[BioC] No significant p-values

Dave Wettmann [guest] guest at bioconductor.org
Fri Jun 27 15:27:01 CEST 2014


I have constructed the following dataset for analysis using DESeq2:

class: DESeqDataSet
dim: 57396 10
assays(1): counts
rownames(57396): ENSG00000223972 ENSG00000227232 ... ENSG00000210195
rowData metadata column names(0):
colnames(10): 1 2 ... 10 11
colData names(1): condition

> colData(ddsHTSeq)
DataFrame with 10 rows and 1 column
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,

 -- output of sessionInfo(): 

R version 3.1.0 (2014-04-10)
Platform: x86_64-unknown-linux-gnu (64-bit)

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=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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