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DNAMethylation
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0
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7
replies
315
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Methylation Analysis experiment design using Limma
DNAMethylation
limma
methylationArrayAnalysis
17 days ago • updated 11 days ago
hortense96
• 0
0
votes
0
replies
91
views
DMRcate error need at least two non-NA values to interpolate
DNAMethylation
DMRcate
4 weeks ago
Macsue
• 0
0
votes
6
replies
334
views
unable to access Annotationhub latest record
DNAMethylation
AnnotationHub
EPICv2manifest
annotation
updated 24 days ago by
Tim Peters
▴ 180 • written 4 weeks ago by
s.malik
• 0
0
votes
1
reply
256
views
Error in GOmeth function of missMethyl when EPICv2 annotation is used
DNAMethylation
GO
MethylationArray
missMethyl
EPICv2
updated 8 weeks ago by
belinda.phipson
▴ 40 • written 8 weeks ago by
s.malik
• 0
0
votes
4
replies
418
views
Seeking Strategies for Managing Duplicate CpG Entries in EPIC v2 Manifest
DNAMethylation
EPICv2
IlluminaHumanMethylationEPICmanifest
updated 10 weeks ago by
James W. MacDonald
65k • written 10 weeks ago by
rae.alshammari
▴ 10
1
vote
4
replies
409
views
limma design model for categorical variable
DNAMethylation
limmaGUI
biostatistics
limma
updated 12 weeks ago by
Gordon Smyth
50k • written 12 weeks ago by
Jitendra
▴ 10
0
votes
1
reply
177
views
Sample-specific methylation values using QSEA
DNAMethylation
cfMeDIP-seq
cfDNA
qsea
updated 6 weeks ago by
Simon Pearce
• 0 • written 12 weeks ago by
User3088
• 0
1
vote
4
replies
468
views
EPIC or EPICv2 demo data of brain samples. FlowSorted.DLPFC.EPIC Availability?
DNAMethylation
MethylationArray
methylationArrayAnalysis
illumina
12 weeks ago
mikat
• 0
0
votes
1
reply
494
views
topGO() analysis of DMPs in missMethyl methylation analysis
DNAMethylation
missMethyl
Pathways
12 months ago
Grace
• 0
2
votes
6
replies
1.1k
views
cpg.annotate() in DMRcate error
DNAMethylation
methylationArrayAnalysis
DMRcate
MethylationArrayData
MethylationArray
updated 12 months ago by
Tim Peters
▴ 180 • written 12 months ago by
Grace
• 0
2
votes
6
replies
950
views
No enriched GO terms CpGs
DNAMethylation
missMethyl
updated 14 months ago by
belinda.phipson
▴ 40 • written 14 months ago by
Joana
• 0
0
votes
0
replies
540
views
A survey of computational setups for Illumina Infinium DNA methylation EPIC array pipeline
DNAMethylation
wateRmelon
Computation
minfi
21 months ago
calen.p.ryan
• 0
3
votes
8
replies
1.4k
views
Annotation using QSEA package
DNAMethylation
QSEA
MEDIP
2.9 years ago
rtrivedi1
▴ 10
3
votes
5
replies
2.2k
views
Why the LogFC and delta-beta value are different in ChAMP
DNAMethylation
ChAMP
updated 10 weeks ago by
sup0903
• 0 • written 3.4 years ago by
Hongbii
• 0
14 results • Page
1 of 1
Recent ...
Replies
Answer: How to use bootRanges to bootstrap small RNA loci (nullranges package)
by
Poonam
• 0
Hi Michael, I think there is something wrong with the generation of nullranges. I was working with small RNA and methylation overlaps. …
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 (as at 4 May 2024). In the meantime, you could consider using the goana() and kegga() …
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…
Votes
Answer: Why does GSEA on edgeR results for randomized samples give highly significant p-
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
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