MultiModal_CCA: Run Canonical Correlation Analysis (CCA) on multimodal data

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

Description

CCA finds a shared correlation structure betwen two different datasets, enabling integrated downstream analysis

Usage

MultiModal_CCA(object, assay.1 = "RNA", assay.2 = "CITE",
  features.1 = NULL, features.2 = NULL, num.cc = 20,
  normalize.variance = TRUE)

Arguments

object	Seurat object
assay.1	First assay for multimodal analysis. Default is RNA
assay.2	Second assay for multimodal analysis. Default is CITE for CITE-Seq analysis.
features.1	Features of assay 1 to consider (default is variable genes)
features.2	Features of assay 2 to consider (default is all features, i.e. for CITE-Seq, all antibodies)
num.cc	Number of canonical correlations to compute and store. Default is 20, but will calculate less if either assay has <20 features.
normalize.variance	Z-score the embedding of each CC to 1, so each CC contributes equally in downstream analysis (default is T)

Value

Returns object after CCA, with results stored in dimensional reduction cca.assay1 (ie. cca.RNA) and cca.assay2. For example, results can be visualized using DimPlot(object,reduction.use="cca.RNA")

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