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AffymetrixChip
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Unable to annotate ExpressionSet object from hugene11st chip
oligo
limma
hugene11stprobeset.db
AffymetrixChip
7 weeks ago • updated 6 weeks ago
Efra
• 0
1
vote
4
replies
689
views
How to annotate mir4.1 arrays?
AffymetrixChip
miRNA
oligo
12 months ago • updated 9 months ago
richardallenfriedmanbrooklyn
▴ 20
0
votes
1
reply
491
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Affymetrix hgu133plus2.db: How to best derive an expression value for genes that map to multiple probe ids
affydata
hgu133plus2.db
AffymetrixChip
updated 9 months ago by
ATpoint
★ 4.1k • written 9 months ago by
Matthias Munz
▴ 10
1
vote
2
replies
503
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Affymetrix analysis Chiptype hgu133plus2hsentrezg.db vs hgu133plus2.db
hgu133plus2hsentrezg.db
chiptype
chipster
hgu133plus2.db
AffymetrixChip
11 months ago
alexia.brunel
• 0
4 results • Page
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Comment: deseq2 results
by
sajadahmad41454
• 0
thank you for your response, should i remove or discard that sample? since the red outlier on left represents one of healthy samples.
Comment: deseq2 results
by
swbarnes2
★ 1.4k
It looks like the PCA plot of a real RNASeq experiment. The red outlier on the left might be the mathematical reason why you have few vali…
Comment: Multi-factorial, longitudinal disease progression analysis with unbalanced patie
by
Dylan.Sheerin
• 0
Thank you very much, Gordon. I'll give voomLmFit a go!
Answer: Multi-factorial, longitudinal disease progression analysis with unbalanced patie
by
Gordon Smyth
50k
Including subjectID in the design matrix always accounts for unbalanced sampling and patient variation but subjects with incomplete records…
Comment: Log-cpm values from limma
by
Gordon Smyth
50k
No, it does not mean that. `voom()` uses the design matrix, including the W covariates, to compute precision weights but not to adjust the …
Votes
Comment: deseq2 results
Comment: deseq2 results
Answer: Multi-factorial, longitudinal disease progression analysis with unbalanced patie
Answer: Extremely small p-values using Limma for proteomic data
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