lassoSEM function

The Lasso penalty for SEM

Upon lambda_max to lambda_min in 20 step, the function compute 5 fold CV to determine the optimal lambda for the data.

lassoSEM(Y, X, Missing, B, verbose = 5)

Arguments

• Y: gene expression M by N matrix

• X: The network node attribute matrix with dimension of M by N. Theoretically, X can be L by N matrix, with L being the total node attributes. In current implementation, each node only allows one and only one attribute.

  If you have more than one attributes for some nodes, please consider selecting the top one by either correlation or principal component methods.

  If for some nodes there is no attribute available, fill in the rows with all zeros. See the yeast data yeast.rda for example.

  X is normalized inside the function.

• Missing: missing data in Y

• B: true network topology if available

• verbose: describe the information output from -1 - 10, larger number means more output

Details

the function perform CV and parameter inference, calculate power and FDR

Returns

• Bout: the matrix B from SEM

• fout: f: the weight for matrix X

• stat: compute the power and FDR statistics if the ture topology is provided

• simTime: computational time

References

1. Cai, X., Bazerque, J.A., and Giannakis, G.B. (2013). Inference of Gene Regulatory Networks with Sparse Structural Equation Models Exploiting Genetic Perturbations. PLoS Comput Biol 9, e1003068.

2. Huang, A. (2014). "Sparse model learning for inferring genotype and phenotype associations." Ph.D Dissertation. University of Miami(1186).

Author(s)

Anhui Huang

Examples

library(sparseSEM)
        data(B);
        data(Y);
        data(X);
        data(Missing);
        ## Not run: OUT <- lassoSEM(Y, X, Missing, B, verbose = 0);