CCcorrect {RaceID} | R Documentation |
Dimensional Reduction by PCA or ICA
Description
This functions performs dimensional reduction by PCA or ICA and removes components enriched for particular gene sets, e.g. cell cycle related genes genes associated with technical batch effects.
Usage
CCcorrect(
object,
vset = NULL,
CGenes = NULL,
ccor = 0.4,
pvalue = 0.01,
quant = 0.01,
nComp = NULL,
dimR = FALSE,
mode = "pca",
logscale = FALSE,
FSelect = TRUE
)
Arguments
object |
|
vset |
List of vectors with genes sets. The loadings of each component are tested for enrichment in any of these gene sets and if the lower |
CGenes |
Vector of gene names. If this argument is given, gene sets to be tested for enrichment in PCA- or ICA-components are defined by all genes with a Pearson's correlation of |
ccor |
Positive number between 0 and 1. Correlation threshold used to detrmine correlating gene sets for all genes in |
pvalue |
Positive number between 0 and 1. P-value cutoff for determining enriched components. See |
quant |
Positive number between 0 and 1. Upper and lower fraction of gene loadings used for determining enriched components. See |
nComp |
Number of PCA- or ICA-components to use. Default is |
dimR |
logical. If |
mode |
|
logscale |
logical. If |
FSelect |
logical. If |
Value
The function returns an updated SCseq
object with the principal or independent component matrix written to the slot dimRed$x
of the SCseq
object. Additional information on the PCA or ICA is stored in slot dimRed
.
Examples
sc <- SCseq(intestinalDataSmall)
sc <- filterdata(sc)
sc <- CCcorrect(sc,dimR=TRUE,nComp=3)