combineRecomputedResults {SingleR} | R Documentation

## Combine SingleR results with recomputation

### Description

Combine results from multiple runs of `classifySingleR` (usually against different references) into a single DataFrame. The label from the results with the highest score for each cell is retained. Unlike `combineCommonResults`, this does not assume that each run of `classifySingleR` was performed using the same set of common genes, instead recomputing the scores for comparison across references.

### Usage

    combineRecomputedResults(
      results,
      test,
      trained,
      quantile = 0.8,
      assay.type.test = "logcounts",
      check.missing = TRUE,
      BNPARAM = KmknnParam(),
      BPPARAM = SerialParam()
    )

### Arguments

`results` | A list of DataFrame prediction results as returned by `classifySingleR` when run on each reference separately.
`test` | A numeric matrix of single-cell expression values where rows are genes and columns are cells. Alternatively, a SummarizedExperiment object containing such a matrix.
`trained` | A list of Lists containing the trained outputs of multiple references, equivalent to either (i) the output of `trainSingleR` on multiple references with `recompute=TRUE`, or (ii) running `trainSingleR` on each reference separately and manually making a list of the trained outputs.
`quantile` | Further arguments to pass to `classifySingleR`.
`assay.type.test` | An integer scalar or string specifying the assay of `test` containing the relevant expression matrix, if `test` is a SummarizedExperiment object.
`check.missing` | Logical scalar indicating whether rows should be checked for missing values (and if found, removed).
`BNPARAM` | A BiocNeighborParam object specifying the algorithm to use for building nearest neighbor indices.
`BPPARAM` | A BiocParallelParam object specifying how parallelization should be performed, if any.

### Details

This function implements a variant of Option 3 described in `?"combine-predictions"`. For a given cell in `test`, we extract its assigned label from `results` for each reference. We also retrieve the marker genes associated with that label and take the union of markers across all references. This defines a common feature space in which the score for each reference's assigned label is recomputed using `ref`; the label from the reference with the top recomputed score is then reported as the combined annotation for that cell.

Unlike `combineCommonResults`, the union of markers is not used for the within-reference calls. This avoids the inclusion of noise from irrelevant genes in the within-reference assignments. Obviously, `combineRecomputedResults` is slower as it does require recomputation of the scores, but the within-reference calls are faster as there are fewer genes in the union of markers for assigned labels (compared to the union of markers across all labels, as required by `combineCommonResults`), so it is likely that the net compute time should be lower.

It is strongly recommended that the universe of genes be the same across all references. The intersection of genes across all `ref` and `test` is used when recomputing scores, and differences in the availability of genes between references may have unpredictable effects.

### Value

A DataFrame is returned containing the annotation statistics for each cell or cluster (row). This mimics the output of `classifySingleR` and contains the following fields:

- `scores`, a numeric matrix of correlations containing the recomputed scores. For any given cell, entries of this matrix are only non-`NA` for the assigned label in each reference; scores are not recomputed for the other labels.

- `labels`, a character vector containing the per-cell combined label across references.

- `references`, an integer vector specifying the reference from which the combined label was derived.

- `orig.results`, a DataFrame containing `results`.

It may also contain `first.labels` and `pruned.labels` if these were also present in `results`.

### Author(s)

Aaron Lun

### References

Lun A, Bunis D, Andrews J (2020). Thoughts on a more scalable algorithm for multiple references. https://github.com/LTLA/SingleR/issues/94

### See Also

`SingleR` and `classifySingleR`, for generating predictions to use in `results`.

`combineCommonResults`, for another approach to combining predictions.

### Examples

    # Making up data.
    ref <- .mockRefData(nreps=8)
    ref1 <- ref[,1:2%%2==0]
    ref2 <- ref[,1:2%%2==1]
    ref2$label <- tolower(ref2$label)

    test <- .mockTestData(ref)

    # Performing classification within each reference.
    test <- scater::logNormCounts(test)

    ref1 <- scater::logNormCounts(ref1)
    train1 <- trainSingleR(ref1, labels=ref1$label)
    pred1 <- classifySingleR(test, train1)

    ref2 <- scater::logNormCounts(ref2)
    train2 <- trainSingleR(ref2, labels=ref2$label)
    pred2 <- classifySingleR(test, train2)

    # Combining results with recomputation of scores.
    combined <- combineRecomputedResults(
        results=list(pred1, pred2),
        test=test,
        trained=list(train1, train2))

    combined[,1:5]

[Package SingleR version 1.2.4 Index]