Description Available methods Author(s) See Also Examples

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.

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.

Aaron Lun

`assay`

and `assay<-`

, for the wrapped methods.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
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))
``` |

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