scran::SuggestAdtQcFilters Class Reference

Create filters to identify low-quality cells from ADT-derived QC metrics. More...

#include <SuggestAdtQcFilters.hpp>

Classes
struct	Defaults
	Default parameters. More...
struct	Thresholds
	Thresholds to define outliers on each metric. More...

Public Member Functions
SuggestAdtQcFilters &	set_detected_num_mads (double n=Defaults::num_mads)
SuggestAdtQcFilters &	set_subset_num_mads (double n=Defaults::num_mads)
SuggestAdtQcFilters &	set_num_mads (double n=Defaults::num_mads)
SuggestAdtQcFilters &	set_min_detected_drop (double m=Defaults::min_detected_drop)
template<typename Float , typename Integer >
Thresholds	run (size_t n, const PerCellAdtQcMetrics::Buffers< Float, Integer > &buffers) const
Thresholds	run (const PerCellAdtQcMetrics::Results &metrics) const
template<typename Block , typename Float , typename Integer >
Thresholds	run_blocked (size_t n, const Block *block, const PerCellAdtQcMetrics::Buffers< Float, Integer > &buffers) const
template<typename Block >
Thresholds	run_blocked (const PerCellAdtQcMetrics::Results &metrics, const Block *block) const

Detailed Description

Create filters to identify low-quality cells from ADT-derived QC metrics.

In antibody-derived tag (ADT) count matrices, the QC filtering decisions are slightly different than those for RNA count matrices (see SuggestAdtQcFilters for the latter). Here, low-quality cells are defined as those with:

• Low numbers of detected features, which indicates that library preparation or sequencing depth was suboptimal. Even in ADT data, we should expect to detect many features in a cell due to ambient contamination.
• High total counts in the isotype control (IgG) subsets. The IgG antibodies should not bind to anything, so high coverage is indicative of non-specific binding or antibody conjugates.

We define a threshold on each metric based on a certain number of MADs from the median. This assumes that most cells in the experiment are of high (or at least acceptable) quality; any outliers are indicative of low-quality cells that should be filtered out. See the ChooseOutlierFilters class for implementation details.

For the number of detected features and the total IgG counts, the outliers are defined after log-transformation of the metrics. This improves resolution at low values and ensures that the defined threshold is not negative. Note that all thresholds are still reported on the original scale, so no further exponentiation is required.

For the number of detected features, we supplement the MAD-based threshold with a minimum drop. Cells are only considered to be low quality if the difference in the number of detected features from the median is greater than a certain percentage. By default, the number must drop by at least 10% from the median. This avoids overly aggressive filtering when the MAD is zero due to the discrete nature of this statistic in datasets with few tags.

For datasets with multiple blocks, SuggestAdtQcFilters::run_blocked() will compute block-specific thresholds for each metric. See comments in SuggestRnaQcFilters for more details.

Member Function Documentation

set_detected_num_mads()

SuggestAdtQcFilters & scran::SuggestAdtQcFilters::set_detected_num_mads ( double  n = Defaults::num_mads )

inline

Parameters

n	Number of MADs below the median, to define the threshold for outliers in the number of detected features. This should be non-negative.

Returns

Reference to this SuggestAdtQcFilters object.

set_subset_num_mads()

SuggestAdtQcFilters & scran::SuggestAdtQcFilters::set_subset_num_mads ( double  n = Defaults::num_mads )

inline

Parameters

n	Number of MADs above the median, to define the threshold for outliers in the total count for each subset. This should be non-negative.

Returns

Reference to this SuggestAdtQcFilters object.

set_num_mads()

SuggestAdtQcFilters & scran::SuggestAdtQcFilters::set_num_mads ( double  n = Defaults::num_mads )

inline

Parameters

n	Number of MADs from the median, overriding previous calls to set_detected_num_mads() and set_subset_num_mads(). This should be non-negative.

Returns

Reference to this SuggestAdtQcFilters object.

set_min_detected_drop()

SuggestAdtQcFilters & scran::SuggestAdtQcFilters::set_min_detected_drop ( double  m = Defaults::min_detected_drop )

inline

Parameters

m	Minimum drop in the number of detected features from the median, in order to consider a cell to be of low quality. This should lie in $[0, 1)$.

Returns

Reference to this SuggestAdtQcFilters object.

run() [1/2]

template<typename Float , typename Integer >

Thresholds scran::SuggestAdtQcFilters::run ( size_t  n, const PerCellAdtQcMetrics::Buffers< Float, Integer > &  buffers  ) const

inline

Template Parameters

Float	Floating point type for the metrics.
Integer	Integer for the metrics.

Parameters

	n	Number of cells.
[in]	buffers	Pointers to arrays of length n, containing the per-cell ADT-derived metrics.

Returns

Filtering thresholds for each metric.

run() [2/2]

Thresholds scran::SuggestAdtQcFilters::run ( const PerCellAdtQcMetrics::Results &  metrics ) const

inline

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

metrics

Collection of arrays of length equal to the number of cells, containing the per-cell ADT-derived metrics.

Returns

Filtering thresholds for each metric.

run_blocked() [1/2]

template<typename Block , typename Float , typename Integer >

Thresholds scran::SuggestAdtQcFilters::run_blocked ( size_t  n, const Block *  block, const PerCellAdtQcMetrics::Buffers< Float, Integer > &  buffers  ) const

inline

Template Parameters

Block	Integer type for the block assignments.
Float	Floating point type for the metrics.
Integer	Integer for the metrics.

Parameters

	n	Number of cells.
[in]	block	Pointer to an array of length n, containing the block assignments for each cell. This may be NULL, in which case all cells are assumed to belong to the same block.
[in]	buffers	Pointers to arrays of length n, containing the per-cell ADT-derived metrics. Only detected and subset_totals are used; sums does not need to be set.

Returns

Filtering thresholds for each metric in each block.

run_blocked() [2/2]

template<typename Block >

Thresholds scran::SuggestAdtQcFilters::run_blocked ( const PerCellAdtQcMetrics::Results &  metrics, const Block *  block  ) const

inline

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Template Parameters

Block

Integer type for the block assignments.

Parameters

	metrics	Collection of arrays of length equal to the number of cells, containing the per-cell ADT-derived metrics. Only detected and subset_totals are used; sums does not need to be set.
[in]	block	Pointer to an array of length equal to the number of cells, containing the block assignments for each cell. This may be NULL, in which case all cells are assumed to belong to the same block.

Returns

Filtering thresholds for each metric in each block.

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

• scran/quality_control/SuggestAdtQcFilters.hpp