Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

6.4 Core Differentially Expressed Genes Results
a curve to this data. With the diagnostic plot shown in fig 6.9 we validated that
the estimates of the single gene variance functions followed the empirical vari-
ance well enough, as indicated by the red line representing the local regression
fit, even though the spread of the single gene variance values is considerable,
as one should expect given that each variance value is estimated from just
four values. Having estimated and verified the variance-to-mean dependance,
Figure 6.9: Plot of the estimates for each gene of the variance against the base
levels, i.e. the count value for a tag divided by the total number of counts. The red
line represents the fit from the local regression.
we then proceeded to look for differentially expressed genes using the DESeq
package function nbinomTest. With this function we generated the following
values for each gene: the mean expression level, as a joint estimate between
conditions "N" (normal) and "T" (tumour) and as a separate estimate for each
condition, the fold change (FC) from condition N to T, the natural logarithm
(Ln) of the fold change, and the p-value for the statistical significance of this
change. The p-adjusted value is also computed by the nbinomTest function
to adjust the p-value for multiple testing with the Benjamin-Hochberg pro-
cedure, which controls the FDR. We first plotted the computed fold changes
against the mean values and coloured in red the genes that were significant
at 1% FDR (Fig 6.10). Interestingly, these genes seemed to cluster at higher
values of the mean and absolute value of Ln(FC), indicative of gene expression
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