scran_aggregate Namespace Reference

Aggregate single-cell expression values. More...

Classes
struct	AggregateAcrossCellsBuffers
	Buffers for aggregate_across_cells(). More...
struct	AggregateAcrossCellsOptions
	Options for aggregate_across_cells(). More...
struct	AggregateAcrossCellsResults
	Results of aggregate_across_cells(). More...
struct	AggregateAcrossGenesBuffers
	Buffers for aggregate_across_genes(). More...
struct	AggregateAcrossGenesOptions
	Options for aggregate_across_genes(). More...
struct	AggregateAcrossGenesResults
	Results of aggregate_across_genes(). More...

Functions
template<typename Data_ , typename Index_ , typename Group_ , typename Sum_ , typename Detected_ >
void	aggregate_across_cells (const tatami::Matrix< Data_, Index_ > &input, const Group_ *const group, const AggregateAcrossCellsBuffers< Sum_, Detected_ > &buffers, const AggregateAcrossCellsOptions &options)
template<typename Sum_ = double, typename Detected_ = int, typename Data_ , typename Index_ , typename Group_ >
AggregateAcrossCellsResults< Sum_, Detected_ >	aggregate_across_cells (const tatami::Matrix< Data_, Index_ > &input, const Group_ *const group, const AggregateAcrossCellsOptions &options)
template<typename Data_ , typename Index_ , typename Gene_ , typename Weight_ , typename Sum_ >
void	aggregate_across_genes (const tatami::Matrix< Data_, Index_ > &input, const std::vector< std::tuple< std::size_t, const Gene_ *, const Weight_ * > > &gene_sets, const AggregateAcrossGenesBuffers< Sum_ > &buffers, const AggregateAcrossGenesOptions &options)
template<typename Sum_ = double, typename Data_ , typename Index_ , typename Gene_ , typename Weight_ >
AggregateAcrossGenesResults< Sum_ >	aggregate_across_genes (const tatami::Matrix< Data_, Index_ > &input, const std::vector< std::tuple< std::size_t, const Gene_ *, const Weight_ * > > &gene_sets, const AggregateAcrossGenesOptions &options)
template<typename Factor_ , typename Output_ >
std::vector< Factor_ >	clean_factor (const std::size_t n, const Factor_ *const factor, Output_ *const cleaned)
template<typename Factor_ , typename Combined_ >
std::vector< std::vector< Factor_ > >	combine_factors (const std::size_t n, const std::vector< const Factor_ * > &factors, Combined_ *const combined)
template<typename Factor_ , typename Number_ , typename Combined_ >
std::vector< std::vector< Factor_ > >	combine_factors_unused (const std::size_t n, const std::vector< std::pair< const Factor_ *, Number_ > > &factors, Combined_ *const combined)

Detailed Description

Aggregate single-cell expression values.

Function Documentation

aggregate_across_cells() [1/2]

template<typename Data_ , typename Index_ , typename Group_ , typename Sum_ , typename Detected_ >

void scran_aggregate::aggregate_across_cells	(	const tatami::Matrix< Data_, Index_ > &	input,
		const Group_ *const	group,
		const AggregateAcrossCellsBuffers< Sum_, Detected_ > &	buffers,
		const AggregateAcrossCellsOptions &	options )

Aggregate expression values across groups of cells for each gene. We report the sum of expression values and the number of cells with detected (i.e., positive) expression values in each group. This is typically used to create pseudo-bulk expression profiles for cluster/sample combinations. Expression values are generally expected to be counts so that the sums can be used as if they were counts from bulk data, e.g., for differential analyses with edgeR.

Template Parameters

Data_	Type of data in the input matrix, should be numeric.
Index_	Integer type of index in the input matrix.
Group_	Integer type of the group assignments.
Sum_	Numeric type of the sum, often the same as Data_.
Detected_	Numeric type (usually integer) of the number of detected cells.

Parameters

	input	The input matrix, usually containing non-negative counts. Rows are features and columns are cells.
[in]	group	Pointer to an array of length equal to the number of columns of input, containing the assigned group for each cell. All entries should be integers in \([0, N)\) where \(N\) is the number of unique groups.
	options	Further options.

aggregate_across_cells() [2/2]

template<typename Sum_ = double, typename Detected_ = int, typename Data_ , typename Index_ , typename Group_ >

AggregateAcrossCellsResults< Sum_, Detected_ > scran_aggregate::aggregate_across_cells	(	const tatami::Matrix< Data_, Index_ > &	input,
		const Group_ *const	group,
		const AggregateAcrossCellsOptions &	options )

Overload of aggregate_across_cells() that allocates memory for the results.

Template Parameters

Sum_	Numerict ype of the sum.
Detected_	Numeric type (usually integer) of the number of detected cells.
Data_	Type of data in the input matrix, should be numeric.
Index_	Integer type of index in the input matrix.
Group_	Integer type of the group assignments.

Parameters

	input	The input matrix, usually containing non-negative counts. Rows are features and columns are cells.
[in]	group	Pointer to an array of length equal to the number of columns of input, containing the assigned group for each cell. All entries should be integers in \([0, N)\) where \(N\) is the number of unique groups.
	options	Further options.

Returns

Results of the aggregation, where the available statistics depend on AggregateAcrossCellsOptions.

aggregate_across_genes() [1/2]

template<typename Data_ , typename Index_ , typename Gene_ , typename Weight_ , typename Sum_ >

void scran_aggregate::aggregate_across_genes	(	const tatami::Matrix< Data_, Index_ > &	input,
		const std::vector< std::tuple< std::size_t, const Gene_ *, const Weight_ * > > &	gene_sets,
		const AggregateAcrossGenesBuffers< Sum_ > &	buffers,
		const AggregateAcrossGenesOptions &	options )

Aggregate expression values across gene sets for each cell. This involves computing the sum/mean of expression values for any number of gene sets. The aim is to quantify the activity of signatures, pathways or regulons in each cell. Each gene in each set can also be weighted based on any a priori assumptions of their importance to the corresponding pathway.

Template Parameters

Data_	Type of data in the input matrix, should be numeric.
Index_	Integer type of index in the input matrix.
Gene_	Integer type of the indices of genes in each set.
Weight_	Floating-point type of the weights of genes in each set.
Sum_	Floating-point type of the sum.

Parameters

	input	Matrix of expression values where rows are features and columns are cells. This is usually normalized and possibly log-transformed, but the exact nature of the values depends on the application.
	gene_sets	Vector of gene sets. Each tuple corresponds to a set and contains (i) the number of genes in the set, (ii) a pointer to the row indices of the genes in the set, and (iii) a pointer to the weights of the genes in the set. The weight pointer may be NULL, in which case all weights are set to 1.
[out]	buffers	Collection of buffers in which to store the sum/mean for each gene set and cell.
	options	Further options.

aggregate_across_genes() [2/2]

template<typename Sum_ = double, typename Data_ , typename Index_ , typename Gene_ , typename Weight_ >

AggregateAcrossGenesResults< Sum_ > scran_aggregate::aggregate_across_genes	(	const tatami::Matrix< Data_, Index_ > &	input,
		const std::vector< std::tuple< std::size_t, const Gene_ *, const Weight_ * > > &	gene_sets,
		const AggregateAcrossGenesOptions &	options )

Overload of aggregate_across_genes() that allocates memory for the results.

Template Parameters

Sum_	Floating-point type of the sum.
Data_	Type of data in the input matrix, should be numeric.
Index_	Integer type of index in the input matrix.
Gene_	Integer type of the indices of genes in each set.
Weight_	Floating-point type of the weights of genes in each set.

Parameters

input	Matrix of expression values where rows are features and columns are cells.
gene_sets	Vector of gene sets. Each tuple corresponds to a set and contains (i) the number of genes in the set, (ii) a pointer to the row indices of the genes in the set, and (iii) a pointer to the weights of the genes in the set. The weight pointer may be NULL, in which case all weights are set to 1.
options	Further options.

Returns

Results of the aggregation.