Bioconductor Bgx

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3.1k

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Annotation limma beadarray bgx Annotation limma beadarray bgx

updated 11.7 years ago by Gordon Smyth 53k • written 11.7 years ago by Stefanie Busch ▴ 10

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2.0k

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bgx AgiMicroRna bgx AgiMicroRna

updated 13.7 years ago by Lucas Santana dos Santos ▴ 60 • written 13.7 years ago by Karthik K N ▴ 200

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Microarray Annotation Normalization vsn bgx puma Microarray Annotation Normalization vsn

updated 15.4 years ago by Benilton Carvalho ★ 4.3k • written 15.4 years ago by Rainer Tischler ▴ 60

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Microarray affy bgx Microarray affy bgx

15.4 years ago Ernest Turro ▴ 30

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1.4k

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bgx BGmix bgx BGmix

16.4 years ago Lucas Santana dos Santos ▴ 60

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1.2k

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Annotation Cancer bgx Annotation Cancer bgx

17.4 years ago Lynn Amon ▴ 280

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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 …

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

I am, but perhaps I'm not understanding how it should work. > sse = DropletUtils::read10xCounts(curr_files[1], mtx.class = 'SVT_Sparse…

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

Did you use the development version as Aaron suggested? There is an argument to read the market matrix file directly into an `SVT_SparseMat…

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DESeq2 for candidate gene analysis

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Drug datasets for RNA-seq

Comment: Check removeBatchEffect effectiveness

Comment: Streamlining the computing time for MiloDE p-value correction in large dataset?

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