scran::SimplePca::Results Struct Reference

Container for the PCA results. More...

#include <SimplePca.hpp>

Public Attributes
Eigen::MatrixXd	pcs
Eigen::VectorXd	variance_explained
double	total_variance = 0
Eigen::MatrixXd	rotation
Eigen::VectorXd	center
Eigen::VectorXd	scale

Detailed Description

Container for the PCA results.

Instances should be constructed by the SimplePca::run() methods.

Member Data Documentation

pcs

Eigen::MatrixXd scran::SimplePca::Results::pcs

Matrix of principal components. By default, each row corresponds to a PC while each column corresponds to a cell in the input matrix. If set_transpose() is set to false, rows are cells instead. The number of PCs is determined by set_rank().

variance_explained

Eigen::VectorXd scran::SimplePca::Results::variance_explained

Variance explained by each PC. Each entry corresponds to a column in pcs and is in decreasing order.

total_variance

double scran::SimplePca::Results::total_variance = 0

Total variance of the dataset (possibly after scaling, if set_scale() is set to true). This can be used to divide variance_explained to obtain the percentage of variance explained.

rotation

Eigen::MatrixXd scran::SimplePca::Results::rotation

Rotation matrix, only returned if SimplePca::set_return_rotation() is true. Each row corresponds to a feature while each column corresponds to a PC. The number of PCs is determined by set_rank(). If feature filtering was performed, the number of rows is equal to the number of features remaining after filtering.

center

Eigen::VectorXd scran::SimplePca::Results::center

Centering vector, only returned if SimplePca::set_return_center() is true. Each entry corresponds to a row in the matrix and contains the mean value for that feature. If feature filtering was performed, the length is equal to the number of features remaining after filtering.

scale

Eigen::VectorXd scran::SimplePca::Results::scale

Scaling vector, only returned if SimplePca::set_return_center() is true. Each entry corresponds to a row in the matrix and contains the scaling factor used to divide the feature values if SimplePca::set_scale() is true. If feature filtering was performed, the length is equal to the number of features remaining after filtering.

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

• scran/dimensionality_reduction/SimplePca.hpp