classifySingleR {SingleR} | R Documentation

## Classify cells with SingleR

### Description

Assign labels to each cell in a test dataset, using a pre-trained classifier combined with an iterative fine-tuning approach.

### Usage

    classifySingleR(
      test,
      trained,
      quantile = 0.8,
      fine.tune = TRUE,
      tune.thresh = 0.05,
      sd.thresh = NULL,
      prune = TRUE,
      assay.type = "logcounts",
      check.missing = TRUE,
      BPPARAM = SerialParam()
    )

### Arguments

`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 containing the output of the `trainSingleR` function. Alternatively, a List of Lists produced by `trainSingleR` for multiple references.
`quantile` | A numeric scalar specifying the quantile of the correlation distribution to use to compute the score for each label.
`fine.tune` | A logical scalar indicating whether fine-tuning should be performed.
`tune.thresh` | A numeric scalar specifying the maximum difference from the maximum correlation to use in fine-tuning.
`sd.thresh` | A numeric scalar specifying the threshold on the standard deviation, for use in gene selection during fine-tuning. This is only used if `genes="sd"` when constructing `trained` and defaults to the value used in `trainSingleR`.
`prune` | A logical scalar indicating whether label pruning should be performed.
`assay.type` | Integer scalar or string specifying the matrix of expression values to use if `test` is a SummarizedExperiment.
`check.missing` | Logical scalar indicating whether rows should be checked for missing values (and if found, removed).
`BPPARAM` | A BiocParallelParam object specifyign the parallelization scheme to use.

### Details

Consider each cell in the test set `test` and each label in the training set. We compute Spearman's rank correlations between the test cell and all cells in the training set with the given label, based on the expression profiles of the genes selected by `trained`. The score is defined as the quantile of the distribution of correlations, as specified by `quantile`. (Technically, we avoid explicitly computing all correlations by using a nearest neighbor search, but the resulting score is the same.) After repeating this across all labels, the label with the highest score is used as the prediction for that cell.

If `fine.tune=TRUE`, an additional fine-tuning step is performed for each cell to improve resolution. We identify all labels with scores that are no more than `tune.thresh` below the maximum score. These labels are used to identify a fresh set of marker genes, and the calculation of the score is repeated using only these genes. The aim is to refine the choice of markers and reduce noise when distinguishing between closely related labels. The best and next-best scores are reported in the output for use in diagnostics, e.g., `pruneScores`.

The default `assay.type` is set to `"logcounts"` simply for consistency with `trainSingleR`. In practice, the raw counts (for UMI data) or the transcript counts (for read count data) can also be used without normalization and log-transformation. Any monotonic transformation will have no effect the calculation of the correlation values other than for some minor differences due to numerical precision.

If `prune=TRUE`, label pruning is performed as described in `pruneScores` with default arguments. This aims to remove low-quality labels that are ambiguous or correspond to misassigned cells. However, the default settings can be somewhat aggressive and discard otherwise useful labels in some cases - see `?pruneScores` for details.

If `trained` was generated from multiple references, `combineResults` is used to consolidate the per-reference statistics into a single DataFrame of results.

### Value

A DataFrame where each row corresponds to a cell in `test`. If `fine.tune=FALSE`, fields are:

- `scores`, a numeric matrix of correlations at the specified `quantile` for each label (column) in each cell (row).

- `labels`, a character vector containing the predicted label based on the maximum entry in `scores`.

- `pruned.labels`, a character vector containing the pruned labels where “low-quality” labels are replaced with `NA`s. Only added if `prune=TRUE`.

If `fine.tune=TRUE`, fields are:

- `scores`, a numeric matrix of correlations as above.

- `first.labels`, a character vector containing the predicted label before fine-tuning.

- `tuned.scores`, a DataFrame containing `first` and `second`. These are numeric vectors containing the best and next-best scores at the final round of fine-tuning for each cell.

- `labels`, a character vector containing the predicted label after fine-tuning.

- `pruned.labels`, a character vector of pruned labels as above. Only added if `prune=TRUE`.

In both cases, the `metadata` of the DataFrame contains:

- `common.genes`, a character vector of genes used to compute the correlations prior to fine-tuning.

- `de.genes`, a list of list of genes used to distinguish between each pair of labels. Only returned if `genes="de"` when constructing `trained`, see `?trainSingleR` for more details.

### Author(s)

Aaron Lun, based on the original `SingleR` code by Dvir Aran.

### See Also

`trainSingleR`, to prepare the training set for classification.

`pruneScores`, to remove low-quality labels based on the scores.

`combineResults`, to combine results from multiple references.

### Examples

    ##############################
    ## Mocking up training data ##
    ##############################

    Ngroups <- 5
    Ngenes <- 1000
    means <- matrix(rnorm(Ngenes*Ngroups), nrow=Ngenes)
    means[1:900,] <- 0
    colnames(means) <- LETTERS[1:5]

    g <- rep(LETTERS[1:5], each=4)
    ref <- SummarizedExperiment(
        list(counts=matrix(rpois(1000*length(g),
            lambda=10*2^means[,g]), ncol=length(g))),
        colData=DataFrame(label=g)
    )
    rownames(ref) <- sprintf("GENE_%s", seq_len(nrow(ref)))

    ref <- scater::logNormCounts(ref)
    trained <- trainSingleR(ref, ref$label)

    ###############################
    ## Mocking up some test data ##
    ###############################

    N <- 100
    g <- sample(LETTERS[1:5], N, replace=TRUE)
    test <- SummarizedExperiment(
        list(counts=matrix(rpois(1000*N, lambda=2^means[,g]), ncol=N)),
        colData=DataFrame(cluster=g)
    )

    rownames(test) <- sprintf("GENE_%s", seq_len(nrow(test)))
    test <- scater::logNormCounts(test)

    ###############################
    ## Performing classification ##
    ###############################

    pred <- classifySingleR(test, trained)
    table(predicted=pred$labels, truth=g)

[Package SingleR version 1.0.6 Index]