# Compositional Sparse Canonical Correlation Analysis A compositional sparse canonical correlation analysis (csCCA) framework for integrating microbiome data with other high-dimensional omics data. ```r 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. ## Returns `a.new` the estimated coefficient vector. ## Examples ```r ## 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) ``` ## 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.

cscca function