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
std::optional< EigenVector_ >	scale
irlba::Metrics	metrics

Detailed Description

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

Results of simple_pca().

Template Parameters

EigenMatrix_	A floating-point column-major 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 gene (i.e., row of the input matrix) and contains the mean value of that gene. If the input matrix has no cells, the mean is set to zero for all genes.

components

template<typename EigenMatrix_ , typename EigenVector_ >

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

Matrix of principal component scores. 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 the smaller of SimplePcaOptions::number and min(NR, NC) - 1, where NR and NC are the number of rows and columns, respectively, of the input matrix.

metrics

template<typename EigenMatrix_ , typename EigenVector_ >

irlba::Metrics scran_pca::SimplePcaResults< EigenMatrix_, EigenVector_ >::metrics

Metrics for IRLBA, including whether the algorithm converged and the number of iterations/multiplications required.

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 as described for SimplePcaResults::components.

scale

template<typename EigenMatrix_ , typename EigenVector_ >

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

Scaling vector, only returned if SimplePcaOptions::scale = true. Each entry corresponds to a gene (i.e., row of the input matrix) and contains the scaling factor used to divide the feature values if SimplePcaOptions::scale = true. This is usually the sample standard deviation of that gene. For genes with zero variance, the scaling factor is set to 1 to avoid non-finite values upon scaling. For input matrices with fewer than 2 cells, the scaling factor is set to 1 for all genes.

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. The number of PCs is as described for SimplePcaResults::components.

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

• scran_pca/simple_pca.hpp