smooth_via_pca: Smooth data by PCA

Description Usage Arguments Value Examples

View source: R/denoise.R

Description

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

Usage

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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

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

ChristophH/sctransform documentation built on Dec. 27, 2019, 8:02 a.m.