top_results: Filter significant results.

In distinct: distinct: a method for differential analyses via hierarchical permutation tests

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 [e-mail]

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")

distinct documentation built on Nov. 8, 2020, 8:20 p.m.