CIARA: CIARA

In CIARA: Cluster Independent Algorithm for Rare Cell Types Identification

CIARA

Description

It selects highly localized genes as specified in CIARA_gene, starting from genes in background

Usage

CIARA(
  norm_matrix,
  knn_matrix,
  background,
  cores_number = 1,
  p_value = 0.001,
  odds_ratio = 2,
  local_region = 1,
  approximation = FALSE
)

Arguments

norm_matrix	Norm count matrix (n_genes X n_cells).
knn_matrix	K-nearest neighbors matrix (n_cells X n_cells).
background	Vector of genes for which the function CIARA_gene is run.
cores_number	Integer.Number of cores to use.
p_value	p value returned by the function fisher.test with parameter alternative = "g"
odds_ratio	odds_ratio returned by the function fisher.test with parameter alternative = "g"
local_region	Integer. Minimum number of local regions (cell with its knn neighbours) where the binarized gene expression is enriched in 1.
approximation	Logical.For a given gene, the fisher test is run in the local regions of only the cells where the binarized gene expression is 1.

Value

Dataframe with n_rows equal to the length of background . Each row is the output from CIARA_gene.

Author(s)

Gabriele Lubatti [e-mail]

CIARA documentation built on March 18, 2022, 6:48 p.m.