Haplotype Estimation Using Low-Rank Matrix Factorization in Presence of Outliers

Haplotype estimates based on DNA information are used to detect human genetic diseases. This problem can be modeled in the genomic processing of signals as a low-rank matrix in which only a few elements are observed. As a result, an effective way to estimate the haplotype from incomplete observations is to use matrix completion methods. In this paper, using matrix completion methods, an attempt has been made to estimate the haplotype through matrix factorization. In references, the reduction gradient method has been used to solve the problem. However, in the previous methods, outliers were also included in the calculations, which caused an error in the haplotype estimation. In other words, these methods do not pay attention to the existing conditions for haplotype matrices, and this has led to outdated estimates for haplotypes. In this paper, with the matrix completion method and considering these conditions in the haplotype matrix, we introduce a new cost function as a penalty expression for haplotype estimation. The new expression added to the cost function reduces the effect of skewed data and thus increases the accuracy of haplotype estimates. The simulation results confirm the need to reduce the haplotype retrieval error