cscca: Compositional Sparse Canonical Correlation Analysis

In MicrobiomeStat: Statistical Methods for Microbiome Compositional Data

Compositional Sparse Canonical Correlation Analysis

Description

A compositional sparse canonical correlation analysis (csCCA) framework for integrating microbiome data with other high-dimensional omics data.

Usage

cscca(
  Y,
  View.ind,
  lambda.seq,
  a.old = NULL,
  View.type = NULL,
  eps.stop = 1e-04,
  max.step = 30,
  eps = 1e-04,
  T.step = 1
)

Arguments

Y	a n*(K*p) matrix representing the observations.
View.ind	a (K*p) integer vector indicating the classes of features. The features with the same View.ind is in the same class.
lambda.seq	a K vector consisting of hyper-parameters.
a.old	Optional initial value for the coefficient vector a.new.
View.type	a K vector encoding the structure type of each feature class. There are two choices: "O" (Omics Data),"C" (Compositional Data).
eps.stop	a numerical value controlling the convergence.
max.step	an integer controlling the maximum step for interaction.
eps	a numerical value controlling the convergence.
T.step	an integer controlling the maximum step for interaction.

Value

a.new the estimated coefficient vector.

References

1. Deng, L., Tang, Y., Zhang, X., et al. (2024). Structure-adaptive canonical correlation analysis for microbiome multi-omics data. Frontiers in Genetics, 15, 1489694.

2. Chen, J., Bushman, F. D., Lewis, J. D., et al. (2013). Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis. Biostatistics, 14(2), 244–258.

Examples

## Not run: 
library(dplyr)

n <- 200
p <- q <- 100
sigma.nu <- 5
sigma.eps <- 1
omega_X <- 0.85*c(rep(1/10,9),-9/10,rep(0,p-10))
omega_Y <- 0.85*c(seq(0.08,0.12,length = 10),rep(0,q-10))
Data1 <- DGP_OC(seed=10,n,p,q,sigma.nu,sigma.eps,omega_X,omega_Y)

library(mlrMBO)
Res.sCCA.CV <- cscca.CV(Y=Data1$Y,View.ind=Data1$View.ind,
                          View.type=c("O","O"),
                          show.info = TRUE)


Res.CsCCA.CV <- cscca.CV(Y=Data1$Y,View.ind=Data1$View.ind,
                                   View.type=c("O","C"),
                                   show.info = TRUE)

Res.sCCA <- cscca(Y=Data1$Y,View.ind=Data1$View.ind,
                     lambda.seq=Res.sCCA.CV$lam.opt.trgt,
                     View.type=c("O","O"))
Res.CsCCA <- cscca(Y=Data1$Y,View.ind=Data1$View.ind,
                     lambda.seq=Res.CsCCA.CV$lam.opt.trgt,
                     View.type=c("O","C"))
   
## End(Not run)

MicrobiomeStat documentation built on Jan. 9, 2026, 1:07 a.m.