https://github.com/Pennisetum/JACA

R package to implements a joint framework that performs association and classification analysis for multi-view data
https://github.com/Pennisetum/JACA

Last synced: about 8 hours ago
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R package to implements a joint framework that performs association and classification analysis for multi-view data

• Host: GitHub
• URL: https://github.com/Pennisetum/JACA
• Owner: Pennisetum
• Created: 2018-12-10T00:23:40.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2021-04-26T20:54:22.000Z (over 3 years ago)
• Last Synced: 2024-08-02T20:43:40.221Z (3 months ago)
• Language: R
• Homepage:
• Size: 75.2 KB
• Stars: 3
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-multi-omics - JACA - Zhang - Joint Association and Classification Analysis - [paper](https://arxiv.org/abs/1811.08511) (Software packages and methods / Multi-omics correlation or factor analysis)

README

        
# JACA: Joint association and classification analysis of multi-view data

The R package 'JACA' implements a joint statistical framework that  performs association and classification analysis for multi-view data, where multi-view data refers to matched sets of measurements on the same subjects. The corresponding reference is

["Joint association and classification analysis of multi-view data" by Zhang and Gaynanova](https://arxiv.org/abs/1811.08511) (2018+).

## Installation

To install the latest version from Github, use

```s

library(devtools)

devtools::install_github("Pennisetum/JACA")

```

## Usage

```s

library(JACA)

# Example

set.seed(1)

# Generate class indicator matrix Z

n = 100

Z=matrix(c(rep(1, n),rep(0, 2 * n)), byrow = FALSE, nrow = n)

for(i in 1:n){

 Z[i, ] = sample(Z[i, ])

}

# Generate input data X_list

d = 2

X_list = sapply(1:d, function(i) list(matrix(rnorm(n * 20), n, 20)))

# Train JACA model

W = jacaTrain(Z, X_list, lambda = rep(0.05, 2), verbose = FALSE, alpha= 0.5, rho = 0.2)

# Show the number of non-zero rows of each matrix of discriminant vectors

sapply(W, function(x) sum(rowSums(x) != 0))

# Test semi supervised learning

# Set certain class labels and subsets of views as missing 

Z[90:100, ] = rep(NA, 3)

X_list[[1]][1:10, ] = NA

X_list[[2]][11:20, ] = NA

W = jacaTrain(Z, X_list, kmax = 200, eps = 1e-06, lambda = rep(0.05, 2),alpha = 0.5, rho = 0.2, missing = TRUE)

# Show the number of non-zero rows of each matrix of discriminant vectors

sapply(W, function(x) sum(rowSums(x) != 0))