---
title: "Seurat Normalization"
author: "`r params$authors`"
date: "`r format(Sys.time(), '%B %d, %Y')`"
params:
  subtitle: !r NULL
  authors: !r NULL
  sce: !r NULL
  outputPath: !r NULL
  showSession: TRUE
  pdf: TRUE
  forceRun: FALSE
output:
  html_document:
    toc: yes
    toc_float: yes
    number_sections: yes
    theme: cosmo
    code_folding: hide
    self_contained: false
  pdf_document:
    toc: yes
subtitle: "`r params$subtitle`"
editor_options:
  chunk_output_type: console
---
```{r, echo=FALSE, message=FALSE, warning=FALSE}
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)
```

```{r, echo=FALSE, results='asis', warning=FALSE, message=FALSE}
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. 

```{r, message=FALSE, warning=FALSE, eval=is.null(metadata(data)$seurat$normAssay) || forceRun}
normalizeParams <- list(
  inSCE = data,
  normalizationMethod = "LogNormalize",
  scaleFactor = 10000,
  verbose = FALSE
)
data <- do.call("runSeuratNormalizeData", normalizeParams)
```

```{r, echo=FALSE, include=FALSE, eval=(is.null(metadata(data)$seurat$normAssay))}
normalizeParams$inSCE <- NULL
metadata(data)$seurat$sctk$report$normalizeParams <- normalizeParams
```

