MicrobiomeStat (version 1.4)

cscca: 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
)

Value

a.new the estimated coefficient vector.

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.

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

if (FALSE) {
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"))
   }