Quick start of BALLI package

In BALLI: Expression RNA-Seq Data Analysis Based on Linear Mixed Model

Quick Start

This is an quick start manual of BALLI

require(BALLI)

1. Load Count Data

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

or make example count data

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

Group <- c(rep("A",10),rep("B",10))
Group

design <- model.matrix(~Group, data = data)
head(design)

3. Normalize Count Data

dge <- DGEList(counts=data, group=Group)
dge <- calcNormFactors(dge)
dge

4. Estimate Technical Variance

tV <- tecVarEstim(dge,design)
tV

5. Fit BALLI and See Top Significant Genes

fit <- balli(tV,intV=2)
fit

BALLI documentation built on May 2, 2019, 7:58 a.m.