Bioconductor Coldata

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242

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DESeq2 design coldata

updated 6 days ago by swbarnes2 ★ 1.4k • written 7 days ago by arfranco ▴ 130

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564

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ColData pheatmap DESeq2

written 10 months ago by dania.khalid • 0

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1.0k

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SingleCellExperiment colData rename

updated 14 months ago by Basti ▴ 780 • written 14 months ago by achoppe ▴ 10

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580

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BiSeq Coldata

3.9 years ago akhaira • 0

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1.4k

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deseq2 levels factor colData

updated 5.2 years ago by Michael Love 42k • written 5.2 years ago by ariel ▴ 20

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42k

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deseq2 coldata

updated 5.5 years ago by swbarnes2 ★ 1.4k • written 5.5 years ago by mjrarcher ▴ 10

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3.1k

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variantannotation coldata

updated 5.6 years ago by Valerie Obenchain ★ 6.8k • written 5.6 years ago by lvclark ▴ 10

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3.5k

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deseq2 coldata NA

updated 5.8 years ago by Michael Love 42k • written 5.8 years ago by t.kuilman ▴ 170

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1.5k

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multiassayexperiment colData

updated 6.4 years ago by Levi Waldron ★ 1.1k • written 6.4 years ago by Lluís Revilla Sancho ▴ 730

9 results • Page 1 of 1

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Answer: Differential gene expression analysis results by ATpoint ★ 4.1k

Pease do not open multiple posts for the same issue: https://support.bioconductor.org/p/9158194/#9158222 The support site is not meant f…

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…

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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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