Bioconductor Txdb.mmulatta.ucsc.rhemac8.refgene

Showing : txdb.mmulatta.ucsc.rhemac8.refgene • reset

0

votes

1

reply

1.4k

views

txdb TxDb.Mmulatta.UCSC.rheMac8.refGene

updated 7.3 years ago by daniel.vantwisk ▴ 50 • written 7.3 years ago by Brian Smith ▴ 120

1 result • Page 1 of 1

Recent ...

Replies

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

Aaron already answered your question. If you have an HDF5 file, it will be read in as a `TENxMatrix` object, which isn't actually read in -…

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…

Votes

A: Multi-level experiments in Limma

Answer: DESeq2 for candidate gene analysis

DESeq2 for candidate gene analysis

Answer: DESeq2 for candidate gene analysis

Awards • All