smooth_via_pca: Smooth data by PCA

In sctransform: Variance Stabilizing Transformations for Single Cell UMI Data

Smooth data by PCA

Description

Perform PCA, identify significant dimensions, and reverse the rotation using only significant dimensions.

Usage

smooth_via_pca(
  x,
  elbow_th = 0.025,
  dims_use = NULL,
  max_pc = 100,
  do_plot = FALSE,
  scale. = FALSE
)

Arguments

x	A data matrix with genes as rows and cells as columns
elbow_th	The fraction of PC sdev drop that is considered significant; low values will lead to more PCs being used
dims_use	Directly specify PCs to use, e.g. 1:10
max_pc	Maximum number of PCs computed
do_plot	Plot PC sdev and sdev drop
scale.	Boolean indicating whether genes should be divided by standard deviation after centering and prior to PCA

Value

Smoothed data

Examples

vst_out <- vst(pbmc)
y_smooth <- smooth_via_pca(vst_out$y, do_plot = TRUE)

sctransform documentation built on Oct. 19, 2023, 9:08 a.m.