Normalise expression expression levels for an SCESet object
Compute normalised expression values from an SCESet object and return the object with the normalised expression values added.
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character string giving method to be used to calculate
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 the PCA. 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 added to the
'norm_exprs' slot of the object. Normalised expression values are on
the log2-scale, with an offset defined by the
slot of the SCESet object. If the
'exprs_values' argument is one of
'fpkm', then a corresponding slot
with normalised values is added:
'norm_fpkm', as appropriate. If
'exprs_values' argument is
'norm_cpm' slot is
also added, containing normalised counts-per-million values.
Normalisation is done relative to a defined feature set, if desired, which
defines the 'library size' by which expression values are divided. If no
feature set is defined, then all features are used. A normalisation size
factor can be computed (optionally), which internally uses
calcNormFactors. Thus, any of the methods available for
calcNormFactors can be used: "TMM", "RLE", "upperquartile"
or "none". See that function for further details. Library sizes are multiplied
by size factors to obtain a "normalised library size" before normalisation.
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 normalised expression values are then 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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