DASC {DASC}R Documentation

Detecting hidden batch factors through data adaptive shrinkage and clustering (DASC)

Description

Detecting hidden batch factors through data adaptive shrinkage and clustering (DASC)

Batch factor detection via DASC (Data-adaptive Shrinkage and Clustering-DASC)

Usage

DASC(edata, pdata, factor, method = "ama", type = 3, lambda, rank, nrun,
  spanning = FALSE, annotation)

Arguments

edata

the normalized target matrix, a data.frame The row is gene, the column is sample

pdata

Phenotypic data summarizes information about the samples

factor

A factor vector which controls the convex clustering

method

Algorithm to use: 'admm' or 'ama'

type

An integer indicating the norm used: 1 = 1-norm 2 = 2-norm 3 = 2-norm^2

lambda

A double number A regularization parameter in the convex optimization

rank

integer sequence

nrun

the iteration numbers of Semi-NMF

spanning

parameter is assigned as false

annotation

An annotation of the dataset

Details

The DASC function is the main function of our algorithm DASC (Data-adaptive Shrinkage and Clustering-DASC) package. The DASC includes two main steps

Value

outputs the result of semi-NMF. It classifies each sample to its batch factor.

Author(s)

Haidong Yi, Ayush T. Raman

Haidong Yi, Ayush T. Raman

See Also

cvxclust_path_ama and cvxclust_path_admm for the detailed algorithm

Examples

library(NMF)
library(cvxclustr)
library(Biobase)
dat <- data.frame(matrix(rnbinom(n=200, mu=100, size=1/0.5), ncol=4))
pdat <- data.frame(sample = colnames(dat), type = c(rep('A',2), rep('B',2)))
rownames(pdat) <- colnames(dat)
res <- DASC(edata=dat, pdata=pdat, factor=pdat$type, method='ama', type=3,
lambda = 1, rank = 2, nrun = 50, spanning = FALSE,
annotation='simulated dataset')


[Package DASC version 1.1.0 Index]