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Disease Diagnosis based on Single Cell Sequencing

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Project Quick Facts

Principal Investigator

  • Prof. WEI Yingying

    Department of Statistics

  • Funding Sources

    Research Grants Council

  • Collaboration

    The Chinese University of Hong Kong (Shenzhen)

    The Second Affiliated Hospital of Medical College of Zhejiang University

  • Award

    2019 W. J. Youden Award in Interlaboratory Testing by American Statistical Association

    2019 Distinguished Student Paper Award, The Section on Statistics in Genomics and Genetics of the American Statistical Association

Our team developed a novel statistical method that can accurately cluster cells into cell types, estimate cellular compositions and identify cell types whose abundances differ between patients and healthy individuals using single cell sequencing data. The accuracy of our proposed method in clustering cells into cell types outperforms the state-of-the-art method by 21% for real data. As a result, our technique is able to identify rare cell types, such as cancer cells, when their abundances are low, thus allowing early detection of diseases. Moreover, our proposed method is the first method that is able to identify abnormally expressed genes for a given patient as compared to healthy individuals in a cell-type-specific manner, thus facilitating individualized treatment.

DIFseq outperforms the state-of-the-art methods in clustering accuracy for single-cell RNA-seq data, with an increase in ARI by 21% for a pancreas study
DIFseq is able to not only detect genes that are differentially expressed between cell types but also identify genes that are abnormally expressed in disease samples as compared to normal samples. (a) Expression patterns of genes that are differentially expressed between different pancreatic cell types. (b) Expression patterns of genes that are abnormally expressed in type II diabetes patients as compared to normal individuals for different pancreatic cell types

Uniqueness and Competitive Advantages:

  • High accuracy in detecting rare cell types
  • Sensitive in detecting changes in cellular compositions between patients and healthy individuals
  • First to identify genes that are differentially expressed between patients and healthy individuals in a cell-type-specific manner
  • Scalable to hundreds of thousands of cells
  • Can integrate data collected from different laboratories
Prof. Yingying Wei received the 2019 W. J. Youden Award in Interlaboratory Testing from Prof. Karen Kafadar, the president of American Statistical Association (ASA), at the ASA President's Address and Awards Ceremony during the 2019 Joint Statistical Meeting in Denver.

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