---
title: "Quick start of BALLI package"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Quick start of BALLI package}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

# Quick Start

This is an quick start manual of **BALLI**

```{r load-packages, message=FALSE, warning=F}
require(BALLI)
```

### 1. Load Count Data 

```
data <- data.frame(read.table("counts.txt"))
```

or make example count data 

```{r}
GenerateData <- function(nRow) {
	expr_mean <- runif(1,10,100)
	expr_size <- runif(1,1,10)
	expr <- rnbinom(20,mu=expr_mean,size=expr_size)
	return(expr)
}

data <- data.frame(t(sapply(1:10000,GenerateData)))
colnames(data) <- c(paste0("A",1:10),paste0("B",1:10))
rownames(data) <- paste0("gene",1:10000)
head(data)
```

### 2. Designate Group Information and Make Design Matrix 

```{r}
Group <- c(rep("A",10),rep("B",10))
Group
```
```{r}
design <- model.matrix(~Group, data = data)
head(design)
```

### 3. Normalize Count Data

```{r}
dge <- DGEList(counts=data, group=Group)
dge <- calcNormFactors(dge)
dge
```

### 4. Estimate Technical Variance

```{r}
tV <- tecVarEstim(dge,design)
tV
```

### 5. Fit BALLI and See Top Significant Genes

```{r}
fit <- balli(tV,intV=2)
fit
```