SCE-assays {SingleCellExperiment} | R Documentation

## Named assay getters and setters

### Description

These are methods for getting or setting `assay(sce, i=X, ...)` where `sce` is a SingleCellExperiment object and `X` is the name of the method. For example, `counts` will get or set `X="counts"`. This provides some convenience for users as well as encouraging standardization of assay names across packages.

### Available methods

In the following code snippets, `x` is a SingleCellExperiment object, `value` is a matrix-like object with the same dimensions as `x`, and `...` are further arguments passed to `assay` (for the getter) or `assay<-` (for the setter).

`counts(x, ...)`, `counts(x, ...) <- value`:

  Get or set a matrix of raw count data, e.g., number of reads or transcripts.

`normcounts(x, ...)`, `normcounts(x, ...) <- value`:

  Get or set a matrix of 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(x, ...)`, `logcounts(x, ...) <- value`:

  Get or set a matrix of 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(x, ...)`, `cpm(x, ...) <- value`:

  Get or set a matrix of counts-per-million values. This is the read count for each gene in each cell, divided by the library size of each cell in millions.

`tpm(x, ...)`, `tpm(x, ...) <- value`:

  Get or set a matrix of transcripts-per-million values. 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(x, ...)`, `weights(x, ...) <- value`:

  Get or set a matrix of weights, e.g., observational weights to be used in differential expression analysis.

### Author(s)

Aaron Lun

### See Also

`assay` and `assay<-`, for the wrapped methods.

### Examples

    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))

[Package SingleCellExperiment version 1.16.0 Index]