High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis

High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis Hui Tang1, Tao Zeng1* and Luonan Chen1,2,3,4* 1Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China 2CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China 3School of Life Science and Technology, ShanghaiTech University, Shanghai, China 4Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai, China Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. Here we propose an effective data integration framework named as HCI (High-order Correlation Integration), which takes an advantage of high-order correlation matrix incorporated with pattern fusion analysis (PFA), to realize high-dimensional data feature extraction. On the one hand, the high-order Pearson's correlation coefficient can highlight the latent patterns underlying noisy input datasets and thus improve the accuracy and robustness of the algorithms currently available for samp...
Source: Frontiers in Genetics - Category: Genetics & Stem Cells Source Type: research

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