BASiCS_Filter: Filter for input datasets

Description

BASiCS_Filter indicates which transcripts and cells pass a pre-defined inclusion criteria.

Usage

BASiCS_Filter(Counts, Tech, SpikeInput, MinTotalCountsPerCell = 2,
  MinTotalCountsPerGene = 2, MinCellsWithExpression = 2,
  MinAvCountsPerCellsWithExpression = 2)

Arguments

Counts

Matrix of dimensions q times n whose elements corresponds to the simulated expression counts. First q.bio rows correspond to biological genes. Last q-q.bio rows correspond to technical spike-in genes.

Tech

Logical vector of length q. If Tech = F the gene is biological; otherwise the gene is spike-in.

SpikeInput

Vector of length q-q.bio whose elements indicate the simulated input concentrations for the spike-in genes.

MinTotalCountsPerCell

Minimum value of total expression counts required per cell (biological and technical)

MinTotalCountsPerGene

Minimum value of total expression counts required per transcript (biological and technical)

MinCellsWithExpression

Minimum number of cells where expression must be detected (positive count). Criteria applied to each transcript.

MinAvCountsPerCellsWithExpression

Minimum average number of counts per cells where expression is detected. Criteria applied to each transcript.

Value

A list of 2 elements

Counts: Filtered matrix of expression counts
Tech: Filtered vector of spike-in indicators
SpikeInput: Filtered vector of spike-in genes input molecules
IncludeGenes: Inclusion indicators for transcripts
IncludeCells: Inclusion indicators for cells

References

Vallejos, Marioni and Richardson (2015). Bayesian Analysis of Single-Cell Sequencing data.

Examples

Run this code

# NOT RUN {
Counts = matrix(rpois(50*10, 2), ncol = 10)
Tech = c(rep(FALSE,40),rep(TRUE,10))
SpikeInput = rgamma(10,1,1)
Filter = BASiCS_Filter(Counts, Tech, SpikeInput)

FilterData = newBASiCS_Data(Filter$Counts, Filter$Tech, Filter$SpikeInput)
# }