findCDIMarkers: Calculate feature co-dependency index

In NMikolajewicz/scMiko: scRNAseq analysis functions. Developed using Seurat framework.

Calculate feature co-dependency index

Description

Calculate feature co-dependency index (CDI).

Usage

findCDIMarkers(
  object,
  features.x = NULL,
  features.y = rownames(object),
  ncell.subset = 5000,
  geosketch.subset = F,
  assay = DefaultAssay(object),
  slot = "data",
  n.workers = 1,
  verbose = T
)

Arguments

object	Seurat object
features.x	feature or meta feature. CDI between features.x and features.y are computed.
features.y	feature or meta feature. CDI between features.x and features.y are computed.
ncell.subset	max number of cells to run analysis on. Default is 5000. Computationally intensive for larger datasets.
geosketch.subset	Use GeoSketch method to subsample scRNA-seq data while preserving rare cell states (https://doi.org/10.1016/j.cels.2019.05.003). Logical, T or F (Default F). Recommended if cell type representation is imbalanced.
assay	Assay to run CDI scoring on. Default is DefaultAssay(object).
slot	slot to run CDI scoring on. Default is data.
n.workers	number of workers for parallel implementation. Default is 1 (no parallel).
verbose	print progress. Default is T.

Value

data.frame with CDI scores.

Author(s)

Nicholas Mikolajewicz

See Also

binom.test

NMikolajewicz/scMiko documentation built on June 28, 2023, 1:41 p.m.