contrast_each_group_to_the_rest {celaref} | R Documentation

## contrast_each_group_to_the_rest

### Description

Produces a table of within-experiment differential expression results (for either query or reference experiment), where each group (cluster) is compared to the rest of the cells.

### Usage

    contrast_each_group_to_the_rest(dataset_se, dataset_name,
      groups2test = NA, num_cores = 1, n.group = Inf, n.other = n.group
      * 5)

### Arguments

`dataset_se` | Summarised experiment object containing count data. Also requires 'ID' and 'group' to be set within the cell information (see `colData()`)
`dataset_name` | Short, meaningful name for this dataset/experiment.
`groups2test` | An optional character vector specificing specific groups to check. By default (set to NA), all groups will be tested.
`num_cores` | Number of cores to use to run MAST jobs in parallel. Ignored if parallel package not available. Set to 1 to avoid parallelisation. Default = 1
`n.group` | How many cells to keep for each group in groupwise comparisons. Default = Inf
`n.other` | How many cells to keep from everything not in the group. Default = n.group * 5

### Details

Note that this function is slow, because it runs the differential expression. It only needs to be run once per dataset though (unless group labels change). Having package parallel installed is highly recomended.

If this function runs out of memory, consider specifying n.group and n.other to run on a subset of cells (taken from each group, and proportionally from the rest for each test). Alternatively use subset_cells_by_group to subset dataset_se for each group independantly.

Both reference and query datasets should be processed with this function.

The tables produced by this function (usually named something like de_table.datasetname) contain summarised results of MAST results. Each group is compared versus cells in the group, versus not in the group, (Ie. always a 2-group contrast, other groups information is ignored). As per MAST reccomendataions, the proportion of genes seen in each cell is included in the model.

### Value

A tibble the within-experiment de_table (differential expression table). This is a core summary of the individual experiment/dataset, which is used for the cross-dataset comparisons.

The table feilds won't neccesarily match across datasets, as they include cell annotations information. Important columns (used in downstream analysis) are:

ID

  Gene identifier

ci_inner

  Inner (conservative) 95% confidence interval of log2 fold-change.

fdr

  Multiple hypothesis corrected p-value (using BH/FDR method)

group

  Cells from this group were compared to everything else

sig_up

  Significnatly differentially expressed (fdr < 0.01), with a positive fold change?

rank

  Rank position (within group), ranked by CI inner, highest to lowest.

rescaled_rank

  Rank scaled 0(top most overrepresented genes in group) - 1(top most not-present genes)

dataset

  Name of dataset/experiment

### Examples

    de_table.demo_query  <- contrast_each_group_to_the_rest(
         demo_query_se, "a_demo_query")

    de_table.demo_ref    <- contrast_each_group_to_the_rest(
         demo_ref_se, "a_demo_ref", num_cores=2)

[Package celaref version 1.8.0 Index]