---
title: "BatchQC package Introduction"
author: "Solaiappan Manimaran"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
    %\VignetteIndexEntry{BatchQCIntro}
    %\VignetteEngine{knitr::rmarkdown}
    \usepackage[utf8]{inputenc}
---
Sequencing and microarray samples often are collected or processed in multiple 
batches or at different times. This often produces technical biases that can 
lead to incorrect results in the downstream analysis. BatchQC is a software tool
that streamlines batch preprocessing and evaluation by providing interactive 
diagnostics, visualizations, and statistical analyses to explore the extent to 
which batch variation impacts the data. BatchQC diagnostics help determine 
whether batch adjustment needs to be done, and how correction should be applied 
before proceeding with a downstream analysis. Moreover, BatchQCvinteractively 
applies multiple common batch effect approaches to the data, and the user can 
quickly see the benefits of each method. BatchQC is developed as a Shiny App. 
The output is organized into multiple tabs, and each tab features an important 
part of the batch effect analysis and visualization of the data. The BatchQC 
interface has the following analysis groups: Summary, Differential Expression, 
Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat 
and SVA. 

The package includes:

1. Summary and Sample Diagnostics
2. Differential Expression Plots and Analysis using LIMMA
3. Principal Component Analysis and plots to check batch effects
4. Heatmap plot of gene expressions
5. Median Correlation Plot
6. Circular Dendrogram clustered and colored by batch and condition
7. Shape Analysis for the distribution curve based on HTShape package
8. Batch Adjustment using ComBat
9. Surrogate Variable Analysis using sva package
10. Function to generate simulated RNA-Seq data

`batchQC` is the pipeline function that generates the BatchQC report and 
launches the Shiny App in interactive mode. It combines all the functions into 
one step.

## Installation

To begin, install [Bioconductor](http://www.bioconductor.org/) and simply
run the following to automatically install BatchQC and all the dependencies, 
except pandoc, which you have to manually install as follows.

```r
source("http://bioconductor.org/biocLite.R")
biocLite("BatchQC")
```
Install 'pandoc' package by following the instructions at the following URL:
http://pandoc.org/installing.html

Rstudio also provides pandoc binaries at the following location for Windows, 
Linux and Mac:
https://s3.amazonaws.com/rstudio-buildtools/pandoc-1.13.1.zip 

If all went well you should now be able to load BatchQC. Here is an example 
usage of the pipeline.

### Simulate data and Apply BatchQC
```r
library(BatchQC)
nbatch <- 3
ncond <- 2
npercond <- 10
data.matrix <- rnaseq_sim(ngenes=50, nbatch=nbatch, ncond=ncond, npercond=
    npercond, basemean=10000, ggstep=50, bbstep=2000, ccstep=800, 
    basedisp=100, bdispstep=-10, swvar=1000, seed=1234)
batch <- rep(1:nbatch, each=ncond*npercond)
condition <- rep(rep(1:ncond, each=npercond), nbatch)
batchQC(data.matrix, batch=batch, condition=condition, 
        report_file="batchqc_report.html", report_dir=".", 
        report_option_binary="111111111",
        view_report=FALSE, interactive=TRUE, batchqc_output=TRUE)

```
### Apply combat and rerun the BatchQC pipeline on the batch adjusted data
```r
nsample <- nbatch*ncond*npercond
sample <- 1:nsample
pdata <- data.frame(sample, batch, condition)
modmatrix = model.matrix(~as.factor(condition), data=pdata)
combat_data.matrix = ComBat(dat=data.matrix, batch=batch, mod=modmatrix)
batchQC(combat_data.matrix, batch=batch, condition=condition, 
        report_file="batchqc_combat_adj_report.html", report_dir=".", 
        report_option_binary="110011111",
        interactive=FALSE)
```

### Apply BatchQC on a real signature dataset
```r
library(BatchQC)
data(example_batchqc_data)
batch <- batch_indicator$V1
condition <- batch_indicator$V2
batchQC(signature_data, batch=batch, condition=condition, 
        report_file="batchqc_signature_data_report.html", report_dir=".", 
        report_option_binary="111111111",
        view_report=FALSE, interactive=TRUE)
```
### Apply BatchQC on a real bladderbatch dataset
```r
library(BatchQC)
library(bladderbatch)
data(bladderdata)
pheno <- pData(bladderEset)
edata <- exprs(bladderEset)
batch <- pheno$batch  ### note 5 batches, 3 covariate levels. Batch 1 contains 
### only cancer, 2 and 3 have cancer and controls, 4 contains only biopsy, and 
### 5 contains cancer and biopsy
condition <- pheno$cancer
batchQC(edata, batch=batch, condition=condition, 
        report_file="batchqc_report.html", report_dir=".", 
        report_option_binary="111111111",
        view_report=FALSE, interactive=TRUE)
```
### Apply BatchQC on a real protein expression dataset
```r
library(BatchQC)
data(protein_example_data)
batchQC(protein_data, protein_sample_info$Batch, protein_sample_info$category,
        report_file="batchqc_protein_data_report.html", report_dir=".", 
        report_option_binary="111111111",
        view_report=FALSE, interactive=TRUE)
```
### Second simulated dataset example with only batch variance difference
```r
library(BatchQC)
nbatch <- 3
ncond <- 2
npercond <- 10
data.matrix <- rnaseq_sim(ngenes=50, nbatch=nbatch, ncond=ncond, npercond=
    npercond, basemean=5000, ggstep=50, bbstep=0, ccstep=2000, 
    basedisp=10, bdispstep=-4, swvar=1000, seed=1234)

### apply BatchQC
batch <- rep(1:nbatch, each=ncond*npercond)
condition <- rep(rep(1:ncond, each=npercond), nbatch)
batchQC(data.matrix, batch=batch, condition=condition, 
        report_file="batchqc_report.html", report_dir=".", 
        report_option_binary="111111111",
        view_report=FALSE, interactive=TRUE, batchqc_output=TRUE)

```
