multiMarkerStats {scran} | R Documentation

## Combine multiple sets of marker statistics

### Description

Combine multiple sets of marker statistics, typically from different tests, into a single DataFrame for convenient inspection.

### Usage

    multiMarkerStats(..., repeated = NULL, sorted = TRUE)

### Arguments

`...` | Two or more lists or Lists produced by `findMarkers` or `combineMarkers`. Each list should contain DataFrames of results, one for each group/cluster of cells. The names of each List should be the same; the universe of genes in each DataFrame should be the same; and the same number of columns in each DataFrame should be named. All elements in `...` are also expected to be named.
`repeated` | Character vector of columns that are present in one or more DataFrames but should only be reported once. Typically used to avoid reporting redundant copies of annotation-related columns.
`sorted` | Logical scalar indicating whether each output DataFrame should be sorted by some relevant statistic.

### Details

The combined statistics are designed to favor a gene that is highly ranked in each of the individual test results. This is highly conservative and aims to identify robust DE that is significant under all testing schemes.

A combined `Top` value of T indicates that the gene is among the top T genes of one or more pairwise comparisons in each of the testing schemes. (We can be even more aggressive if the individual results were generated with a larger `min.prop` value.) In effect, a gene can only achieve a low `Top` value if it is consistently highly ranked in each test. If `sorted=TRUE`, this is used to order the genes in the output DataFrame.

The combined `p.value` is effectively the result of applying an intersection-union test to the per-test results. This will only be low if the gene has a low p-value in each of the test results. If `sorted=TRUE` and `Top` is not present, this will be used to order the genes in the output DataFrame.

### Value

A named List of DataFrames with one DataFrame per group/cluster. Each DataFrame contains statistics from the corresponding entry of each List in `...`, prefixed with the name of the List. In addition, several combined statistics are reported:

- `Top`, the largest rank of each gene across all DataFrames for that group. This is only reported if each list in `...` was generated with `pval.type="any"` in `combineMarkers`.

- `p.value`, the largest p-value of each gene across all DataFrames for that group. This is replaced by `log.p.value` if p-values in `...` are log-transformed.

- `FDR`, the BH-adjusted value of `p.value`. This is replaced by `log.FDR` if p-values in `...` are log-transformed.

### Author(s)

Aaron Lun

### See Also

`findMarkers` and `combineMarkers`, to generate elements in `...`.

### 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)

    tout <- findMarkers(sce, groups=kout$cluster, direction="up")
    wout <- findMarkers(sce, groups=kout$cluster, direction="up", test="wilcox")

    combined <- multiMarkerStats(t=tout, wilcox=wout)
    colnames(combined[[1]])

[Package scran version 1.16.0 Index]