Bioconductor Methylation

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324

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EPICV1 Methylation combineArrays minifi EPICv2manifest

5 weeks ago • updated 6 days ago Kim • 0

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165

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bsseq methylation

updated 5 weeks ago by James W. MacDonald 65k • written 5 weeks ago by ja569116 • 0

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552

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edgeR methylation

updated 3 months ago by Gordon Smyth 50k • written 3 months ago by sat • 0

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266

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Methylation minfi preprocessQuantile MethylationArrayData preprocessFunnorm

4 months ago kyj2226 • 0

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1.4k

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DMR Methylation R Gviz

updated 5 months ago by Robert Ivanek ▴ 730 • written 3.0 years ago by ltzhou • 0

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1.8k

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ChAMP champ methylation

updated 8 months ago by mike • 0 • written 4.1 years ago by jkong • 0

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2.0k

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ChAMP methylation BMIQ

updated 6 months ago by peter.brederlo • 0 • written 5.0 years ago by moldach ▴ 20

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1.4k

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minfi software error methylation illumina450k

updated 11 months ago by kuzn.di • 0 • written 8.4 years ago by pm2015 • 0

8 results • Page 1 of 1

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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

Answer: Multi-factorial, longitudinal disease progression analysis with unbalanced patie

Answer: Extremely small p-values using Limma for proteomic data

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