RunCCA: Perform Canonical Correlation Analysis

In mayer-lab/SeuratForMayer2018: Seurat : R Toolkit for Single Cell Genomics

Description

Runs a canonical correlation analysis using a diagonal implementation of CCA. For details about stored CCA calculation parameters, see PrintCCAParams.

Usage

RunCCA(object, object2, group1, group2, group.by, num.cc = 20, genes.use,
  scale.data = TRUE, rescale.groups = FALSE)

Arguments

object	Seurat object
object2	Optional second object. If object2 is passed, object1 will be considered as group1 and object2 as group2.
group1	First set of cells (or IDs) for CCA
group2	Second set of cells (or IDs) for CCA
group.by	Factor to group by (column vector stored in [e-mail])
num.cc	Number of canonical vectors to calculate
genes.use	Set of genes to use in CCA. Default is [e-mail]. If two objects are given, the default is the union of both variable gene sets that are also present in both objects.
scale.data	Use the scaled data from the object
rescale.groups	Rescale each set of cells independently

Value

Returns Seurat object with the CCA stored in the @dr$cca slot. If one object is passed, the same object is returned. If two are passed, a combined object is returned.

mayer-lab/SeuratForMayer2018 documentation built on May 25, 2019, 9:34 p.m.