A novel method for next-generation sequence data analysis using PLSA topic modeling technique

Abstract

The generation of substantial quantities of low-cost, high-quality next-generation sequences (NGS) has empowered mainstream researchers to address various biological and medical research problems. The vast information delivered by NGS technologies presents a big challenge for data processing, analysis, data mining, and text mining. This paper proposes a novel topic modeling technique for NGS data analysis using Probabilistic Latent Semantic Analysis (PLSA). The proposed method has four tasks: NGS dataset construction, preprocessing of data, topic modeling, and text mining using PLSA topic outputs. The NGS data of Salmonella enterica strains were used as the dataset in this procedure. The topic modeling performance is measured using standard clustering comparison measures such as Adjusted Rand Index, Normalized Mutual Information, Normalized Information Distance, and Normalized Variation of Information. The performance of PLSA topic modeling on NGS data was compared with Non-negative Matrix Factorization (NNMF) algorithm and existing Latent Dirichlet Allocation (LDA) algorithm. The Evaluations have shown that PLSA outperforms compare with NNMF and LDA topic models.