plot_pc_variance_explained {monocle} | R Documentation

## 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.

### 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)

[Package monocle version 2.18.0 Index]