Takes a pre-computed dimensional reduction (typically calculated on a subset of genes) and projects this onto the entire dataset (all genes). Note that the cell loadings will remain unchanged, but now there are gene loadings for all genes.
Usage
ProjectDim(
object,
reduction = "pca",
assay = NULL,
dims.print = 1:5,
nfeatures.print = 20,
overwrite = FALSE,
do.center = FALSE,
verbose = TRUE
)Arguments
- object
Seurat object
- reduction
Reduction to use
- assay
Assay to use
- dims.print
Number of dims to print features for
- nfeatures.print
Number of features with highest/lowest loadings to print for each dimension
- overwrite
Replace the existing data in feature.loadings
- do.center
Center the dataset prior to projection (should be set to TRUE)
- verbose
Print top genes associated with the projected dimensions
Examples
data("pbmc_small")
pbmc_small
pbmc_small <- ProjectDim(object = pbmc_small, reduction = "pca")
# Visualize top projected genes in heatmap
DimHeatmap(object = pbmc_small, reduction = "pca", dims = 1, balanced = TRUE)