sctransform: smooth_via_pca – R documentation

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smooth_via_pca

Smooth data by PCA

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

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

`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

Smoothed data

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

Variance Stabilizing Transformations for Single Cell UMI Data

v0.3.2

GPL-3 | file LICENSE

Authors

Christoph Hafemeister [aut, cre] (<https://orcid.org/0000-0001-6365-8254>)

Initial release