README.md

In cyhsuTN/scKWARN: Single-cell RNA Sequencing Normalization Using A Local Average Technique

scKWARN: a normalization method for single-cell RNAseq data

Chih-Yuan Hsu

Feb. 04, 2024

Hsu CY, Chang CJ, Liu Q, Shyr Y (2024). scKWARN: Kernel-weighted-average robust normalization for single-cell RNA-seq data. Bioinformatics. doi: 10.1093/bioinformatics/btae008.

Installation

Download scKWARN_1.1.2.tar.gz and locally install it, or execute the following code:

library(devtools)
install_github("cyhsuTN/scKWARN")

Usage

library(scKWARN)

A simulated count matrix generated from negative binomial distributions

set.seed(12345)
G <- 2000; n <- 600 # G: number of genes, n: number of cells
mu <- rgamma(G, shape = 2, rate = 2)
NB_cell <- function(j) rnbinom(G, size = 0.1, mu = mu)
countsimdata <- sapply(1:n, NB_cell)
colnames(countsimdata) <- paste("c", 1:n, sep = "_")
rownames(countsimdata) <- paste("g", 1:G, sep = "_")

Inputs: countmatrix (general matrix or sparse matrix)

Result <- LocASN(countmatrix = countsimdata)
#Result <- LocASN(countmatrix = as(countsimdata,"sparseMatrix"))

Outputs: NormalizedData (normalized data = counts/scaling factor) and scalingFactor (a scaling factor for each cell)

Result$NormalizedData[1:6,1:5]; Result$scalingFactor[1:6]

## 6 x 5 sparse Matrix of class "dgCMatrix"
##          c_1      c_2      c_3      c_4      c_5
## g_1 .        .        .        3.065545 .       
## g_2 .        .        .        2.043697 .       
## g_3 .        .        .        .        .       
## g_4 2.192714 1.989941 .        .        .       
## g_5 .        .        1.964309 .        .       
## g_6 .        .        .        .        3.085495

## [1] 0.9121116 1.0050549 1.0181696 0.9786187 0.9722913 1.0133925

# log1p(Result$NormalizedData) for clustering

cyhsuTN/scKWARN documentation built on Feb. 11, 2024, 2:21 p.m.