In compbiomed/singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

library(Seurat)
library(dplyr)
library(cowplot)
library(RColorBrewer)
library(ggplot2)
library(knitr)
library(kableExtra)
library(SingleCellExperiment)
library(scater)
library(gridExtra)
library(grid)
library(ggpubr)
library(patchwork)
library(singleCellTK)

if(!exists("headingNorm")) headingNorm <- "#"
cat(headingNorm, " Normalization {}\n\n")

Before the data can be used in the downstream analysis, normalization techniques that remove technical noise or variation must be applied. For this purpose, Seurat uses a global-scaling normalization method where the input raw data matrix is normalized by dividing the gene expression measurements by the total expression for each cell, multiplies it with a global-scaling factor and log-transforms the result. The output log-transformed and normalized gene expression measurements with technical variation and bias minimized can now better reflect the biological variability in the downstream methods.

The input raw data matrix 'r assayNames(data)[1]' was normalized using a global-scaling normalization method 'LogNormalize' that divides the gene expression measurements by the total expression for each cell, multiplies the resultant values with a scaling factor of '10,000' and log-transforms the result.

normalizeParams <- list(
  inSCE = data,
  normalizationMethod = "LogNormalize",
  scaleFactor = 10000,
  verbose = FALSE
)
data <- do.call("runSeuratNormalizeData", normalizeParams)

normalizeParams$inSCE <- NULL
metadata(data)$seurat$sctk$report$normalizeParams <- normalizeParams

cat("# Session Information\n\n")

sessionInfo()

compbiomed/singleCellTK documentation built on May 8, 2024, 6:58 p.m.