| balli {BALLI} | R Documentation |
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)