In idc9/r_jive: Angle Based Joint and Individual Variation Explained

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ajive: Angle based Joint and Individual Variation Explained

Author: Iain Carmichael
License: MIT

Additional documentation, examples and code revisions are coming soon. For questions, issues or feature requests please reach out to Iain: iain@unc.edu.

Overview

Angle based Joint and Individual Variation Explained (AJIVE) is a dimensionality reduction algorithm for the multi-block setting i.e. $K$ different data matrices, with the same set of observations and (possibly) different numbers of variables. AJIVE finds joint modes of variation which are common to all $K$ data blocks as well as modes of individual variation which are specific to each block. For a detailed discussion of AJIVE see Angle-Based Joint and Individual Variation Explained.

A python version of this package can be found here.

Installation

The ajive package is currently available using devtools

# install.packages('devtools')
devtools::install_github("idc9/r_jive")

Example

Consider the following two block toy example: the first block has 200 observations (rows) and 100 variables; the second block has the same set of 200 observations and 500 variables (similar to Figure 2 of the AJIVE paper).

library(ajive)

# sample a toy dataset with true joint rank of 1
blocks <- sample_toy_data(n=200, dx=100, dy=500)

data_blocks_heatmap(blocks, show_color_bar=FALSE)

After selecting the initial signal ranks we can compute the AJIVE decomposition using the ajive function.

initial_signal_ranks <- c(2, 3) # set by looking at scree plots
jive_results <- ajive(blocks, initial_signal_ranks, 
                      n_wedin_samples = 100, n_rand_dir_samples = 100)

# estimated joint rank
jive_results$joint_rank

The heatmap below shows that AJIVE separates the joint and individual signals for this toy data set.

decomposition_heatmaps(blocks, jive_results)

Using notation from Section 3 of the AJIVE paper (where u means scores and v means loadings) we can get the jive data out as follows

# common normalized scores
dim(jive_results$joint_scores)

# Full matrix representation of the joint signal for the first block
dim(jive_results$block_decomps[[1]][['joint']][['full']])

# joint block specific scores for the first block
dim(jive_results$block_decomps[[1]][['joint']][['u']])

# joint block specific loadings for the first block
dim(jive_results$block_decomps[[1]][['joint']][['v']])

# individual block specific scores for the second block
dim(jive_results$block_decomps[[2]][['individual']][['u']])

Help and Support

Additional documentation, examples and code revisions are coming soon. For questions, issues or feature requests please reach out to Iain: idc9@cornell.edu.