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()
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