findCorMarkers: Calculate spearman correlations between features.

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

Calculate spearman correlations between features.

Description

Calculate spearman correlations between features in Seruat object. Sparse implementation enables faster calculation of spearman correlations without need to cast sparse expression matrix to dense matrix.

Usage

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

Arguments

object	Seurat object
features.x	feature or meta feature. Spearman correlation between features.x and features.y are computed.
features.y	feature or meta feature. Spearman correlation between features.x and features.y are computed.
ncell.subset	max number of cells to run analysis on. Default is 5000.
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 spearman correlation on. Default is DefaultAssay(object).
slot	slot to run spearman correlation on. Default is data.
verbose	print progress. Default is T.

Value

data.frame with spearman correlations.

Author(s)

Nicholas Mikolajewicz and Saket Choudhary (https://github.com/saketkc/blog/blob/main/2022-03-09/SparseSpearmanCorrelation.ipynb)

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