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("headingSD")) headingSD <- "#"

cat(headingSD, " Scale Data {}\n\n")

Standardizing gene expression values is considered necessary to ensure that dimensionality reduction tools produce unbiased results due to high variance of some features. For this purpose, Seurat performs a z-score scaling on the input matrix and standardizes (centers and scales) the expression values of the variable features identified previously before they are input to the downstream methods.

The variable features identified previously were scaled using z-score scaling method and the output expression values were clipped to maximum and minimum value of 10 & -10 respectively.

data <- runSeuratScaleData(inSCE = data, model = "linear", verbose = FALSE)

scaleParams <- list(
  model = "linear", 
  verbose = FALSE)
metadata(data)$seurat$sctk$report$scaleParams <- scaleParams

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

sessionInfo()

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