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Find all markers

find_all_markers_sc.Rd

This function can be used to run differential gene expression for every group of an unsupervised clustering method for example. You specify a column and the function will start calculating differential gene expression of the first cluster vs. everything else, second cluster vs. everything else, etc. The function will automatically downsample everything else to a random set of 100,000 cells if it should exceed that. This automatic downsampling can be turned off however.

Usage

find_all_markers_sc(
  object,
  column_of_interest,
  method = "wilcox",
  alternative = c("greater", "less", "twosided"),
  min_prop = 0.05,
  downsampling = TRUE,
  seed = 42L,
  .verbose = TRUE
)

Arguments

object

SingleCells or SingleCellsSubset class.

column_of_interest

String. The column you wish to use to identify the markers between all combination. Needs to be in the obs table

method

String. Which method to use for the calculations of the DGE. At the moment the only option is "wilcox", but the parameter is reserved for future features.

alternative

String. Test alternative. One of c("twosided", "greater", "less"). This function will default to "greater", i.e., genes upregulated in the group.

min_prop

Numeric. The minimum proportion of cells that need to express the gene to be tested in any of the two groups.

downsampling

Boolean. If the other group exceeds 100,000 cells, a random subsample of 100,000 cells will be used.

seed

Integer. Seed that is used for the downsampling.

.verbose

Boolean or integer. Controls verbosity and returns run times. FALSE -> quiet, TRUE or 1L -> normal verbosity, 2L -> detailed verbosity.

Value

data.table with the DGE results from the test.

Examples

# one versus rest across the Leiden clusters
sc <- demo_single_cells()
sc <- find_clusters_sc(sc, res = 1.0)
res <- find_all_markers_sc(
  sc,
  column_of_interest = "leiden_clustering",
  .verbose = FALSE
)
head(res)
#>      grp gene_id      lfc     prop1     prop2 z_scores     p_values
#>    <int>  <char>    <num>     <num>     <num>    <num>        <num>
#> 1:     0 gene_01 3.211139 0.9881657 0.7280967 16.50891 1.582561e-61
#> 2:     0 gene_02 3.121258 0.9881657 0.7129909 16.00064 6.323246e-58
#> 3:     0 gene_03 3.216300 0.9881657 0.7311178 16.73337 3.744067e-63
#> 4:     0 gene_04 3.098137 0.9644970 0.6706949 15.48991 2.029132e-54
#> 5:     0 gene_05 2.711545 0.8284023 0.4290030 12.43032 8.944763e-36
#> 6:     0 gene_06 3.202754 0.9822485 0.6767372 16.24377 1.236194e-59
#>             fdr
#>           <num>
#> 1: 1.978202e-60
#> 2: 5.269372e-57
#> 3: 9.360168e-62
#> 4: 1.449380e-53
#> 5: 4.969313e-35
#> 6: 1.236194e-58

unlink(sc@dir_data, recursive = TRUE, force = TRUE)