SCE-assays: Named assay fields

Description Usage Arguments Details Value Author(s) See Also Examples

Description

Convenience methods to get or set named assay fields.

Usage

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## S4 method for signature 'SingleCellExperiment'
counts(object, ...)
## S4 replacement method for signature 'SingleCellExperiment'
counts(object, ...) <- value

## S4 method for signature 'SingleCellExperiment'
normcounts(object, ...)
## S4 replacement method for signature 'SingleCellExperiment'
normcounts(object, ...) <- value

## S4 method for signature 'SingleCellExperiment'
logcounts(object, ...)
## S4 replacement method for signature 'SingleCellExperiment'
logcounts(object, ...) <- value

## S4 method for signature 'SingleCellExperiment'
cpm(object, ...)
## S4 replacement method for signature 'SingleCellExperiment'
cpm(object, ...) <- value

## S4 method for signature 'SingleCellExperiment'
tpm(object, ...)
## S4 replacement method for signature 'SingleCellExperiment'
tpm(object, ...) <- value

## S4 method for signature 'SingleCellExperiment'
weights(object, ...)
## S4 replacement method for signature 'SingleCellExperiment'
weights(object, ...) <- value

Arguments

object

A SingleCellExperiment object.

value

A numeric matrix of the same dimensions as object.

...

May contain withDimnames, which is forwarded to assays

.

Details

These are wrapper methods for getting or setting assay(object, i=X, ...) where X is the name of the method. For example, counts will get or set X="counts". This provide some convenience for users as well as encouraging standardization of naming across packages.

Our suggested interpretation of the fields are as follows:

counts:

Raw count data, e.g., number of reads or transcripts.

normcounts:

Normalized values on the same scale as the original counts. For example, counts divided by cell-specific size factors that are centred at unity.

logcounts:

Log-transformed counts or count-like values. In most cases, this will be defined as log-transformed normcounts, e.g., using log base 2 and a pseudo-count of 1.

cpm:

Counts-per-million. This is the read count for each gene in each cell, divided by the library size of each cell in millions.

tpm:

Transcripts-per-million. This is the number of transcripts for each gene in each cell, divided by the total number of transcripts in that cell (in millions).

weights:

A matrix of weights, e.g., observational weights to be used in differential expression analysis.

Value

Each method returns a matrix from the correspondingly named field in the assays slot.

Author(s)

Aaron Lun

See Also

SingleCellExperiment

Examples

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example(SingleCellExperiment, echo=FALSE) # Using the class example
counts(sce) <- matrix(rnorm(nrow(sce)*ncol(sce)), ncol=ncol(sce))
dim(counts(sce))

# One possible way of computing normalized "counts"
sf <- 2^rnorm(ncol(sce))
sf <- sf/mean(sf)
normcounts(sce) <- t(t(counts(sce))/sf)
dim(normcounts(sce))

# One possible way of computing log-counts
logcounts(sce) <- log2(normcounts(sce)+1)
dim(normcounts(sce))

SingleCellExperiment documentation built on May 6, 2019, 4:05 a.m.