---
title: "BatchQC Examples"
author: "Solaiappan Manimaran"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
    %\VignetteIndexEntry{BatchQC_examples}
    %\VignetteEngine{knitr::rmarkdown}
    \usepackage[utf8]{inputenc}
---
### Example 1: Simulated dataset
The simulated data consists of three batches and two conditions, 
and expression measurements for 50 genes
```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)

```

### Example 2: Real signature dataset
This dataset is from signature data captured when activating different growth 
pathway genes in human mammary epithelial cells (GEO accession: GSE73628). 
This data consists of three batches and ten different conditions corresponding 
to control and nine different pathways
```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)
```

### Example 3: Real bladderbatch dataset
This dataset has 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
```r
library(BatchQC)
library(bladderbatch)
data(bladderdata)
pheno <- pData(bladderEset)
edata <- exprs(bladderEset)
batch <- pheno$batch  
condition <- pheno$cancer
batchQC(edata, batch=batch, condition=condition, 
        report_file="batchqc_report.html", report_dir=".", 
        report_option_binary="111111111",
        view_report=FALSE, interactive=TRUE)
```
### Example 4: Real protein expression dataset
This dataset is from protein expression data captured for 39 proteins.
It has two batches and two conditions corresponding to case and control.
```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)
```
### Example 5: Second simulated dataset with only batch variance difference
The simulated data consists of three batches and two conditions, 
and expression measurements for 50 genes. In this dataset, there is no
difference in batch mean but only difference in batch variance from
batch to batch.
```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)

```
