Options for simple_pca(). More...

#include <simple_pca.hpp>

Public Attributes
int	number = 25

bool	scale = false

bool	transpose = true

bool	realize_matrix = true

int	num_threads = 1

irlba::Options	irlba_options

Detailed Description

Options for simple_pca().

Member Data Documentation

◆ irlba_options

irlba::Options scran_pca::SimplePcaOptions::irlba_options

Further options to pass to irlba::compute().

◆ num_threads

int scran_pca::SimplePcaOptions::num_threads = 1

Number of threads to use. The parallelization scheme is determined by tatami::parallelize() and irlba::parallelize().

◆ number

int scran_pca::SimplePcaOptions::number = 25

Number of the top principal components (PCs) to compute. Retaining more PCs will capture more biological signal at the cost of increasing noise and compute time. If this is greater than the maximum number of PCs (i.e., the smaller dimension of the input matrix), only the maximum number of PCs will be reported in the results.

◆ realize_matrix

bool scran_pca::SimplePcaOptions::realize_matrix = true

Whether to realize tatami::Matrix objects into an appropriate in-memory format before PCA. This is typically faster but increases memory usage.

◆ scale

bool scran_pca::SimplePcaOptions::scale = false

Should genes be scaled to unit variance? This ensures that each gene contributes equally to the PCA, favoring consistent variation across many genes rather than large variation in a few genes. Genes with zero variance are ignored.

◆ transpose

bool scran_pca::SimplePcaOptions::transpose = true

Should the PC matrix be transposed on output? If true, the output matrix is column-major with cells in the columns, which is compatible with downstream libscran steps.

The documentation for this struct was generated from the following file:

scran_pca/simple_pca.hpp

Public Attributes