Hi Dave,

If in your PCA your samples do not cluster by treatment, you likely have
some sort of unwanted variation or batch effect masking the effect of the
treatment in your data. I am not sure more samples will help.
Have you taken a look at the PC loadings past 1 and 2 to see if there is
any PC that captures your treatment? do you have any positive controls? are
you sure your treatment actually causes measurable differences in gene
expression?

The only think I believe will help is RUVSeq:

http://www.bioconductor.org/packages/devel/bioc/html/RUVSeq.html

Lucia


On Fri, Jun 27, 2014 at 9:27 AM, Dave Wettmann [guest] <
guest@bioconductor.org> wrote:

> 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
>
>
> --
> Sent via the guest posting facility at bioconductor.org.
>
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>



-- 
Lucia Peixoto PhD
Postdoctoral Research Fellow
Laboratory of Dr. Ted Abel
Department of Biology
School of Arts and Sciences
University of Pennsylvania

"Think boldly, don't be afraid of making mistakes, don't miss small
details, keep your eyes open, and be modest in everything except your
aims."
Albert Szent-Gyorgyi

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