doubletRecovery {scran} | R Documentation

## Recover intra-sample doublets

### Description

Recover intra-sample doublets that are neighbors to known inter-sample doublets in a multiplexed experiment. This function is now deprecated, use `recoverDoublets` from scDblFinder instead.

### Usage

    doubletRecovery(x, ...)

    ## S4 method for signature 'ANY'
    doubletRecovery(
      x,
      doublets,
      samples,
      k = 50,
      transposed = FALSE,
      subset.row = NULL,
      BNPARAM = KmknnParam(),
      BPPARAM = SerialParam()
    )

    ## S4 method for signature 'SummarizedExperiment'
    doubletRecovery(x, ..., assay.type = "logcounts")

    ## S4 method for signature 'SingleCellExperiment'
    doubletRecovery(x, ..., use.dimred = NULL)

### Arguments

`x` | A log-expression matrix for all cells (including doublets) in columns and genes in rows. Alternatively, a SummarizedExperiment or SingleCellExperiment containing such a matrix. If `transposed=TRUE`, a matrix of low-dimensional coordinates where each row corresponds to a cell. This can also be in the `reducedDims` of a SingleCellExperiment if `use.dimred` is specified.
`...` | For the generic, additional arguments to pass to specific methods. For the SummarizedExperiment method, additional arguments to pass to the ANY method. For the SingleCellExperiment method, additional arguments to pass to the SummarizedExperiment method.
`doublets` | A logical, integer or character vector specifying which cells in `x` are known (inter-sample) doublets.
`samples` | A numeric vector containing the relative proportions of cells from each sample, used to determine how many cells are to be considered as intra-sample doublets.
`k` | Integer scalar specifying the number of nearest neighbors to use for computing the local doublet proportions.
`transposed` | Logical scalar indicating whether `x` is transposed, i.e., cells in the rows.
`subset.row` | See `?"scran-gene-selection"`, specifying the genes to use for the neighbor search. Only used when `transposed=FALSE`.
`BNPARAM` | A BiocNeighborParam object specifying the algorithm to use for the nearest neighbor search.
`BPPARAM` | A BiocParallelParam object specifying the parallelization to use for the nearest neighbor search.
`assay.type` | A string specifying which assay values contain the log-expression matrix.
`use.dimred` | A string specifying whether existing values in `reducedDims(x)` should be used.

### Details

In multiplexed single-cell experiments, we can detect doublets as libraries with labels for multiple samples. However, this approach fails to identify doublets consisting of two cells with the same label. Such cells may be problematic if they are still sufficiently abundant to drive formation of spurious clusters.

This function identifies intra-sample doublets based on the similarity in expression profiles to known inter-sample doublets. For each cell, we compute the proportion of the `k` neighbors that are known doublets. Of the “unmarked” cells that are not known doublets, those with top X largest proportions are considered to be intra-sample doublets.

To compute X, we assume that the formation of doublets is random with respect to their originating samples. This allows us to use `samples` to estimate the expected percentage of doublets that should occur within samples. We then convert into an absolute number X based on the number of known doublets in `doublets`.

A larger value of `k` provides more stable estimates of the doublet proportion in each cell. However, this comes at the cost of assuming that each cell actually has `k` neighboring cells of the same state. For example, if a doublet cluster has fewer than `k` members, its doublet proportions will be “diluted” by inclusion of unmarked cells in the next-closest cluster.

In principle, it is also possible to identify inter-sample doublets by applying a hard threshold on the doublet proportion. This threshold can be set close to the expected percentage from `samples` (i.e., the same one used to derive X). Unfortunately, in practice, the observed proportions are generally lower than expected, possibly due to contamination of doublet subpopulations by unmarked cells in noisy expression data. This motivates the use of a top X approach instead.

### Value

A DataFrame containing one row per cell and the following fields:

- `proportion`, a numeric field containing the proportion of neighbors that are doublets.

- `known`, a logical field indicating whether this cell is a known inter-sample doublet.

- `predicted`, a logical field indicating whether this cell is a predicted intra-sample doublet.

The `metadata` contains `intra`, a numeric scalar containing the expected number of intra-sample doublets.

### Author(s)

Aaron Lun

### See Also

`doubletCells` and `doubletCluster`, for alternative methods of doublet detection when no prior doublet information is available.

`hashedDrops` from the DropletUtils package, to identify doublets from cell hashing experiments.

### Examples

    # Mocking up an example.
    set.seed(100)
    ngenes <- 1000
    mu1 <- 2^rnorm(ngenes, sd=2)
    mu2 <- 2^rnorm(ngenes, sd=2)

    counts.1 <- matrix(rpois(ngenes*100, mu1), nrow=ngenes) # Pure type 1
    counts.2 <- matrix(rpois(ngenes*100, mu2), nrow=ngenes) # Pure type 2
    counts.m <- matrix(rpois(ngenes*20, mu1+mu2), nrow=ngenes) # Doublets (1 & 2)
    all.counts <- cbind(counts.1, counts.2, counts.m)
    lcounts <- scuttle::normalizeCounts(all.counts)

    # Pretending that half of the doublets are known. Also pretending that
    # the experiment involved two samples of equal size.
    known <- 200 + seq_len(10)
    out <- doubletRecovery(lcounts, doublets=known, k=10, samples=c(1, 1))
    out

[Package scran version 1.18.7 Index]