plot_pc_variance_explained: Plots the percentage of variance explained by the each...

In monocle: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq

Description

Plots the percentage of variance explained by the each component based on PCA from the normalized expression data using the same procedure used in reduceDimension function.

Usage

plot_pc_variance_explained(cds, max_components = 100, norm_method = c("log",
  "vstExprs", "none"), residualModelFormulaStr = NULL, pseudo_expr = NULL,
  return_all = F, use_existing_pc_variance = FALSE, verbose = FALSE, ...)

Arguments

cds	CellDataSet for the experiment after running reduceDimension with reduction_method as tSNE
max_components	Maximum number of components shown in the scree plot (variance explained by each component)
norm_method	Determines how to transform expression values prior to reducing dimensionality
residualModelFormulaStr	A model formula specifying the effects to subtract from the data before clustering.
pseudo_expr	amount to increase expression values before dimensionality reduction
return_all	A logical argument to determine whether or not the variance of each component is returned
use_existing_pc_variance	Whether to plot existing results for variance explained by each PC
verbose	Whether to emit verbose output during dimensionality reduction
...	additional arguments to pass to the dimensionality reduction function

Examples

## Not run: 
library(HSMMSingleCell)
HSMM <- load_HSMM()
plot_pc_variance_explained(HSMM)

## End(Not run)

monocle documentation built on Nov. 8, 2020, 5:06 p.m.