Bioconductor Random effects

Showing : random effects • reset

vote

replies

2.0k

views

limma repeated measurements blocking random effects

updated 4.2 years ago by Gordon Smyth 50k • written 4.2 years ago by Jane ▴ 10

votes

replies

989

views

limma rna-seq edger random effects

updated 4.4 years ago by Gordon Smyth 50k • written 4.4 years ago by mjgarcia • 0

votes

1.2k

views

random effects interaction limma rna-seq

5.0 years ago flippy23 • 0

votes

replies

882

views

rnaseq limma edger deseq2 random effects

5.3 years ago tcalvo ▴ 90

votes

replies

2.5k

views

limma-voom duplicatecorrelation random effects

updated 5.5 years ago by Gordon Smyth 50k • written 5.5 years ago by tcalvo ▴ 90

vote

replies

2.8k

views

batch effect Coeficents non estimable block random effects limma design matrix

updated 6.5 years ago by Aaron Lun ★ 28k • written 6.5 years ago by mheydarpour ▴ 10

votes

replies

1.2k

views

deseq2 random effects batch effects RNA-seq rna-seq

updated 6.8 years ago by Michael Love 41k • written 6.8 years ago by Dipro • 0

votes

replies

3.5k

views

rna-seq edger mixed model random effects lme4

updated 7.9 years ago by Gordon Smyth 50k • written 7.9 years ago by Ekarl2 ▴ 80

8 results • Page 1 of 1

Recent ...

Replies

Comment: Why does GSEA on edgeR results for randomized samples give highly significant p- by Gordon Smyth 50k

Please ask you own question. Inserting your own dataset into someone else's question like this is very impolite. I have already answered m…

Comment: package goseq seems to be not available on the latest version of R by Gordon Smyth 50k

limma most certainly is available. It installs in R 4.4.0 and Bioc 3.19 for all platforms without any issues. It has not had any period of …

Comment: package goseq seems to be not available on the latest version of R by Patrycja ▴ 20

limma package also isn't available.Is there any information when these packages will be available?

Comment: Why does GSEA on edgeR results for randomized samples give highly significant p- by Chris • 0

Thanks Gordon for the advice! I ran fgsea, gsva and camera(), the most significant pathways are different. For the purpose of finding pathw…

Comment: How to use bootRanges to bootstrap small RNA loci (nullranges package) by Michael Love 41k

Check the number of bootstrapped data per chromosome compared to the original, and then you can also look at its distribution. For this it'…

Votes

Answer: Why does GSEA on edgeR results for randomized samples give highly significant p-

Handling multiple differential expression comparisons

Answer: fgsea significant result

package goseq seems to be not available on the latest version of R

Awards • All

Scholar to ATpoint ★ 4.1k