findMastMarkers_one_vs_all

Identify marker genes for all clusters in a one vs all manner based on the MAST package.

findMastMarkers_one_vs_all(
  gobject,
  expression_values = c("normalized", "scaled", "custom"),
  cluster_column,
  subset_clusters = NULL,
  adjust_columns = NULL,
  pval = 0.001,
  logFC = 1,
  min_genes = 10,
  verbose = TRUE,
  ...
)

Arguments

gobject	giotto object
expression_values	gene expression values to use
cluster_column	clusters to use
subset_clusters	selection of clusters to compare
adjust_columns	column in pDataDT to adjust for (e.g. detection rate)
pval	filter on minimal p-value
logFC	filter on logFC
min_genes	minimum genes to keep per cluster, overrides pval and logFC
verbose	be verbose
...	additional parameters for the zlm function in MAST

Value

data.table with marker genes

See also

findMastMarkers

Examples

data(mini_giotto_single_cell)

mast_markers = findMastMarkers_one_vs_all(gobject = mini_giotto_single_cell,
                                          cluster_column = 'leiden_clus')
#> using 'MAST' to detect marker genes. If used in published research, please cite:
#>   McDavid A, Finak G, Yajima M (2020).
#>   MAST: Model-based Analysis of Single Cell Transcriptomics. R package version 1.14.0,
#>   https://github.com/RGLab/MAST/.
#> 
#>  start with cluster  1 
#> Assuming data assay in position 1, with name et is log-transformed.
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!
#> 
#>  start with cluster  2 
#> Assuming data assay in position 1, with name et is log-transformed.
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!
#> 
#>  start with cluster  3 
#> Assuming data assay in position 1, with name et is log-transformed.
#> 
#> Done!
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#> 
#> Done!