normaliseExprs: Normalise expression expression levels for an SCESet object

In dynverse/scaterlegacy: Single-cell analysis toolkit for gene expression data in R

Description

Compute normalised expression values from an SCESet object and return the object with the normalised expression values added.

Usage

normaliseExprs(object, method = "none", design = NULL, feature_set = NULL,
  exprs_values = NULL, return_norm_as_exprs = TRUE, ...)

normalizeExprs(...)

Arguments

object	an SCESet object.
method	character string specified the method of calculating normalisation factors. Passed to calcNormFactors.
design	design matrix defining the linear model to be fitted to the normalised expression values. If not NULL, then the residuals of this linear model fit are used as the normalised expression values.
feature_set	character, numeric or logical vector indicating a set of features to use for calculating normalisation factors. If character, entries must all be in featureNames(object). If numeric, values are taken to be indices for features. If logical, vector is used to index features and should have length equal to nrow(object).
exprs_values	character string indicating which slot of the assayData from the SCESet object should be used for the calculations. Valid options are 'counts', 'tpm', 'cpm', 'fpkm' and 'exprs'. Defaults to the first available value of these options in in order shown.
return_norm_as_exprs	logical, should the normalised expression values be returned to the exprs slot of the object? Default is TRUE. If FALSE, values in the exprs slot will be left untouched. Regardless, normalised expression values will be returned to the norm_exprs slot of the object.
...	arguments passed to normaliseExprs (in the case of normalizeExprs) or to calcNormFactors.

Details

This function allows the user to compute normalised expression values from an SCESet object. The 'raw' values used can be the values in the 'counts' (default), 'tpm', 'cpm' or 'fpkm' slot of the SCESet. Normalised expression values are computed through normalize.SCESet and are on the log2-scale, with an offset defined by the logExprsOffset slot of the SCESet object. These are dded to the 'norm_exprs' slot of the returned object. If 'exprs_values' argument is 'counts', a 'norm_cpm' slot is also added, containing normalised counts-per-million values.

If the raw values are counts, this function will compute size factors using methods in calcNormFactors. Library sizes are multiplied by size factors to obtain an "effective library size" before calculation of the aforementioned normalized expression values. If feature_set is specified, only the specified features will be used to calculate the size factors.

If the user wishes to remove the effects of certain explanatory variables, then the 'design' argument can be defined. The design argument must be a valid design matrix, for example as produced by model.matrix, with the relevant variables. A linear model is then fitted using lmFit on expression values after any size-factor and library size normalisation as descrived above. The returned values in 'norm_exprs' are the residuals from the linear model fit.

After normalisation, normalised expression values can be accessed with the norm_exprs function (with corresponding accessor functions for counts, tpm, fpkm, cpm). These functions can also be used to assign normalised expression values produced with external tools to an SCESet object.

normalizeExprs is exactly the same as normaliseExprs, provided for those who prefer North American spelling.

Value

an SCESet object

Author(s)

Davis McCarthy

Examples

data("sc_example_counts")
data("sc_example_cell_info")
pd <- new("AnnotatedDataFrame", data = sc_example_cell_info)
example_sceset <- newSCESet(countData = sc_example_counts, phenoData = pd)
keep_gene <- rowSums(counts(example_sceset)) > 0
example_sceset <- example_sceset[keep_gene,]

## Apply TMM normalisation taking into account all genes
example_sceset <- normaliseExprs(example_sceset, method = "TMM")
## Scale counts relative to a set of control features (here the first 100 features)
example_sceset <- normaliseExprs(example_sceset, method = "none",
feature_set = 1:100)

dynverse/scaterlegacy documentation built on Feb. 17, 2020, 5:07 a.m.