DASC: Detecting hidden batch factors through data adaptive...

In zhanglabNKU/DASC: Detecting hidden batch factors through data adaptive adjustment for biological effects

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, countable = TRUE, 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
countable	mark whether edata is countable
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

• Data-adaptive shrinkage using convex clustering shrinkage (Implemented by convex optimization.);

• Extract batch factors using matrix factorization.

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

zhanglabNKU/DASC documentation built on May 27, 2019, 1:46 p.m.