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ape
•
reset
0
votes
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1.2k
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ape::dnds produces nan value
R
ape
dnds
nan
nanotubes
2.9 years ago
Working
• 0
0
votes
1
reply
1.7k
views
Difficulty with msaConvert function in R package
ape
msa
updated 3.5 years ago by
UBod
▴ 300 • written 3.9 years ago by
João Tadeu
• 0
0
votes
1
reply
1.5k
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Issues while downloading supraHex package in Linux
supraHex
nlme
hexbin
ape
updated 8.0 years ago by
Martin Morgan
25k • written 8.0 years ago by
agneet257
• 0
0
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0
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1.7k
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Phylogenetic tree from marker genes
ape
phyloseq
dist
metagenomics
8.5 years ago
David
▴ 860
0
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1
reply
4.4k
views
ape - delete bootstrap values (node.labels) less than a certain value
ape
bootstrap
node
phylogenetic
tree
10.4 years ago
wd
▴ 30
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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
Join BOC Sciences at Drug Discovery Chemistry
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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