runDecontX {singleCellTK} | R Documentation

## Detecting contamination with DecontX.

### Description

A wrapper function for decontX. Identify potential contamination from experimental factors such as ambient RNA.

### Usage

    runDecontX(
      inSCE,
      sample = NULL,
      useAssay = "counts",
      z = NULL,
      maxIter = 500,
      delta = c(10, 10),
      estimateDelta = TRUE,
      convergence = 0.001,
      iterLogLik = 10,
      varGenes = 5000,
      dbscanEps = 1,
      seed = 12345,
      logfile = NULL,
      verbose = TRUE
    )

### Arguments

`inSCE` | A SingleCellExperiment object.
`sample` | Character vector. Indicates which sample each cell belongs to. Default NULL. decontX will be run on cells from each sample separately.
`useAssay` | A string specifying which assay in the SCE to use. Default 'counts'.
`z` | Numeric or character vector. Cell cluster labels. If NULL, PCA will be used to reduce the dimensionality of the dataset initially, 'umap' from the 'uwot' package will be used to further reduce the dataset to 2 dimenions and the 'dbscan' function from the 'dbscan' package will be used to identify clusters of broad cell types. Default NULL.
`maxIter` | Integer. Maximum iterations of the EM algorithm. Default 500.
`delta` | Numeric Vector of length 2. Concentration parameters for the Dirichlet prior for the contamination in each cell. The first element is the prior for the native counts while the second element is the prior for the contamination counts. These essentially act as pseudocounts for the native and contamination in each cell. If `estimateDelta = TRUE`, this is only used to produce a random sample of proportions for an initial value of contamination in each cell. Then `fit_dirichlet` is used to update `delta` in each iteration. If `estimateDelta = FALSE`, then `delta` is fixed with these values for the entire inference procedure. Fixing `delta` and setting a high number in the second element will force `decontX` to be more aggressive and estimate higher levels of contamination at the expense of potentially removing native expression. Default `c(10, 10)`.
`estimateDelta` | Boolean. Whether to update `delta` at each iteration.
`convergence` | Numeric. The EM algorithm will be stopped if the maximum difference in the contamination estimates between the previous and current iterations is less than this. Default 0.001.
`iterLogLik` | Integer. Calculate log likelihood every `iterLogLik` iteration. Default 10.
`varGenes` | Integer. The number of variable genes to use in dimensionality reduction before clustering. Variability is calcualted using `modelGeneVar` function from the 'scran' package. Used only when z is not provided. Default 5000.
`dbscanEps` | Numeric. The clustering resolution parameter used in 'dbscan' to estimate broad cell clusters. Used only when z is not provided. Default 1.
`seed` | Integer. Passed to with_seed. For reproducibility, a default value of 12345 is used. If NULL, no calls to with_seed are made.
`logfile` | Character. Messages will be redirected to a file named 'logfile'. If NULL, messages will be printed to stdout. Default NULL.
`verbose` | Logical. Whether to print log messages. Default TRUE.

### Value

A SingleCellExperiment object with 'decontX_Contamination' and 'decontX_Clusters' added to the colData slot. Additionally, the decontaminated counts will be added as an assay called 'decontXCounts'.

### Examples

    data(scExample, package = "singleCellTK")
    sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
    sce <- runDecontX(sce)

[Package singleCellTK version 2.0.0 Index]