ProjectDim: Project Dimensional reduction onto full dataset

In lambdamoses/SeuratBasics: Slimmed down version of Seurat

Description

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

Value

Returns Seurat object with the projected values

Examples

pbmc_small
pbmc_small <- ProjectDim(object = pbmc_small, reduction = "pca")
# Vizualize top projected genes in heatmap
DimHeatmap(object = pbmc_small, reduction = "pca", dims = 1, balanced = TRUE)

lambdamoses/SeuratBasics documentation built on May 6, 2020, 9:32 a.m.