BALLI (version 0.2.0)

balli: BALLI

Description

DEG analysis using BALLI algorithm

Usage

balli(object, intV = 2, logcpm = NULL, tecVar = NULL,
  design = NULL, numCores = NULL, threshold = 1e-06, maxiter = 200)

Arguments

object

a TecVarList object

intV

numeric vector designating interest variable(s) which is(are) column number(s) of design matrix

logcpm

logcpm values for each gene and each sample

tecVar

estimated technical variance values for each gene and each sample

design

design matrix with samples in row and covariable(s) to be estimated in column

numCores

number of cores to be used for multithreding. If NULL, a single core is used

threshold

threshold for convergence

maxiter

maximum number of iteration to converge of estimated biological variance. If not, biological variance is estimated by using Brent method

Value

an Balli object including Result and topGenes list. Following components are shown by Result (same order of genes with input data) and topGenes (ordered by pBALLI in Result) :

log2FC

log2 fold changes of interest variable(s)

lLLI

log-likelihoods estimated by LLI

lBALLI

log-likelihoods estimated by BALLI

pLLI

p-values estimated by LLI

pBALLI

p-values estimated by BALLI

BCF

Bartlett's correction factor

expr <- data.frame(t(sapply(1:1000,function(x)rnbinom(20,mu=500,size=50)))) group <- c(rep("A",10),rep("B",10)) design <- model.matrix(~group, data = expr) dge <- DGEList(counts=expr, group=group) dge <- calcNormFactors(dge) tV <- tecVarEstim(dge,design) balli(tV,intV=2)