scran_pca::SimplePcaResults< EigenMatrix_, EigenVector_ > Struct Template Reference

Results of simple_pca(). More...

#include <simple_pca.hpp>

Public Attributes
EigenMatrix_	components
EigenVector_	variance_explained
EigenVector_::Scalar	total_variance = 0
EigenMatrix_	rotation
EigenVector_	center
EigenVector_	scale
bool	converged = false

Detailed Description

template<typename EigenMatrix_, typename EigenVector_>
struct scran_pca::SimplePcaResults< EigenMatrix_, EigenVector_ >

Results of simple_pca().

Template Parameters

EigenMatrix_	A floating-point Eigen::Matrix class.
EigenVector_	A floating-point Eigen::Vector class.

Member Data Documentation

center

template<typename EigenMatrix_ , typename EigenVector_ >

EigenVector_ scran_pca::SimplePcaResults< EigenMatrix_, EigenVector_ >::center

Centering vector. Each entry corresponds to a row in the matrix and contains the mean value for that feature.

components

template<typename EigenMatrix_ , typename EigenVector_ >

EigenMatrix_ scran_pca::SimplePcaResults< EigenMatrix_, EigenVector_ >::components

Matrix of principal components. By default, each row corresponds to a PC while each column corresponds to a cell in the input matrix. If SimplePcaOptions::transpose = false, rows are cells instead. The number of PCs is determined by SimplePcaOptions::number.

converged

template<typename EigenMatrix_ , typename EigenVector_ >

bool scran_pca::SimplePcaResults< EigenMatrix_, EigenVector_ >::converged = false

Whether the algorithm converged.

rotation

template<typename EigenMatrix_ , typename EigenVector_ >

EigenMatrix_ scran_pca::SimplePcaResults< EigenMatrix_, EigenVector_ >::rotation

Rotation matrix. Each row corresponds to a feature while each column corresponds to a PC. The number of PCs is determined by SimplePcaOptions::number.

scale

template<typename EigenMatrix_ , typename EigenVector_ >

EigenVector_ scran_pca::SimplePcaResults< EigenMatrix_, EigenVector_ >::scale

Scaling vector, only returned if SimplePcaOptions::scale = true. Each entry corresponds to a row in the matrix and contains the scaling factor used to divide the feature values if SimplePcaOptions::scale = true.

total_variance

template<typename EigenMatrix_ , typename EigenVector_ >

EigenVector_::Scalar scran_pca::SimplePcaResults< EigenMatrix_, EigenVector_ >::total_variance = 0

Total variance of the dataset (possibly after scaling, if SimplePcaOptions::scale = true). This can be used to divide variance_explained to obtain the percentage of variance explained.

variance_explained

template<typename EigenMatrix_ , typename EigenVector_ >

EigenVector_ scran_pca::SimplePcaResults< EigenMatrix_, EigenVector_ >::variance_explained

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

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

• scran_pca/simple_pca.hpp