Compute normalised expression values from an SCESet object and return the object with the normalised expression values added.
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character string specified the method of calculating
normalisation factors. Passed to
design matrix defining the linear model to be fitted to the
normalised expression values. If not
character, numeric or logical vector indicating a set of
features to use for calculating normalisation factors. If character, entries
must all be in
character string indicating which slot of the
assayData from the
logical, should the normalised expression values
be returned to the
arguments passed to
This function allows the user to compute normalised expression
values from an SCESet object. The 'raw' values used can be the values in the
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
is also added, containing normalised counts-per-million values.
If the raw values are counts, this function will compute size factors using
calcNormFactors. Library sizes are multiplied
by size factors to obtain an "effective library size" before calculation of
the aforementioned normalized expression values. If
specified, only the specified features will be used to calculate the
If the user wishes to remove the effects of certain explanatory variables,
'design' argument can be defined. The
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
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
for those who prefer North American spelling.
an SCESet object
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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)
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