compare_sce: Use SCPA to compare pathways within a SingleCellExperiment...
In jackbibby1/SCPA: Single Cell Pathway Analysis

compare_sce

R Documentation

Use SCPA to compare pathways within a SingleCellExperiment object

Description

This function takes a SingleCellExperiment object as an input, and compares gene sets over specified conditions/populations.

Usage

compare_sce(
  sce_object,
  assay_name = "logcounts",
  group1 = NULL,
  group1_population = NULL,
  group2 = NULL,
  group2_population = NULL,
  pathways,
  min_genes = 15,
  max_genes = 500,
  downsample = 500,
  parallel = FALSE,
  cores = NULL
)

Arguments

`sce_object`	SingleCellExperiment object with populations defined in the column data
`assay_name`	Assay name to extract expression values from. Defaults to logcounts
`group1`	First comparison group as defined by `colData()` columns of SingleCellExperiment object e.g. cell_type
`group1_population`	Populations within group1 to compare e.g. c("t_cell", "b_cell")
`group2`	Second comparison group as defined by `colData()` columns of SingleCellExperiment object e.g. hour
`group2_population`	Population within group2 to compare e.g. 24
`pathways`	Pathway gene sets with each pathway in a separate list. For formatting of gene lists, see documentation at https://jackbibby1.github.io/SCPA/articles/using_gene_sets.html
`min_genes`	Gene sets with fewer than this number of genes will be excluded
`max_genes`	Gene sets with more than this number of genes will be excluded
`downsample`	Option to downsample cell numbers. Defaults to 500 cells per condition. If a population has < 500 cells, all cells from that condition are used.
`parallel`	Should parallel processing be used?
`cores`	The number of cores used for parallel processing

Value

Statistical results from the SCPA analysis. The qval should be the primary metric that is used to interpret pathway differences i.e. a higher qval translates to larger pathway differences between conditions. If only two samples are provided, a fold change (FC) enrichment score will also be calculated. The FC output is generated from a running sum of mean changes in gene expression from all genes of the pathway. It's calculated from average pathway expression in population1 - population2, so a negative FC means the pathway is higher in population2.

Examples

## Not run: 
scpa_out <- compare_sce(
     group1 = "cell",
     group1_population = c("t_cell", "b_cell"),
     group2 = "hour",
     group2_population = c("24"),
     pathways = pathways)

## End(Not run)

jackbibby1/SCPA documentation built on May 14, 2024, 7:16 a.m.