Seurat: RunCCA – R documentation

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RunCCA

Perform Canonical Correlation Analysis

Description

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

Usage

RunCCA(object1, object2, ...)

## Default S3 method:
RunCCA(
  object1,
  object2,
  standardize = TRUE,
  num.cc = 20,
  seed.use = 42,
  verbose = FALSE,
  ...
)

## S3 method for class 'Seurat'
RunCCA(
  object1,
  object2,
  assay1 = NULL,
  assay2 = NULL,
  num.cc = 20,
  features = NULL,
  renormalize = FALSE,
  rescale = FALSE,
  compute.gene.loadings = TRUE,
  add.cell.id1 = NULL,
  add.cell.id2 = NULL,
  verbose = TRUE,
  ...
)

Arguments

`object1`	First Seurat object
`object2`	Second Seurat object.
`...`	Extra parameters (passed onto MergeSeurat in case with two objects passed, passed onto ScaleData in case with single object and rescale.groups set to TRUE)
`standardize`	Standardize matrices - scales columns to have unit variance and mean 0
`num.cc`	Number of canonical vectors to calculate
`seed.use`	Random seed to set. If NULL, does not set a seed
`verbose`	Show progress messages
`assay1, assay2`	Assays to pull from in the first and second objects, respectively
`features`	Set of genes to use in CCA. Default is the union of both the variable features sets present in both objects.
`renormalize`	Renormalize raw data after merging the objects. If FALSE, merge the data matrices also.
`rescale`	Rescale the datasets prior to CCA. If FALSE, uses existing data in the scale data slots.
`compute.gene.loadings`	Also compute the gene loadings. NOTE - this will scale every gene in the dataset which may impose a high memory cost.
`add.cell.id1, add.cell.id2`	Add ...

Value

Returns a combined Seurat object with the CCA results stored.

Examples

data("pbmc_small")
pbmc_small
# As CCA requires two datasets, we will split our test object into two just for this example
pbmc1 <- subset(pbmc_small, cells = colnames(pbmc_small)[1:40])
pbmc2 <- subset(pbmc_small, cells = colnames(x = pbmc_small)[41:80])
pbmc1[["group"]] <- "group1"
pbmc2[["group"]] <- "group2"
pbmc_cca <- RunCCA(object1 = pbmc1, object2 = pbmc2)
# Print results
print(x = pbmc_cca[["cca"]])

Seurat

Tools for Single Cell Genomics

v4.0.1

GPL-3 | file LICENSE

Authors

Andrew Butler [ctb] (<https://orcid.org/0000-0003-3608-0463>), Saket Choudhary [ctb] (<https://orcid.org/0000-0001-5202-7633>), Charlotte Darby [ctb] (<https://orcid.org/0000-0003-2195-5300>), Jeff Farrell [ctb], Christoph Hafemeister [ctb] (<https://orcid.org/0000-0001-6365-8254>), Yuhan Hao [ctb] (<https://orcid.org/0000-0002-1810-0822>), Paul Hoffman [aut, cre] (<https://orcid.org/0000-0002-7693-8957>), Jaison Jain [ctb] (<https://orcid.org/0000-0002-9478-5018>), Efthymia Papalexi [ctb] (<https://orcid.org/0000-0001-5898-694X>), Patrick Roelli [ctb], Rahul Satija [ctb] (<https://orcid.org/0000-0001-9448-8833>), Karthik Shekhar [ctb], Avi Srivastava [ctb] (<https://orcid.org/0000-0001-9798-2079>), Tim Stuart [ctb] (<https://orcid.org/0000-0002-3044-0897>), Kristof Torkenczy [ctb] (<https://orcid.org/0000-0002-4869-7957>), Shiwei Zheng [ctb] (<https://orcid.org/0000-0001-6682-6743>), Satija Lab and Collaborators [fnd]

Initial release

2021-03-17