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 = c("Prob", "GeneIndex", "GeneName"),
      Plot = FALSE,
      ...
    )

    BASiCS_DetectLVG(
      Chain,
      PercentileThreshold = 0.1,
      VarThreshold = NULL,
      ProbThreshold = NULL,
      EFDR = 0.1,
      OrderVariable = c("Prob", "GeneIndex", "GeneName"),
      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 1.8.1 Index]