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

- 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

### 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]