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Last data update: 2014.03.03

R: Discriminant Non-Negative Matrix Factorization.

DNMF	R Documentation

Discriminant Non-Negative Matrix Factorization.

Description

Discriminant Non-Negative Matrix Factorization, DNMF, is to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner.

Usage

DNMF(data, trainlabel, r = 2, gamma = 0.1, delta = 1e-04,
  maxIter = 1000, tol = 1e-07, log = TRUE, plotit = FALSE,
  checkH = TRUE, ...)

Arguments

`data`	a matrix, like expression profilings of some samples. the columns are samples and the rows are gene's expression.
`trainlabel`	a numeric vector of sample type of all the samples, this vector should ONLY contain 1 and 2 so far and length of it should equal the column (sample) size of data.
`r`	the dimension of expected reduction dimension, with the default value 2.
`gamma`	the tradeoff value for the within scatter matrix, with the default value 0.1.
`delta`	the tradeoff value for the between scatter matrix, with the default value 1e-4.
`maxIter`	the maximum iteration of update rules, with the default value 1000.
`tol`	the toleration of coverange, with the default value 1e-7.
`log`	log2 data. Default is TRUE.
`plotit`	whether plot H (V=WH). Default: FALSE.
`checkH`	whether or not check H. Default: TRUE. This parameter aims to check whether or not the H safisfy the discriminant metagenes. Usually, this should be TRUE.
`...`	to gplots::heatmap.2

Details

The main algorithm is based on Zafeiriou, S., et al. (2006) Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification, IEEE transactions on neural networks, 17, 683-695, with some CORRECTIONs.

Author(s)

Zhilong Jia and Xiang Zhang

Examples

dat <- rbind(matrix(c(rep(3, 16), rep(8, 24)), ncol=5),
matrix(c(rep(5, 16), rep(5, 24)), ncol=5),
matrix(c(rep(18, 16), rep(7, 24)), ncol=5)) +
matrix(runif(120,-1,1), ncol=5)
trainlabel <- c(1,1,2,2,2)

DNMF_result <- DNMF(dat, trainlabel, r=2)


## Not run: 
# Gene ranking. dat is the raw read count maatrix with sample in column.

#normalising dat
Sizefactors <- DESeq::estimateSizeFactorsForMatrix(dat)
dat = sweep(dat, 2, Sizefactors, `/`)

res <- DNMF(dat, trainlabel, r=2)
rnk <- res$rnk

#The end of gene ranking exmaples

#Other exmaples
DNMF_result <- DNMF(dat, trainlabel, r=2, gamma=0.1, delta=0.0001, plotit=TRUE)

## End(Not run)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(DNMF)
Loading required package: foreach
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DNMF/DNMF.Rd_%03d_medium.png", width=480, height=480)
> ### Name: DNMF
> ### Title: Discriminant Non-Negative Matrix Factorization.
> ### Aliases: DNMF
> 
> ### ** Examples
> 
> dat <- rbind(matrix(c(rep(3, 16), rep(8, 24)), ncol=5),
+ matrix(c(rep(5, 16), rep(5, 24)), ncol=5),
+ matrix(c(rep(18, 16), rep(7, 24)), ncol=5)) +
+ matrix(runif(120,-1,1), ncol=5)
> trainlabel <- c(1,1,2,2,2)
> 
> DNMF_result <- DNMF(dat, trainlabel, r=2)
> 
> 
> ## Not run: 
> ##D # Gene ranking. dat is the raw read count maatrix with sample in column.
> ##D 
> ##D #normalising dat
> ##D Sizefactors <- DESeq::estimateSizeFactorsForMatrix(dat)
> ##D dat = sweep(dat, 2, Sizefactors, `/`)
> ##D 
> ##D res <- DNMF(dat, trainlabel, r=2)
> ##D rnk <- res$rnk
> ##D 
> ##D #The end of gene ranking exmaples
> ##D 
> ##D #Other exmaples
> ##D DNMF_result <- DNMF(dat, trainlabel, r=2, gamma=0.1, delta=0.0001, plotit=TRUE)
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>