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

Convenience methods to get or set named assay fields.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
## 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
``` |

`object` |
A SingleCellExperiment object. |

`value` |
A numeric matrix of the same dimensions as |

`...` |
May contain |

.

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.

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

slot.

Aaron Lun

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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