[BioC] DESeq2 : interactions ?
Yvan Wenger
yvan.wenger at unige.ch
Thu May 1 11:57:07 CEST 2014
Dear list members,
There must be something I missed. I have an RNA-seq timecourse
experiment with triplicates (see below), there are two conditions: H5
and H8, they can be thought of control/treatment.
I am trying to assess if some genes react differently over time
between the two conditions. I use the code below with:
full model: design = ~ time + condition + time:condition
reduced model: reduced = ~ time + condition
Out of 33'000 genes, I find 732 with interaction between time and
condition (FDR < 0.1). Nice.
The problem, some genes that I was expecting to appear in this dataset
do not (see the attached pdf for example, I am only concerned with
differences in H5 and H8). Furthermore, when looking at the FDR of
this gene, I can see that it is very far from being significant, the
FDR is 0.97 !
So I think I am doing something wrong in the way I compare the full
model and the reduce model, probably I am not analyzing the data the
way I want. To recapitulate, what I would like to select are different
behaviours over time between the two conditions.
Many thanks for your inputs !
Yvan
library(DESeq2)
countData <- read.table("counts_all.txt",row.names=1,header=T)
countData_H58 <- countData[,31:90] ### data from table 1 to 30 are
from another condition not considered here.
colData_H58 <- as.data.frame(read.table(file='conditions_H58')) ###
content of the conditions_H58 file pasted below
dds_H58 <- DESeqDataSetFromMatrix(countData = countData_H58,colData =
colData_H58, design = ~ time + condition + time:condition) ### Full
model
dds_H58 <- estimateSizeFactors(dds_H58)
dds_H58 <- estimateDispersions(dds_H58)
write.table(file="normacounts_H58.csv", counts(dds_H58,normalized=TRUE))
ddsLRT_H58 <- nbinomLRT(dds_H58, reduced = ~ time + condition) ### reduced model
res_H58 <- results(ddsLRT_H58,cooksCutoff=FALSE)
write.table(res_H58,file='results_H58.csv',sep="\t",quote=F)
condition time
031_H5_000C H5 0
032_H5_000A H5 0
033_H5_000B H5 0
034_H5_005C H5 0.5
035_H5_005A H5 0.5
036_H5_005B H5 0.5
037_H5_010C H5 1
038_H5_010A H5 1
039_H5_010B H5 1
040_H5_020C H5 2
041_H5_020A H5 2
042_H5_020B H5 2
043_H5_040C H5 4
044_H5_040A H5 4
045_H5_040B H5 4
046_H5_080C H5 8
047_H5_080A H5 8
048_H5_080B H5 8
049_H5_160C H5 16
050_H5_160A H5 16
051_H5_160B H5 16
052_H5_240C H5 24
053_H5_240A H5 24
054_H5_240B H5 24
055_H5_360C H5 36
056_H5_360A H5 36
057_H5_360A H5 36
058_H5_480B H5 48
059_H5_480C H5 48
060_H5_480A H5 48
061_H8_000B H8 0
062_H8_000C H8 0
063_H8_000A H8 0
064_H8_005B H8 0.5
065_H8_005C H8 0.5
066_H8_005A H8 0.5
067_H8_010B H8 1
068_H8_010C H8 1
069_H8_010A H8 1
070_H8_020B H8 2
071_H8_020C H8 2
072_H8_020A H8 2
073_H8_040B H8 4
074_H8_040C H8 4
075_H8_040A H8 4
076_H8_080B H8 8
077_H8_080C H8 8
078_H8_080A H8 8
079_H8_160B H8 16
080_H8_160C H8 16
081_H8_160A H8 16
082_H8_240B H8 24
083_H8_240C H8 24
084_H8_240A H8 24
085_H8_360A H8 36
086_H8_360B H8 36
087_H8_360C H8 36
088_H8_480A H8 48
089_H8_480B H8 48
090_H8_480C H8 48
> sessionInfo()
R version 3.1.0 (2014-04-10)
Platform: x86_64-pc-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] 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
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] DESeq2_1.4.0 RcppArmadillo_0.4.100.2.1
[3] Rcpp_0.11.1 GenomicRanges_1.16.1
[5] GenomeInfoDb_1.0.2 IRanges_1.22.3
[7] BiocGenerics_0.10.0
loaded via a namespace (and not attached):
[1] annotate_1.42.0 AnnotationDbi_1.26.0 Biobase_2.24.0
[4] DBI_0.2-7 genefilter_1.46.0 geneplotter_1.42.0
[7] grid_3.1.0 lattice_0.20-29 locfit_1.5-9.1
[10] RColorBrewer_1.0-5 RSQLite_0.11.4 splines_3.1.0
[13] stats4_3.1.0 survival_2.37-7 tools_3.1.0
[16] XML_3.98-1.1 xtable_1.7-3 XVector_0.4.0
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