Best values to report. svalues? padj values? IHW results values? apeglm shrinkage values?
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16 months ago
n.tear ▴ 60

Hi,

I have run an RNAseq analysis and am stuck on what would be the most appropriate thing to report in results.

I first decided to report the results of IHW tested results from DESeq2, from reading the vignettes this output is suggested to be a better method than the default results output by DESeq2. Is this correct?

Secondly, I applied apeglm shrinkage to my dds object for plotting a MA plot, because from reading the vignettes this output is suggested to be a better shrinkage than the default shrinkage output by DESeq2.

However I am getting confused by which results to finally report.

I am hoping to sort my results by the most robust changes (significnace value) in my dataset as many of my genes have a LFC of >2 in all results.

Should I output apeglm results and report the associated s values? or should I use IHW results and report the associated adj.pvalues?

OR should I output apeglm results and report the associated adj.pvalues?

Which is the 'gold standard' or best course of action?

Fot the latter, svalues = FALSE, doesn't seem to be working for me to produce padj values when I run my dds object through lfcshrink()

> res.sLFC <- lfcShrink(dds.s, coef="comparison_disease_vs_Control", svalue = FALSE, type="apeglm", lfcThreshold=1)
using 'apeglm' for LFC shrinkage. If used in published research, please cite:
    Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
    sequence count data: removing the noise and preserving large differences.
    Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
computing FSOS 'false sign or small' s-values (T=1)
> res.sLFC
log2 fold change (MAP): comparison PPCD vs Control 

DataFrame with 31172 rows and 4 columns
                   baseMean log2FoldChange     lfcSE      svalue
                  <numeric>      <numeric> <numeric>   <numeric>
ENSG00000000003 30899.27765      -2.980110  0.313910 1.14037e-11
ENSG00000000005     2.92703      -3.052505  1.084273 4.83066e-03
ENSG00000000419  2457.80966       0.288835  0.164714 7.25880e-01
ENSG00000000457  1310.70709      -0.930851  0.182720 2.58388e-01
ENSG00000000460   417.26650       0.919899  0.334406 2.23694e-01
...                     ...            ...       ...         ...
ENSG00000288584    2.418664    -0.21016742  0.713188    0.536877
ENSG00000288585   10.382099     0.00861317  0.381028    0.678976
ENSG00000288586   13.772947    -0.34706693  0.427470    0.613719
ENSG00000288587    8.671689     0.01334065  0.859633    0.553893
ENSG00000288588    0.715984    -0.34188076  0.952566    0.358753

Many thanks for any assistance,

Nathan

RNA-Seq apeglm • 819 views
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