Bioconductor DeSeq2

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203

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RNA-seq DESeq2

updated 4 days ago by ATpoint ★ 4.1k • written 6 days ago by Reza • 0

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133

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DESeq2 DifferentialExpression rnaseqGene DifferentialRegulation

1 day ago • updated 1 hour ago sajadahmad41454 • 0

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137

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DESeq2

updated 4 days ago by Michael Love 42k • written 6 days ago by Zainab ▴ 10

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257

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DESeq2

updated 1 day ago by ATpoint ★ 4.1k • written 8 days ago by angelathuynh5 • 0

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242

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DESeq2 design coldata

updated 5 days ago by swbarnes2 ★ 1.4k • written 7 days ago by arfranco ▴ 130

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DESeq2 rnaseqGene Normalization

updated 19 hours ago by ATpoint ★ 4.1k • written 20 hours ago by sajadahmad41454 • 0

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142

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DESeq2 ImpulseDE2 timecour timecoursedata moanin

9 days ago • updated 6 days ago Aurora • 0

7 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 …

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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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