quantKnn: Noise-related quantaties of local pruned k-nearest...

In RaceID: Identification of Cell Types, Inference of Lineage Trees, and Prediction of Noise Dynamics from Single-Cell RNA-Seq Data

Noise-related quantaties of local pruned k-nearest neighbourhoods

Description

This function computes a number of noise-related quantities for all pruned k-nearest neighbourhoods.

Usage

quantKnn(res, noise, object, pvalue = 0.01, minN = 5, no_cores = NULL)

Arguments

res	List object with k nearest neighbour information returned by pruneKnn function.
noise	List of noise parameters returned by compTBNoise.
object	SCseq class object.
pvalue	Positive real number between 0 and 1. All nearest neighbours with link probability < pvalue are discarded. Default is 0.01.
minN	Positive integer number. Noise inference is only done for k-nearest neighbourhoods with at least minN neighbours remaining after pruning.
no_cores	Positive integer number. Number of cores for multithreading. If set to NULL then the number of available cores minus two is used. Default is NULL.

Value

List object with eight components:

noise.av	Vector of biological noise average across all genes for each k-nearest neighbourhood.
noise.ratio	Vector of ratio between total noise and technical noise averaged across all genes for each k-nearest neighbourhood.
local.corr	Vector of average Spearman's correlation coefficient between all cell in a pruned k-nearest neighourhood.
umi	Vector of total UMI counts for all cells.

RaceID documentation built on Sept. 28, 2023, 5:06 p.m.