---
title: "Single Cell Analysis with Seurat using singleCellTK Package"
author: "`r params$authors`"
date: "`r format(Sys.time(), '%B %d, %Y')`"
params:
  subtitle: !r NULL
  authors: !r NULL
  sce: !r NULL
  biological.group: !r NULL
  phenotype.groups: !r NULL
  variable.features: 2000
  pc.count: 50
  outputPath: !r NULL
  showSession: FALSE
  pdf: TRUE
  runHVG: TRUE
  plotHVG: TRUE
  runDimRed: TRUE
  plotJackStraw: FALSE
  plotElbowPlot: TRUE
  plotHeatmaps: TRUE
  runClustering: TRUE
  plotTSNE: TRUE
  plotUMAP: TRUE
  minResolution: 0.3
  maxResolution: 1.5 
  forceRun: FALSE
output:
  html_document:
    toc: yes
    toc_float: yes
    number_sections: yes
    theme: cosmo
    code_folding: hide
    self_contained: false
    keep_md: yes
  pdf_document:
    toc: yes
subtitle: "`r params$subtitle`"
editor_options:
  chunk_output_type: console
---


```{r Load Libraries Seurat Normalization, echo=FALSE, include = TRUE, message=FALSE, warning=FALSE}
#Loading required libraries
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)

data <- params$sce
sampleNames <- unique(colData(data)[[biological.group]])

numCells <- ncol(data)
numFeatures <- nrow(data)
numSamples <- length(sampleNames)

# Knitr Global Options
# dev <- ifelse(isTRUE(pdf), c("png"), c("png", "pdf"))
# opts_chunk$set(
#   echo = TRUE,
#   cache = FALSE,
#   cache.lazy = FALSE,
#   cache.comments = FALSE,
#   fig.align = "center",
#   fig.keep = "all",
#   dev = dev,
#   warning = FALSE
# )
```

<!-- Compute -->

<!-- Normalization -->
```{r, echo=FALSE, results='asis', message=FALSE}
headingNorm <- "##"
resNormalization <- knitr::knit_child('reportSeuratNormalizeData.Rmd', quiet = TRUE, envir = environment())
```

<!-- Feature Selection -->
```{r, echo=FALSE, results='asis'}
headingFS <- "##"
resFeatureSelection <- knitr::knit_child('reportSeuratFeatureSelection.Rmd', quiet = TRUE)
```

<!-- Scale Data -->
```{r, echo=FALSE, results='asis', warning=FALSE, message=FALSE}
headingSD <- "##"
resScaleData <- knitr::knit_child('reportSeuratScaleData.Rmd', quiet = TRUE)
```

<!-- Dimensionality Reduction -->
```{r, echo=FALSE, results='asis', warning=FALSE, message=FALSE}
headingDR <- "##"
headingDR2 <- "###"
significant_PC <- 10

resDimRed <- knitr::knit_child('reportSeuratDimRed.Rmd', quiet = TRUE, envir = environment())
```

<!-- Clustering -->
```{r, echo=FALSE, results='asis', warning=FALSE, message=FALSE}
numClusters <- 10 # this should be computed by the code itself (do later)
showClusterDesc <- TRUE
headingClust <- "##"

resClustering <- knitr::knit_child('reportSeuratClustering.Rmd', quiet = TRUE, envir = environment())
```

# Seurat Run

## Introduction
[Seurat](https://satijalab.org/seurat/) is a popular R package designed for the analysis of single-cell genomics data and offers a multitude of methods for processing and visualization. The singleCellTK package integrates these methods in an intuitive user-interface and all of which are also accessible through console-based wrapper functions. This comprehensive report summarizes the Seurat workflow for the input data by running all of the steps from the proposed pipeline including Normalization, Feature Selection, Scaling, Dimensionality Reduction, Clustering and Differential Expression, along with all of the supporting visualizations. A summary of the input data and results computed from this data is described below:

<!-- Populate RMD with Results -->
```{r, echo=FALSE, results='asis', warning=FALSE, message=FALSE}
cat(resNormalization, sep = '\n')
cat(resFeatureSelection, sep = '\n')
cat(resScaleData, sep = '\n')
cat(resDimRed, sep = '\n')
cat(resClustering, sep = '\n')
```

```{r, results='asis', eval=showSession, echo=FALSE}
cat("# Session Information\n\n")
```

```{r, results='markup', eval = showSession, echo = showSession}
sessionInfo()
```
