About this Research Topic
High throughput technologies enable researchers to collect a large amount of information simultaneously, saving the cost of money and human labor. Numerous omics data were generated in the past decade, including genomics, epi-genomics, proteomics, etc. The technologies have evolved from microarray to bulk next-generation sequencing and then to single-cell next-generation sequencing. The development of new applications of such technologies was never stopped, such as RNA-seq, ChIP-seq, scRNA-seq, CITE-seq, scATAC-seq, etc. Such development created not only new opportunities to investigate mechanisms of cells but also new challenges for method and software development to analyze various omics data.
Statistical data science focuses on the method development and their applications to make sense from complex data, which has been widely applied to solve real-world problems in analyzing genomics, epigenomics and proteomics data. The present Research Topic aims to present novel statistical methodologies for analyzing various omics data, novel applications of statistical methods for generating interesting biological knowledge, as well as comprehensive evaluations of available tools for analyzing data generated by the most recent omics technologies. This collection will reflect the state-of-the-art in current research of statistical data science theory and applications in analyzing omics data.
This Research Topic will reflect the state-of-the-art in current research in genomics, epi-genomics, and proteomics, which focuses on statistical data science methods and applications. Examples of the topics (as an unconstrained open list) are predicting phenotypes using various omic information, pattern discovery in genomic data (such as clustering or dimension reduction methods), batch effect removal, cell type auto annotation, digital cytometry, Mendelian randomization for causal inference, integrated analysis of multi-omics data. Both the development of novel methodologies and interesting applications generating novel biological knowledge are welcome. We also consider including benchmark studies that comprehensively evaluate available methods for modern technologies.
Statistical data science focuses on the method development and their applications to make sense from complex data, which has been widely applied to solve real-world problems in analyzing genomics, epigenomics and proteomics data. The present Research Topic aims to present novel statistical methodologies for analyzing various omics data, novel applications of statistical methods for generating interesting biological knowledge, as well as comprehensive evaluations of available tools for analyzing data generated by the most recent omics technologies. This collection will reflect the state-of-the-art in current research of statistical data science theory and applications in analyzing omics data.
This Research Topic will reflect the state-of-the-art in current research in genomics, epi-genomics, and proteomics, which focuses on statistical data science methods and applications. Examples of the topics (as an unconstrained open list) are predicting phenotypes using various omic information, pattern discovery in genomic data (such as clustering or dimension reduction methods), batch effect removal, cell type auto annotation, digital cytometry, Mendelian randomization for causal inference, integrated analysis of multi-omics data. Both the development of novel methodologies and interesting applications generating novel biological knowledge are welcome. We also consider including benchmark studies that comprehensively evaluate available methods for modern technologies.
Keywords: Machine Learning, Genomics, Proteomics, Statistics, High-dimensional data, Data Science
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