log2_FC {distinct} | R Documentation

## Compute FCs and log2-FCs.

### Description

`log2_FC` extends the results obtained via `distinct_test`, by computing fold changes (FC) and log2-FC between conditions.

### Usage

    log2_FC(
      res,
      x,
      name_assays_expression = "cpm",
      name_group = "group_id",
      name_cluster = "cluster_id"
    )

### Arguments

`res` | a `data.frame` with results as returned from `distinct_test`.
`x` | a `linkS4class{SummarizedExperiment}` or a `linkS4class{SingleCellExperiment}` object.
`name_assays_expression` | a character ("cpm" by default), indicating the name of the assays(x) element which stores the expression data (i.e., assays(x)$name_assays_expression). We strongly encourage using normalized data, such as counts per million (CPM). Do not use logarithm transformed data to compute FCs.
`name_group` | a character ("group_id" by default), indicating the name of the colData(x) element which stores the group id of each cell (i.e., colData(x)$name_group).
`name_cluster` | a character ("cluster_id" by default), indicating the name of the colData(x) element which stores the cluster id of each cell (i.e., colData(x)$name_cluster).

### Value

A `data.frame` object, extending the results in 'res'. Two additional columns are added: FC_group1/group2 and log2FC_group1/group2, inicating the FC and log2-FC of group1/group2. 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).

### Author(s)

Simone Tiberi simone.tiberi@uzh.ch

### See Also

`distinct_test`, `top_results`

### Examples

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

    # We can optionally add the fold change (FC) and log2-FC between groups:
    res = log2_FC(res = res,
      x = Kang_subset,
      name_assays_expression = "cpm",
      name_group = "stim",
      name_cluster = "cell")

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

[Package distinct version 1.2.0 Index]