Jianhong
Jianhong.Ou at umassmed.edu
Thu May 29 21:27:19 CEST 2014
Hi Alejandro,
I am using DEXSeq to analysis alternative splicing events of my knockdown samples. DEXSeq is very easy to use. However, I have some trouble in understanding the results. The question is why some of the log2fold change of the KD vs WT is opposite with the raw counts. For example the E012 in the following sample (attached please find the figures of expression and normalized counts):
groupID featureID exonBaseMean dispersion stat pvalue padj KD NS log2fold_KD_NS
ENSG00000171603:E012 ENSG00000171603 E012 255.50 0.0009438170 2.347679e+02 5.439942e-53 7.353639e-50 36.9088539 20.20013887 0.869601727
genomicData.seqnames genomicData.start genomicData.end genomicData.width genomicData.strand countData.NSrep1 countData.NSrep2
ENSG00000171603:E012 chr1 9797556 9797612 57 - 377 372
countData.KDrep1 countData.KDrep2 transcripts
ENSG00000171603:E012 147 126 ENST0000....
Thank you for your help.
The codes I used is ,
> countFiles
[1] "NS-1.nodenovo.counts" "NS-2.nodenovo.counts" "KD-1.nodenovo.counts" "KD-2.nodenovo.counts"
> sampleTable <- data.frame(row.names=c("NSrep1", "NSrep2", "KDrep1", "KDrep2"), condition=c("NS", "NS", "KD", "KD"))
> sampleTable
condition
NSrep1 NS
NSrep2 NS
KDrep1 KD
KDrep2 KD
> dxd <- DEXSeqDataSetFromHTSeq(countFiles, sampleData=sampleTable, design=~sample+exon+condition:exon, flattenedfile=gffFile)
> dxr <- DEXSeq(dxd)
The sessionInfo is,
R version 3.1.0 (2014-04-10)
Platform: x86_64-apple-darwin12.5.0 (64-bit)
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] biomaRt_2.20.0 Vennerable_3.0 xtable_1.7-3 gtools_3.4.0 reshape_0.8.5 RColorBrewer_1.0-5
[7] lattice_0.20-29 RBGL_1.40.0 graph_1.42.0 DEXSeq_1.10.3 BiocParallel_0.6.0 DESeq2_1.4.5
[13] RcppArmadillo_0.4.300.0 Rcpp_0.11.1 GenomicRanges_1.16.3 GenomeInfoDb_1.0.2 IRanges_1.22.6 Biobase_2.24.0
[19] BiocGenerics_0.10.0
loaded via a namespace (and not attached):
[1] annotate_1.42.0 AnnotationDbi_1.26.0 BatchJobs_1.2 BBmisc_1.6 Biostrings_2.32.0 bitops_1.0-6 brew_1.0-6
[8] codetools_0.2-8 DBI_0.2-7 digest_0.6.4 fail_1.2 foreach_1.4.2 genefilter_1.46.1 geneplotter_1.42.0
[15] hwriter_1.3 iterators_1.0.7 locfit_1.5-9.1 plyr_1.8.1 RCurl_1.95-4.1 Rsamtools_1.16.0 RSQLite_0.11.4
[22] sendmailR_1.1-2 splines_3.1.0 statmod_1.4.19 stats4_3.1.0 stringr_0.6.2 survival_2.37-7 tools_3.1.0
[29] XML_3.98-1.1 XVector_0.4.0 zlibbioc_1.10.0
Yours Sincerely,
Jianhong Ou
LRB 670A
Program in Gene Function and Expression
364 Plantation Street Worcester,
MA 01605
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