as.DESeqDataSet | R Documentation |
Force an object to belong to a class.
as.DESeqDataSet(x, ...)
as.DESeqTransform(x, ...)
as.DGEList(x, ...)
## S4 method for signature 'bcbioRNASeq'
as.DESeqDataSet(x, quiet = FALSE)
## S4 method for signature 'bcbioRNASeq'
as.DESeqTransform(x, quiet = FALSE)
## S4 method for signature 'bcbioRNASeq'
as.DGEList(x, quiet = FALSE)
x |
Object. |
quiet |
|
... |
Additional arguments. |
Modified object, of desired coercion type.
Coerce to RangedSummarizedExperiment
.
Round raw counts to integer matrix
.
Subset colData()
to include only clean
factor columns. See sampleData()
for details.
Simplify metadata()
to include only relevant information and
updates sessionInfo
.
Note that gene-level counts are required. Alternatively,
tximport::summarizeToGene()
can be called to convert transcript-level
counts to gene-level. By default, we're using length-scaled TPM, so a
corresponding average transcript length matrix isn't necessary. The average
transcript length matrix is only necessary when raw counts matrix isn't
scaled during tximport call (see countsFromAbundance
in
tximport::tximport()
documentation).
Coerce to DESeqDataSet
.
Call DESeq2::DESeq()
.
Call DESeq2::varianceStabilizingTransformation()
.
When countsFromAbundance = "lengthScaledTPM"
(default):
Call edgeR::DGEList()
.
When countsFromAbundance = "no"
:
Call edgeR::DGEList()
.
Obtain per-observation scaling factors for length, adjusted to avoid changing the magnitude of the counts.
Computing effective library sizes from scaled counts, to account for composition biases between samples.
Combine effective library sizes with the length factors, and calculate offsets for a log-link GLM.
Apply offset matrix using edgeR::scaleOffset()
.
Updated 2022-03-07.
Michael Steinbaugh
tximport::tximport()
.
DESeq2::DESeqDataSetFromTximport()
.
edgeR::DGEList()
.
data(bcb)
## bcbioRNASeq to DESeqDataSet ====
dds <- as.DESeqDataSet(bcb)
class(dds)
## bcbioRNASeq to DESeqTransform ====
dt <- as.DESeqTransform(bcb)
class(dt)
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