BASiCS_DetectHVG {BASiCS}R Documentation

Detection method for highly (HVG) and lowly (LVG) variable genes

Description

Functions to detect highly and lowly variable genes. If the BASiCS_Chain object was generated using the regression approach, BASiCS finds the top highly variable genes based on the posteriors of the epsilon parameters. Otherwise, the old approach is used, which initially performs a variance decomposition.

Usage

BASiCS_DetectHVG(Chain, PercentileThreshold = 0.9, VarThreshold = NULL,
  ProbThreshold = NULL, EFDR = 0.1, OrderVariable = "Prob",
  Plot = FALSE, ...)

BASiCS_DetectLVG(Chain, PercentileThreshold = 0.1, VarThreshold = NULL,
  ProbThreshold = NULL, EFDR = 0.1, OrderVariable = "Prob",
  Plot = FALSE, ...)

Arguments

Chain

an object of class BASiCS_Chain

PercentileThreshold

Threshold to detect a percentile of variable genes (must be a positive value, between 0 and 1). Defaults: 0.9 for HVG (top 10 percent), 0.1 for LVG (bottom 10 percent)

VarThreshold

Variance contribution threshold (must be a positive value, between 0 and 1). This is only used when the BASiCS non-regression model was used to generate the Chain object.

ProbThreshold

Optional parameter. Posterior probability threshold (must be a positive value, between 0 and 1)

EFDR

Target for expected false discovery rate related to HVG/LVG detection (default = 0.10)

OrderVariable

Ordering variable for output. Possible values: 'GeneIndex', 'GeneName' and 'Prob'.

Plot

If Plot = TRUE error control and expression versus HVG/LVG probability plots are generated

...

Graphical parameters (see par).

Details

See vignette

Value

BASiCS_DetectHVG returns a list of 4 elements:

Table

Matrix whose columns can contain

GeneIndex

Vector of length q.bio. Gene index as in the order present in the analysed SingleCellExperiment

GeneName

Vector of length q.bio. Gene name as in the order present in the analysed SingleCellExperiment

Mu

Vector of length q.bio. For each biological gene, posterior median of gene-specific mean expression parameters μ_i

Delta

Vector of length q.bio. For each biological gene, posterior median of gene-specific biological over-dispersion parameter δ_i

Sigma

Vector of length q.bio. For each biological gene, proportion of the total variability that is due to a biological heterogeneity component.

Epsilon

Vector of length q.bio. For each biological gene, posterior median of gene-specific residual over-dispersion parameter ε_i.

Prob

Vector of length q.bio. For each biological gene, probability of being highly variable according to the given thresholds.

HVG

Vector of length q.bio. For each biological gene, indicator of being detected as highly variable according to the given thresholds.

LVG

Vector of length q.bio. For each biological gene, indicator of being detected as lowly variable according to the given thresholds.

ProbThreshold

Posterior probability threshold.

EFDR

Expected false discovery rate for the given thresholds.

EFNR

Expected false negative rate for the given thresholds.

Author(s)

Catalina A. Vallejos cnvallej@uc.cl

Nils Eling eling@ebi.ac.uk

References

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

See Also

BASiCS_Chain

Examples


# Loads short example chain (non-regression implementation)
data(ChainSC)

# Highly and lowly variable genes detection (within a single group of cells)
DetectHVG <- BASiCS_DetectHVG(ChainSC, VarThreshold = 0.60,
                              EFDR = 0.10, Plot = TRUE)
DetectLVG <- BASiCS_DetectLVG(ChainSC, VarThreshold = 0.40,
                              EFDR = 0.10, Plot = TRUE)
                              
# Loads short example chain (regression implementation)
data(ChainSCReg)

# Highly and lowly variable genes detection (within a single group of cells)
DetectHVG <- BASiCS_DetectHVG(ChainSCReg, PercentileThreshold = 0.90,
                              EFDR = 0.10, Plot = TRUE)
DetectLVG <- BASiCS_DetectLVG(ChainSCReg, PercentileThreshold = 0.10,
                              EFDR = 0.10, Plot = TRUE)


[Package BASiCS version 1.6.0 Index]