[BioC] No significant p-values
Dave Wettmann [guest]
guest at bioconductor.org
Fri Jun 27 15:27:01 CEST 2014
Hello,
I have constructed the following dataset for analysis using DESeq2:
class: DESeqDataSet
dim: 57396 10
exptData(0):
assays(1): counts
rownames(57396): ENSG00000223972 ENSG00000227232 ... ENSG00000210195
ENSG00000210196
rowData metadata column names(0):
colnames(10): 1 2 ... 10 11
colData names(1): condition
> colData(ddsHTSeq)
DataFrame with 10 rows and 1 column
condition
<factor>
1 na
2 na
3 Resistant
4 na
5 Resistant
6 Resistant
7 na
8 na
10 Sensitive
11 Sensitive
I am interested in the differential expression between the drug resistant and sensitive samples ('na' are control samples).
I've clustered the samples and plotted a PCA as described in the vignette. However, in each of these plots the samples do not cluster by their drug sensitivity but are distributed across the plot. I don't have any more information about the samples with which to model any potential covariates.
I was wondering if there were any pointers as to how I could extract some useful meanings from these data please? As might be expected, when I try a DESeq on these data I get no significant p-values.
Thanks in advance,
Dave
-- output of sessionInfo():
R version 3.1.0 (2014-04-10)
Platform: x86_64-unknown-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] pasilla_0.4.0 matrixStats_0.8.14 gplots_2.13.0
[4] vsn_3.32.0 Biobase_2.24.0 DESeq2_1.4.5
[7] RcppArmadillo_0.4.300.0 Rcpp_0.11.1 GenomicRanges_1.16.3
[10] GenomeInfoDb_1.0.2 IRanges_1.22.7 BiocGenerics_0.10.0
loaded via a namespace (and not attached):
[1] affy_1.42.2 affyio_1.32.0 annotate_1.42.0
[4] AnnotationDbi_1.26.0 BiocInstaller_1.14.2 bitops_1.0-6
[7] caTools_1.17 DBI_0.2-7 DESeq_1.16.0
[10] gdata_2.13.3 genefilter_1.46.1 geneplotter_1.42.0
[13] grid_3.1.0 gtools_3.4.0 KernSmooth_2.23-12
[16] lattice_0.20-29 limma_3.20.4 locfit_1.5-9.1
[19] preprocessCore_1.26.1 RColorBrewer_1.0-5 R.methodsS3_1.6.1
[22] RSQLite_0.11.4 splines_3.1.0 stats4_3.1.0
[25] survival_2.37-7 tcltk_3.1.0 tools_3.1.0
[28] XML_3.98-1.1 xtable_1.7-3 XVector_0.4.0
[31] zlibbioc_1.10.0
--
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