multiRegression: multiRegression

In fssemR: Fused Sparse Structural Equation Models to Jointly Infer Gene Regulatory Network

multiRegression

Description

Ridge regression on multiple conditions, initialization of FSSEM algorithm

Usage

multiRegression(Xs, Ys, Sk, gamma, n, p, k, trans = FALSE)

Arguments

Xs	eQTL matrices. eQTL matrix can be matrix/list of multiple conditions
Ys	Gene expression matrices
Sk	eQTL index of genes
gamma	Hyperparameter for ridge regression
n	number of observations
p	number of genes
k	number of eQTLs
trans	if rows for sample, trans = TRUE, otherwise, trans = FALSE. Default FALSE

Value

fit List of SEM model

Bs

coefficient matrices of gene regulatory networks

fs

eQTL's coefficients w.r.t each gene

Fs

coefficient matrices of eQTL-gene effect

mu

Bias vector

sigma2

estimate of covariance in SEM

Examples

seed = 1234
N = 100                                           # sample size
Ng = 5                                            # gene number
Nk = 5 * 3                                        # eQTL number
Ns = 1                                            # sparse ratio
sigma2 = 0.01                                     # sigma2
set.seed(seed)
data = randomFSSEMdata(n = N, p = Ng, k = Nk, sparse = Ns, df = 0.3, sigma2 = sigma2,
                       u = 5, type = "DG", nhub = 1, dag = TRUE)
## If we assume that different condition has different genetics perturbations (eQTLs)
## data$Data$X = list(data$Data$X, data$Data$X)
gamma = cv.multiRegression(data$Data$X, data$Data$Y, data$Data$Sk, ngamma = 20, nfold = 5,
                           N, Ng, Nk)
fit   = multiRegression(data$Data$X, data$Data$Y, data$Data$Sk, gamma, N, Ng, Nk,
                      trans = FALSE)

fssemR documentation built on March 18, 2022, 7:24 p.m.