scran::SimplePca Class Reference

Perform a simple PCA on a gene-cell matrix. More...

#include <SimplePca.hpp>

Classes
struct	Defaults
	Default parameter settings. More...
struct	Results
	Container for the PCA results. More...

Public Member Functions
SimplePca &	set_rank (int r=Defaults::rank)
SimplePca &	set_scale (bool s=Defaults::scale)
SimplePca &	set_transpose (bool t=Defaults::transpose)
SimplePca &	set_return_rotation (bool r=Defaults::return_rotation)
SimplePca &	set_return_center (bool r=Defaults::return_center)
SimplePca &	set_return_scale (bool r=Defaults::return_scale)
SimplePca &	set_num_threads (int n=Defaults::num_threads)
template<typename T , typename IDX >
Results	run (const tatami::Matrix< T, IDX > *mat) const
template<typename T , typename IDX , typename X >
Results	run (const tatami::Matrix< T, IDX > *mat, const X *features) const

Detailed Description

Perform a simple PCA on a gene-cell matrix.

Principal components analysis (PCA) is a helpful technique for data compression and denoising. The idea is that the earlier PCs capture most of the systematic biological variation while the later PCs capture random technical noise. Thus, we can reduce the size of the data and eliminate noise by only using the earlier PCs for further analyses. Most practitioners will keep the first 10-50 PCs, though the exact choice is fairly arbitrary. For speed, we use the CppIrlba package to perform an approximate PCA.

Member Function Documentation

set_rank()

SimplePca & scran::SimplePca::set_rank ( int  r = Defaults::rank )

inline

Parameters

r	Number of PCs to compute. This should be no greater than the maximum number of PCs, i.e., the smaller dimension of the input matrix; otherwise, only the maximum number of PCs will be reported in the Results.

Returns

A reference to this SimplePca instance.

set_scale()

SimplePca & scran::SimplePca::set_scale ( bool  s = Defaults::scale )

inline

Parameters

s	Should genes be scaled to unit variance?

Returns

A reference to this SimplePca instance.

set_transpose()

SimplePca & scran::SimplePca::set_transpose ( bool  t = Defaults::transpose )

inline

Parameters

t	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.

Returns

A reference to this SimplePca instance.

set_return_rotation()

SimplePca & scran::SimplePca::set_return_rotation ( bool  r = Defaults::return_rotation )

inline

Parameters

r	Should the rotation matrix be returned in the output?

Returns

A reference to this SimplePca instance.

set_return_center()

SimplePca & scran::SimplePca::set_return_center ( bool  r = Defaults::return_center )

inline

Parameters

r	Should the center vector be returned in the output?

Returns

A reference to this SimplePca instance.

set_return_scale()

SimplePca & scran::SimplePca::set_return_scale ( bool  r = Defaults::return_scale )

inline

Parameters

r	Should the scale vector be returned in the output?

Returns

A reference to this SimplePca instance.

set_num_threads()

SimplePca & scran::SimplePca::set_num_threads ( int  n = Defaults::num_threads )

inline

Parameters

n	Number of threads to use.

Returns

A reference to this SimplePca instance.

run() [1/2]

template<typename T , typename IDX >

Results scran::SimplePca::run ( const tatami::Matrix< T, IDX > *  mat ) const

inline

Run PCA on an input gene-by-cell matrix.

Template Parameters

T	Floating point type for the data.
IDX	Integer type for the indices.

Parameters

[in]

mat

Pointer to the input matrix. Columns should contain cells while rows should contain genes.

Returns

A Results object containing the PCs and the variance explained.

run() [2/2]

template<typename T , typename IDX , typename X >

Results scran::SimplePca::run ( const tatami::Matrix< T, IDX > *  mat, const X *  features  ) const

inline

Run PCA on an input gene-by-cell matrix after filtering for genes of interest. We typically use the set of highly variable genes from ChooseHVGs, with the aim being to improve computational efficiency and avoid random noise by removing lowly variable genes.

Template Parameters

T	Floating point type for the data.
IDX	Integer type for the indices.
X	Integer type for the feature filter.

Parameters

[in]	mat	Pointer to the input matrix. Columns should contain cells while rows should contain genes.
[in]	features	Pointer to an array of length equal to the number of genes. Each entry treated as a boolean specifying whether the corresponding genes should be used in the PCA.

Returns

A Results object containing the PCs and the variance explained.

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

• scran/dimensionality_reduction/SimplePca.hpp