Identify Marker Genes Using MAST in One vs. All Manner¶

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¶

A data.table with marker genes

Details¶

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!