decontX {celda} | R Documentation

## Contamination estimation with decontX

### Description

Identifies contamination from factors such as ambient RNA in single cell genomic datasets.

### Usage

    decontX(x, ...)

    ## S4 method for signature 'SingleCellExperiment'
    decontX(
      x,
      assayName = "counts",
      z = NULL,
      batch = NULL,
      background = NULL,
      bgAssayName = NULL,
      maxIter = 500,
      delta = c(10, 10),
      estimateDelta = TRUE,
      convergence = 0.001,
      iterLogLik = 10,
      varGenes = 5000,
      dbscanEps = 1,
      seed = 12345,
      logfile = NULL,
      verbose = TRUE
    )

    ## S4 method for signature 'ANY'
    decontX(
      x,
      z = NULL,
      batch = NULL,
      background = 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

`x` | A numeric matrix of counts or a SingleCellExperiment with the matrix located in the assay slot under `assayName`. Cells in each batch will be subsetted and converted to a sparse matrix of class `dgCMatrix` from package Matrix before analysis. This object should only contain filtered cells after cell calling. Empty cell barcodes (low expression droplets before cell calling) are not needed to run DecontX.
`...` | For the generic, further arguments to pass to each method.
`assayName` | Character. Name of the assay to use if `x` is a SingleCellExperiment.
`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.
`batch` | Numeric or character vector. Batch labels for cells. If batch labels are supplied, DecontX is run on cells from each batch separately. Cells run in different channels or assays should be considered different batches. Default NULL.
`background` | A numeric matrix of counts or a SingleCellExperiment with the matrix located in the assay slot under `assayName`. It should have the same structure as `x` except it contains the matrix of empty droplets instead of cells. When supplied, empirical distribution of transcripts from these empty droplets will be used as the contamination distribution. Default NULL.
`bgAssayName` | Character. Name of the assay to use if `background` is a SingleCellExperiment. Default to same as `assayName`.
`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

If `x` is a matrix-like object, a list will be returned with the following items:

`decontXcounts`:

  The decontaminated matrix. Values obtained from the variational inference procedure may be non-integer. However, integer counts can be obtained by rounding, e.g. `round(decontXcounts)`.

`contamination`:

  Percentage of contamination in each cell.

`estimates`:

  List of estimated parameters for each batch. If z was not supplied, then the UMAP coordinates used to generated cell cluster labels will also be stored here.

`z`:

  Cell population/cluster labels used for analysis.

`runParams`:

  List of arguments used in the function call.

If `x` is a SingleCellExperiment, then the decontaminated counts will be stored as an assay and can be accessed with `decontXcounts(x)`. The contamination values and cluster labels will be stored in `colData(x)`. `estimates` and `runParams` will be stored in `metadata(x)$decontX`. The UMAPs used to generated cell cluster labels will be stored in `reducedDims` slot in `x`.

### Author(s)

Shiyi Yang, Yuan Yin, Joshua Campbell

### Examples

    # Generate matrix with contamination
    s <- simulateContamination(seed = 12345)

    library(SingleCellExperiment)
    sce <- SingleCellExperiment(list(counts = s$observedCounts))
    sce <- decontX(sce)

    # Plot contamination on UMAP
    plotDecontXContamination(sce)

    # Plot decontX cluster labels
    umap <- reducedDim(sce)
    plotDimReduceCluster(x = sce$decontX_clusters,
        dim1 = umap[, 1], dim2 = umap[, 2], )

    # Plot percentage of marker genes detected
    # in each cell cluster before decontamination
    s$markers
    plotDecontXMarkerPercentage(sce, markers = s$markers, assayName = "counts")

    # Plot percentage of marker genes detected
    # in each cell cluster after contamination
    plotDecontXMarkerPercentage(sce, markers = s$markers,
                                assayName = "decontXcounts")

    # Plot percentage of marker genes detected in each cell
    # comparing original and decontaminated counts side-by-side
    plotDecontXMarkerPercentage(sce, markers = s$markers,
                                assayName = c("counts", "decontXcounts"))

    # Plot raw counts of indiviual markers genes before
    # and after decontamination
    plotDecontXMarkerExpression(sce, unlist(s$markers))

[Package celda version 1.8.1 Index]