Robust Angle Based Joint and Individual Variation Explained.

RaJIVE

RaJIVE (Robust Angle based Joint and Individual Variation Explained) is a robust alternative to the aJIVE method for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust with respect to outliers and other types of noises present in the data.

Installation

You can install the released version of RaJIVE from CRAN with:

install.packages("RaJIVE")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("ericaponzi/RaJIVE")

Example

This is a basic example which shows how to use RaJIVE on simple simulated data:

Running robust aJIVE

library(RaJIVE)
## basic example code
n <- 50
pks <- c(100, 80, 50)
Y <- ajive.data.sim(K =3, rankJ = 3, rankA = c(7, 6, 4), n = n,
                   pks = pks, dist.type = 1)

initial_signal_ranks <-  c(7, 6, 4)
data.ajive <- list((Y$sim_data[[1]]), (Y$sim_data[[2]]), (Y$sim_data[[3]]))
ajive.results.robust <- Rajive(data.ajive, initial_signal_ranks)

The function returns a list containing the aJIVE decomposition, with the joint component (shared across data sources), individual component (data source specific) and residual component for each data source.

Visualizing results:

• Joint rank:

get_joint_rank(ajive.results.robust)
#> [1] 3

• Individual ranks:

get_individual_rank(ajive.results.robust, 1)
#> [1] 5
get_individual_rank(ajive.results.robust, 2)
#> [1] 3
get_individual_rank(ajive.results.robust, 3)
#> [1] 1

• Heatmap decomposition:

decomposition_heatmaps_robustH(data.ajive, ajive.results.robust)

• Proportion of variance explained:

showVarExplained_robust(ajive.results.robust, data.ajive)
#> $Joint
#> [1] 0.3148569 0.3349692 0.4197429
#> 
#> $Indiv
#> [1] 0.5499653 0.4156423 0.1522468
#> 
#> $Resid
#> [1] 0.1351778 0.2493886 0.4280103