Description Usage Arguments Value See Also Examples
Runs a canonical correlation analysis using a diagonal implementation of CCA.
For details about stored CCA calculation parameters, see
PrintCCAParams
.
1 2 3 4 5 6 7 8 9 10 11 | RunCCA(object1, object2, ...)
## Default S3 method:
RunCCA(object1, object2, standardize = TRUE,
num.cc = 20, verbose = FALSE, use.cpp = TRUE, ...)
## 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, use.cpp = TRUE, ...)
|
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 |
verbose |
... |
use.cpp |
... |
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 ... |
Returns a combined Seurat object with the CCA results stored.
1 2 3 4 5 6 7 8 9 | pbmc_small
# As CCA requires two datasets, we will split our test object into two just for this example
pbmc1 <- SubsetData(pbmc_small, cells = colnames(x = pbmc_small)[1:40])
pbmc2 <- SubsetData(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"]])
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