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

## Description

Ridge regression on multiple conditions, initialization of FSSEM algorithm

## Usage

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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 Dec. 4, 2019, 5:06 p.m.