findMarkers {scran} | R Documentation

## Find marker genes

### Description

Find candidate marker genes for groups of cells (e.g., clusters) by testing for differential expression between pairs of groups.

### Usage

    findMarkers(x, ...)

    ## S4 method for signature 'ANY'
    findMarkers(x, groups, test.type = c("t", "wilcox",
      "binom"), ..., pval.type = c("any", "some", "all"), min.prop = NULL,
      log.p = FALSE, full.stats = FALSE, sorted = TRUE,
      row.data = NULL)

    ## S4 method for signature 'SingleCellExperiment'
    findMarkers(x, ..., subset.row = NULL,
      assay.type = "logcounts", get.spikes = FALSE)

### Arguments

`x` | A numeric matrix-like object of expression values, where each column corresponds to a cell and each row corresponds to an endogenous gene. This is expected to be normalized log-expression values for most tests - see Details. Alternatively, a SingleCellExperiment object containing such a matrix.
`...` | For the generic, further arguments to pass to specific methods. For the ANY method: For `test.type="t"`, further arguments to pass to `pairwiseTTests`. For `test.type="wilcox"`, further arguments to pass to `pairwiseWilcox`. For `test.type="binom"`, further arguments to pass to `pairwiseBinom`. Common arguments for all testing functions include `gene.names`, `direction`, `block` and `BPPARAM`. Test-specific arguments are also supported for the appropriate `test.type`. For the SingleCellExperiment method, further arguments to pass to the ANY method.
`groups` | A vector of group assignments for all cells.
`test.type` | String specifying the type of pairwise test to perform - a t-test with `"t"`, a Wilcoxon rank sum test with `"wilcox"`, or a binomial test with `"binom"`.
`pval.type` | A string specifying how p-values are to be combined across pairwise comparisons for a given group/cluster.
`min.prop` | Numeric scalar specifying the minimum proportion of significant comparisons per gene, Defaults to 0.5 when `pval.type="some"`, otherwise defaults to zero.
`log.p` | A logical scalar indicating if log-transformed p-values/FDRs should be returned.
`full.stats` | A logical scalar indicating whether all statistics in `de.lists` should be stored in the output for each pairwise comparison.
`sorted` | Logical scalar indicating whether each output DataFrame should be sorted by a statistic relevant to `pval.type`.
`row.data` | A DataFrame containing additional row metadata for each gene in `x`, to be included in each of the output DataFrames. If `sorted=TRUE`, this should have the same row names as the output of `combineMarkers` (usually `rownames(x)`, see the `gene.names` argument in functions like `pairwiseTTests`.
`subset.row` | See `?"scran-gene-selection"`.
`assay.type` | A string specifying which assay values to use, usually `"logcounts"`.
`get.spikes` | See `?"scran-gene-selection"`.

### Details

This function provides a convenience wrapper for marker gene identification between groups of cells, based on running `pairwiseTTests` or related functions and passing the result to `combineMarkers`. All of the arguments above are supplied directly to one of these two functions - refer to the relevant function's documentation for more details.

If `x` contains log-normalized expression values generated with a pseudo-count of 1, it can be used in any of the pairwise testing procedures. If `x` is scale-normalized but not log-transformed, it can be used with `test.type="wilcox"` and `test.type="binom"`. If `x` contains raw counts, it can only be used with `test.type="binom"`.

Note that `log.p` only affects the combined p-values and FDRs. If `full.stats=TRUE`, the p-values for each individual pairwise comparison will always be log-transformed, regardless of the value of `log.p`. Log-transformed p-values and FDRs are reported using the natural base.

The choice of `pval.type` determines whether the highly ranked genes are those that are DE between the current group and:

- any other group (`"any"`)

- all other groups (`"all"`)

- some other groups (`"some"`)

See `?combineMarkers` for more details.

### Value

A named list of DataFrames, each of which contains a sorted marker gene list for the corresponding group. In each DataFrame, the top genes are chosen to enable separation of that group from all other groups. Log-fold changes are reported as differences in average `x` between groups (usually in base 2, depending on the transformation applied to `x`).

See `?combineMarkers` for more details on the output format.

### Author(s)

Aaron Lun

### See Also

`pairwiseTTests`, `pairwiseWilcox`, `pairwiseBinom`, for the underlying functions that compute the pairwise DE statistics.

`combineMarkers`, to combine pairwise statistics into a single marker list per cluster.

### Examples

    library(scater)
    sce <- mockSCE()
    sce <- logNormCounts(sce)

    # Any clustering method is okay, only using k-means for convenience.
    kout <- kmeans(t(logcounts(sce)), centers=4)

    out <- findMarkers(sce, groups=kout$cluster)
    names(out)
    out[[1]]

    # More customization of the tests:
    out <- findMarkers(sce, groups=kout$cluster, test.type="wilcox")
    out[[1]]

    out <- findMarkers(sce, groups=kout$cluster, lfc=1, direction="up")
    out[[1]]

    out <- findMarkers(sce, groups=kout$cluster, pval.type="all")
    out[[1]]

[Package scran version 1.14.6 Index]