Bioconductor Txdb.mmusculus.ucsc.mm9.knowngene

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TxDb.Mmusculus.UCSC.mm9.knownGene ensembldb GenomicFeatures Mus_musculus

written 20 months ago by ramonmassoni ▴ 10

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annotatePeak chipseeker chipseq org.Mm.eg.db TxDb.Mmusculus.UCSC.mm9.knownGene

updated 10.1 years ago by Martin Morgan 25k • written 10.2 years ago by Elsa • 0

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software error txdb.mmusculus.ucsc.mm9.knowngene genomicfeatures

updated 10.7 years ago by Hervé Pagès 16k • written 10.7 years ago by nathan.harmston • 0

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Comment: DESeq2 for candidate gene analysis by piffelpaff ▴ 10

thank you so much for answering Dr. Love!

Answer: DESeq2 for candidate gene analysis by Michael Love 43k

Your plan 1 is reasonable. Run DESeq2 on the whole dataset to obtain size factors and moderated dispersion estimates and then extract the u…

Comment: Best practice for handling large data (matrix with >2^31-1 non-zero elements) in by James W. MacDonald 68k

Oh. ``` > z <- read10xCounts(c(tmpdir, tmpdir), mtx.class = "SVT_SparseMatrix", delayed = TRUE) > class(counts(z)) [1] "DelayedMatrix" att…

Comment: Best practice for handling large data (matrix with >2^31-1 non-zero elements) in by James W. MacDonald 68k

That's weird. Using the example data works for me. ``` > example(read10xCounts) rd10xC> # Mocking up some 10X genomics output. rd10xC> ex…

Comment: Join BOC Sciences at Drug Discovery Chemistry by teamardigen • 0

Sounds like an exciting event for anyone passionate about small molecule innovation. The focus on discovery and optimization really drives …

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Answer: DESeq2 for candidate gene analysis

DESeq2 for candidate gene analysis

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Drug datasets for RNA-seq

Comment: Check removeBatchEffect effectiveness

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