Bioconductor Loaddata

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loaddata ChAMPdata champload R ChAMP

updated 2.4 years ago by hfbarrerat • 0 • written 4.7 years ago by Bethan • 0

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Comment: Best practice for handling large data (matrix with >2^31-1 non-zero elements) in by dan.gatti • 0

Very strange. We have the same package versions. I tried adding the mtx.class, type, and delayed arguments, but it's still making the coun…

Comment: DESeq2 for candidate gene analysis by piffelpaff ▴ 20

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…

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A: Multi-level experiments in Limma

Answer: DESeq2 for candidate gene analysis

DESeq2 for candidate gene analysis

Answer: DESeq2 for candidate gene analysis

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