BASiCS_VarThresholdSearchHVG_LVG: Detection method for highly and lowly variable genes using a...

In catavallejos/BASiCS: Bayesian Analysis of Single-Cell Sequencing data

Detection method for highly and lowly variable genes using a grid of variance contribution thresholds

Description

Detection method for highly and lowly variable genes using a grid of variance contribution thresholds. Only used when HVG/LVG are found based on the variance decomposition.

Usage

BASiCS_VarThresholdSearchVG(
  Chain,
  Task = c("HVG", "LVG"),
  VarThresholdsGrid,
  EFDR = 0.1,
  Progress = TRUE
)

BASiCS_VarThresholdSearchHVG(...)

BASiCS_VarThresholdSearchLVG(...)

Arguments

Chain	an object of class BASiCS_Chain
Task	See ?BASiCS_DetectVG.
VarThresholdsGrid	Grid of values for the variance contribution threshold (they must be contained in (0,1))
EFDR	Target for expected false discovery rate related to HVG/LVG detection. Default: EFDR = 0.10.
Progress	If Progress = TRUE, partial output is printed in the console. Default: Progress = TRUE.
...	Passed to methods.

Details

See vignette

Value

BASiCS_VarThresholdSearchHVG

A table displaying the results of highly variable genes detection for different variance contribution thresholds.

BASiCS_VarThresholdSearchLVG

A table displaying the results of lowly variable genes detection for different variance contribution thresholds.

Author(s)

Catalina A. Vallejos [e-mail]

References

Vallejos, Marioni and Richardson (2015). PLoS Computational Biology.

See Also

BASiCS_Chain

Examples

data(ChainSC)

BASiCS_VarThresholdSearchHVG(ChainSC,
                             VarThresholdsGrid = seq(0.55,0.65,by=0.01),
                             EFDR = 0.10)
BASiCS_VarThresholdSearchLVG(ChainSC,
                             VarThresholdsGrid = seq(0.35,0.45,by=0.01),
                             EFDR = 0.10)

catavallejos/BASiCS documentation built on March 27, 2024, 12:49 a.m.