make_meta_markers: Extract meta-analytic markers from multiple datasets.
In gillislab/MetaMarkers: Extract robust markers from multiple datasets

Extract meta-analytic markers from multiple datasets.

make_meta_markers(
  marker_lists,
  order_by = "auroc",
  fdr_threshold = 0.05,
  fc_threshold = 4,
  detection_threshold = 0,
  detailed_stats = FALSE,
  common_genes_only = TRUE,
  check_duplicates = FALSE
)

`marker_lists`	Named list, where elements are marker statistics obtained with `compute_markers` and names are names of the dataset from which markers have been extracted.
`order_by`	Secondary statistic by which meta-markers should be ranked.
`fdr_threshold`	FDR threshold for a gene to be considered Differentially Expressed (DE).
`fc_threshold`	Fold change threshold for a gene to be DE.
`detection_threshold`	Detection rate threshold for a gene to be DE.
`detailed_stats`	Boolean. By default, only output a list of best markers, alternatively output additional statistics, such as average AUROC, FC, etc.
`common_genes_only`	Boolean. Keep only genes that are common to all datasets?
`check_duplicates`	Boolean. Check and remove duplicated gene names? In theory, this step was already performed in `compute_markers` and does not need to be performed again (time consuming).

A tibble containing ranked meta-markers and (if desired) average DE statistics for each cell type. Within each cell type, meta-markers are ranked by recurrence (# datasets in which gene is DE), then by a secondary statistics, as specified in "order_by" (AUROC by default).

gillislab/MetaMarkers documentation built on April 24, 2021, 9:25 p.m.