RnaPcaState

RnaPcaState

This step performs a principal components analysis (PCA) to compact and denoise the data. The resulting PCs can be used as input to various per-cell analyses like clustering and dimensionality reduction. It wraps the runPca function from scran.js.

Methods not documented here are not part of the stable API and should not be used by applications.

Classes

RnaPcaState

Methods

compute(parameters)

Description:

  • This method should not be called directly by users, but is instead invoked by runAnalysis.

Source:
Parameters:
Name Type Description
parameters object

Parameter object, equivalent to the rna_pca property of the parameters of runAnalysis.

Properties
Name Type Description
num_pcs number

Number of PCs to return.
num_hvgs number

Number of highly variable genes (see FeatureSelectionState) to use in the PCA.
block_method string

Blocking method to use when dealing with multiple samples. This can be one of:

  • "none", in which case nothing is done using the sample information.
  • "regress", where linear regression is applied to remove mean differences between samples.
  • "project", where samples are weighted so that they contribute equally regardless of the number of cells.
Returns:

The object is updated with the new results.

fetchPCs() → {external:RunPCAResults}

Source:
Returns:

Results of the PCA on the normalized gene expression values.

Type
external:RunPCAResults

fetchParameters() → {object}

Source:
Returns:

Object containing the parameters.

Type
object