Double shrinking (DOSH), a regression-based algorithm for gene regulatory network inference from co-expression data

Abstract Next generation sequencing allows obtaining large amounts of gene expression data. Inferring regulatory relations between genes from such data has been a long standing challenge. Current algorithms are based on linear regression or a distance measures, like partial correlation or mutual information. The majority of algorithms are of very broad nature. Their aim is to infer a random normal network without using any additional assumptions. Regularized regression algorithms are an exception, since they assume sparsity. Additional truthful assumptions make inferences easier and more accurate. The proposed algorithm, Double Shrinking (DOSH), is based on regularized regression with assumptions about gene expression data and network structure. Reliability of gene expression values is assumed to depend upon their magnitude; larger values are more reliable. Each gene is assumed to be completely predictable from other genes. The effectiveness of the algorithm is demonstrated by identifying genetic markers of survival in lymphoid leukemia.