Bioconductor LocateVariants

Showing : locateVariants • reset

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MetID TxDB.Hsapiens.UCSC.hg38.knownGene locateVariants GenomicFeatures VariantAnnotation

2.8 years ago davidhillis • 0

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960

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genomiclocation canonicaltranscripts locateVariants

updated 4.3 years ago by James W. MacDonald 68k • written 4.3 years ago by Paola • 0

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variantannotation locatevariants metadata mcols

updated 8.1 years ago by Valerie Obenchain ★ 6.8k • written 8.1 years ago by enricoferrero ▴ 680

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variantannotation predict coding locateVariants granges

8.2 years ago tony j • 0

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locatevariants txdb.hsapiens.ucsc.hg19.knowngene variantannotation

8.4 years ago Aleksandra • 0

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txdb.hsapiens.ucsc.hg19.knowngene ucsc annotation locatevariants variantannotation

updated 8.6 years ago by Valerie Obenchain ★ 6.8k • written 8.6 years ago by Lna • 0

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locatevariants variantannotation genomicfeatures

updated 9.2 years ago by Michael Lawrence ★ 11k • written 9.2 years ago by Lna • 0

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2.8k

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variantannotation locatevariants

10.4 years ago Jakob Goldmann • 0

8 results • Page 1 of 1

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Comment: DESeq2 - how many surrogate variables from svaseq, use of lfcshrink by ja569116 • 0

Hi @atpoint Thanks for the information. Could you please expand on when to use adjusted p-values vs nominal p-values. My understanding was …

Answer: limpa analysis advice by Gordon Smyth 53k

It is actually very hard to estimate the DPC from observed data, because the curve is function of unobserved intensities rather than a func…

Answer: When to use edgeR or limma by Gordon Smyth 53k

The short answer is that we generally prefer edgeR for applications with lots of small counts and limma for complex designs with random eff…

Comment: limpa-blank normalization and Spectronaut's PTM stoichiometry by Gordon Smyth 53k

I updated the above answer today.

Answer: When to use edgeR or limma by James W. MacDonald 68k

I would use edgeR's quasi-likelihood model for any analysis with a smaller number of observations (like 3 vs 3 or similar), but if you have…

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Answer: When to use edgeR or limma

A: deseq2 - paired samples in 2 sequencing types with very different library sizes

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