Closed lgeistlinger closed 4 years ago
This is now possible in EnrichmentBrowser v2.19.5.
You can install this version of the package directly from github via:
BiocManager::install("lgeistlinger/EnrichmentBrowser")
Now, importing from DESeq2
> se <- import(dds, res)
> se
class: RangedSummarizedExperiment
dim: 64102 8
metadata(4): '' version design dataType
**assays(2): raw norm**
rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
rowData names(27): baseMean FC ... deviance maxCooks
colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
colData names(11): SampleName cell ... sizeFactor GROUP
will automatically attach an additionally assay norm
that contains the variance-stabilized expression values that are normalized for differences in library size between samples.
The resulting se
can then be used to carry out enrichment methods such as gsea
:
> res <- sbea("gsea", se, kegg.gs)
Permutations: 1 -- 100
Processing ...
Thank you so much, @lgeistlinger ! Here are the updates:
design = ~ batch + condition
worked! (Results were not very different with or without age, thanks to you, age is no more covariate of interest in my analysis.)I could not proceed without normalization:
Normalization worked but excluded ~91 % of genes:
The results were from DESeq2 and not from raw read counts. Considering the vignette, I am not sure if normalization is needed. Also, DESeq2 output already has normalization factors:
How can I make use of DESeq2's normalization factors to normalize the count slot and create "norm" slot in an SE? (I think, it's an important consideration for the
import()
function.)Originally posted by @vivek-verma202 in https://github.com/lgeistlinger/EnrichmentBrowser/issues/23#issuecomment-685080240