top_results {distinct} | R Documentation

## Filter significant results.

### Description

`top_results` returns the significant results obtained via `distinct_test`.

### Usage

    top_results(
      res,
      cluster = "all",
      significance = 0.01,
      global = TRUE,
      up_down = "both",
      sort_by = "p_adj.glb"
    )

### Arguments

`res` | a `data.frame` with results as returned from `distinct_test`.
`cluster` | a character indicating the cluster(s) whose results have to be returned. Results from all clusters are returned by default ("all").
`significance` | numeric, results with adjusted p-value < significance will be returned.
`global` | logical indicating whether to filter results according to p_adj.glb (when TRUE), or p_adj.loc (when FALSE).
`up_down` | a character indicating whether to return: all results ("both" or "BOTH"), only up-regulated results ("up" or "UP") or down-regulated results ("down" or "DOWN"). In 'res', a FC > 1 (or log2FC > 0) indicates up-regulation of group1 (compared to group2); while a FC < 1 (or log2FC < 0) indicates down-regulation of group1 (compared to group2).
`sort_by` | a character indicating how results should be sorted. Results can be sorted by globally adjusted p-value ("p_adj.glb", default choice), locally adjusted p-value ("p_adj.loc"), raw p-value ("p_val") or (log2)fold-change ("FC" or "log2FC").

### Value

A `data.frame` object. Columns 'gene' and 'cluster_id' contain the gene and cell-cluster name, while 'p_val', 'p_adj.loc' and 'p_adj.glb' report the raw p-values, locally and globally adjusted p-values, via Benjamini and Hochberg (BH) correction. In locally adjusted p-values ('p_adj.loc') BH correction is applied in each cluster separately, while in globally adjusted p-values ('p_adj.glb') BH correction is performed to the results from all clusters.

### Author(s)

Simone Tiberi simone.tiberi@uzh.ch

### See Also

`distinct_test`, `log2_FC`

### Examples

    # load pre-computed results (obtaines via `distinct_test`)
    data("res", package = "distinct")

    # Visualize significant results:
    head(top_results(res))

    # Visualize significant results from a specified cluster of cells:
    top_results(res, cluster = "Dendritic cells")

    # We can optionally add the fold change (FC) and log2-FC between groups:
    # load the input data:
    data("Kang_subset", package = "distinct")

    res = log2_FC(res = res,
      x = Kang_subset,
      name_assays_expression = "cpm",
      name_group = "stim",
      name_cluster = "cell")

    # By default, results from 'top_results' are sorted by (globally) adjusted p-value;
    # they can also be sorted by log2-FC:
    top_results(res, cluster = "Dendritic cells", sort_by = "log2FC")

    # Visualize significant UP-regulated genes only:
    top_results(res, up_down = "UP",
      cluster = "Dendritic cells")

[Package distinct version 1.4.1 Index]