---
title: "Gene Detection Analysis for scRNA-seq"
author: "Ruoxin Li, Gerald Quon"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Gene Detection Analysis for scRNA-seq}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r,echo = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)
```

# Introduction

This tutorial provides an example analysis for 
modelling gene detection pattern as outlined in
[R.Li et al, 2018](https://www.biorxiv.org/content/10.1101/454629v1). 
The goal of this tutorial is to provide an overview of the cell type 
classification and visualization tasks by learning a low dimensional embedding 
through a class of gene detection models: that is BFA and Binary PCA.

## Summary of workflow
The following workflow summarizes a typical dimensionality reduction
procedure performed by BFA or Binary PCA. 

1. Data processing
2. Dimensionality reduction
3. Visualization

## Installation

Let's start with the installation 

```{r,eval=FALSE,include=TRUE,results="hide",message=FALSE,warning=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("scBFA")
```

next we can load dependent packages

```{r,include=TRUE,results="hide",message=FALSE,warning=FALSE}
library(zinbwave)
library(SingleCellExperiment)
library(ggplot2)
library(scBFA)
```


## Information of example dataset
The example dataset is generated from our scRNA-seq pre-DC/cDC dataset sourced 
from
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89232
After performing all quality control procedure of genes and cells
(as outlined in the paper), we then select 500 most variable genes for 
illustration purpose. The example dataset consists of 950 cells and 500 genes
```{r}

# raw counts matrix with rows are genes and columns are cells
data("exprdata")

# a vector specify the ground truth of cell types provided by conquer database
data("celltype")


```


## Working with SingleCellExperiment class
The design of BFA and Binary PCA allows three kinds of input object(scData):
\begin{itemize}
\item 1. A raw count matrix in which rows are genes and columns are cells. 
\item 2. A SingleCellExperiment class which at least contains the raw count
matrix 
\item 3. A Seurat class which at least contains the raw count matrix
\end{itemize}
For illustration, here we construct a singleCellExperiment class to be the 
input of BFA and Binary PCA.
```{r}
sce <- SingleCellExperiment(assay = list(counts = exprdata))
```

## Gene Detection Model analysis
### Binary Factor Analysis
Let $N$ stands for number of cells, $G$ stands for the number of genes, and $K$
stands for the number of latent dimensions.

A bfa model object computes the following 
parameters after fitting the gene detection matrix.

1. $Z$ is $N$ by $K$ embedding matrix and is named as "ZZ" in the model object
2. $A$ is $G$ by $K$ loading matrix  and is named as "AA" in the model object
3. $\beta$ if there is cell-level covariates (e.g batch effect),
$\beta$ is corresponding coefficient matrix and is named as "beta" in 
the model object
4. $\gamma$ if there is gene-level covariates (e.g QC measures), 
$\gamma$ is corresponding coefficient matrix and is named as "gamma" in 
the model object


We choose 3 as number of latent dimensions and project the gene detection 
matrix on the embedding space.
```{r,include=TRUE,results="hide",message=FALSE}
bfa_model = scBFA(scData = sce, numFactors = 2) 
```

We then visualize the low dimensional embedding of BFA in tSNE space.
Points are colored by their corresponding cell types.

```{r,fig.width=8, fig.height=6}
set.seed(5)
df = as.data.frame(bfa_model$ZZ)
df$celltype = celltype

p1 <- ggplot(df,aes(x = V1,y = V2,colour = celltype))
p1 <- p1 + geom_jitter(size=2.5,alpha = 0.8) 
colorvalue <- c("#43d5f9","#24b71f","#E41A1C", "#ffc935","#3d014c","#39ddb2",
                "slateblue2","maroon","#f7df27","palevioletred1","olivedrab3",
                "#377EB8","#5043c1","blue","aquamarine2","chartreuse4",
                "burlywood2","indianred1","mediumorchid1")
p1 <- p1 + xlab("tsne axis 1") + ylab("tsne axis 2") 
p1 <- p1 + scale_color_manual(values = colorvalue)
p1 <- p1 + theme(panel.background = element_blank(),
                  legend.position = "right",
                  axis.text=element_blank(),
                  axis.line.x = element_line(color="black"),
                  axis.line.y = element_line(color="black"),
                  plot.title = element_blank()
                   )
p1
```
 
### Binary PCA 
```{r}
bpca = BinaryPCA(scData = sce) 
```
We then visualize the low dimensional embedding of Binary PCA in tSNE space.
Points are colored by their corresponding cell types.
```{r,fig.width=8, fig.height=6}

df = as.data.frame(bpca$x[,c(1:2)])
colnames(df) = c("V1","V2")
df$celltype = celltype

p1 <- ggplot(df,aes(x = V1,y = V2,colour = celltype))
p1 <- p1 + geom_jitter(size=2.5,alpha = 0.8) 
colorvalue <- c("#43d5f9","#24b71f","#E41A1C", "#ffc935","#3d014c","#39ddb2",
                "slateblue2","maroon","#f7df27","palevioletred1","olivedrab3",
                "#377EB8","#5043c1","blue","aquamarine2","chartreuse4",
                "burlywood2","indianred1","mediumorchid1")
p1 <- p1 + xlab("tsne axis 1") + ylab("tsne axis 2") 
p1 <- p1 + scale_color_manual(values = colorvalue)
p1 <- p1 + theme(panel.background = element_blank(),
                legend.position = "right",
                axis.text=element_blank(),
                axis.line.x = element_line(color="black"),
                axis.line.y = element_line(color="black"),
                plot.title = element_blank()
                )
p1
```

## Session Info
```{r}
sessionInfo()
```
