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gene set testing
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Interpretation of mroast p-value results for gene set testing of specific molecular signatures using microarray data
mroast
camera
gene set testing
3.6 years ago
svlachavas
▴ 830
4
votes
11
replies
2.0k
views
Dealing with duplicated genes in camera gene set testing
limma
camera
gene set testing
ensembl
updated 4.4 years ago by
Robert Castelo
★ 3.3k • written 4.4 years ago by
Jeremy Beales
▴ 10
2
votes
3
replies
1.4k
views
Perform limma based gene-set testing for a two-group comparison in a microarray dataset regarding specific biological processes
limma
agilent microarrays
mroast
fry gene tests
gene set testing
updated 5.6 years ago by
Gordon Smyth
50k • written 5.6 years ago by
svlachavas
▴ 830
7
votes
2
replies
1.3k
views
Pre-ranked camera gene set testing with edgeR results
limma
camera
cameraPR
edgeR
gene set testing
updated 5.6 years ago by
Gordon Smyth
50k • written 5.6 years ago by
jamie.gearing
▴ 60
1
vote
4
replies
1.2k
views
Do the gene set statistics of the ROAST paper correspond to those implemented in the .roastEffects R function?
limma
roast
statistics
source code
gene set testing
5.7 years ago
Andrea Carenzo
• 0
0
votes
1
reply
1.3k
views
GAGE function and the same.dir option
gage
gene set testing
updated 5.9 years ago by
Luo Weijun
★ 1.6k • written 5.9 years ago by
Aurora
▴ 20
4
votes
6
replies
2.0k
views
Gene set Testing with Limma for Agilent IDs probes?
limma
agilent microarrays
gene set testing
annotation
7.3 years ago
Bouzid.a
▴ 20
2
votes
3
replies
1.6k
views
EGSEA usage with microarray data (with vooma)?
EGSEA
gene set testing
vooma
limma voom
gsea
7.3 years ago • updated 7.2 years ago
Pekka Kohonen
▴ 190
7
votes
13
replies
2.2k
views
Test whether expression levels of two gene sets are significantly different
R
DESeq2
gene set testing
gene expression
7.5 years ago
Jon Bråte
▴ 250
3
votes
4
replies
1.5k
views
Competitive gene set testing between two sets of genes
gene set testing
camera
edger
updated 7.8 years ago by
Gordon Smyth
50k • written 7.8 years ago by
le2336
▴ 20
1
vote
0
replies
1.3k
views
Gene Set Testing or GSEA for functional annotation of co-expressed ncRNA and coding genes
gsea
ncrna
coexpression
gene set analysis
gene set testing
8.0 years ago
m.fletcher
▴ 20
1
vote
5
replies
2.1k
views
Confused by results from CAMERA gene set testing
limma
camera
gene set testing
8.0 years ago
claire.n.levy
▴ 10
7
votes
6
replies
3.2k
views
Interpretation of mroast gene set analysis in R and appropriate use of the function barcode plot regarding a microarray dataset
limma
mroast
barcodeplot
KEGG
gene set testing
updated 8.4 years ago by
Gordon Smyth
50k • written 8.4 years ago by
svlachavas
▴ 830
6
votes
10
replies
1.8k
views
Possible implementation of roast/mroast for testing common DE patterns across two different experiments
gene set testing
roast
mroast
differential gene expression
bioconductor
9.0 years ago
svlachavas
▴ 830
14 results • Page
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Answer: Once again a "Model matrix not full rank"
by
swbarnes2
★ 1.3k
Replicate numbers, like the 1 in control_1 are fine in sample names, but never add them to anything else in colData. From the computer's p…
Answer: limma Intercept vs No-intercept models completely changing DMR results?
by
Gordon Smyth
50k
I'll add a little bit of general advice to James' answer. You seem to have the misunderstanding that you can change the design matrix but …
Answer: package goseq seems to be not available on the latest version of R
by
Gordon Smyth
50k
It is true that goseq isn't available for Bioc 3.19. That is because goseq depends on txbmaker, which is itself not yet available for Bioc…
Answer: limma Intercept vs No-intercept models completely changing DMR results?
by
James W. MacDonald
65k
This part: ``` design_intercept <- model.matrix(~Alt+Sex+Age+PC1, data=targets_Sherpa) colnames(design_intercept) <- c("AltSHP_LA","Al…
Comment: CombineArrays for EPIC and EPIC V2
by
Kim
• 0
Thank you Tim, this is a great help in getting me started!
Votes
Answer: limma Intercept vs No-intercept models completely changing DMR results?
Answer: CombineArrays for EPIC and EPIC V2
Answer: Too many significant genes when integrating gtex and tcga
Comment: Too many significant genes when integrating gtex and tcga
A: Print Differentially Expressed Exons From Dexseq Results
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