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Statistical analysis of single-cell RNA-seq data with multiple samples

  • Fri, January 21, 2022
  • 09:00 - 10:00

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Fri, Jan 21, 2021, 09:00 – 10:00AM (PDT), 12:00 – 1:00PM (EST)

Abstract

As single-cell RNA-seq (scRNA-seq) is increasingly used in biomedical research, scRNA-seq datasets with multiple patient samples become common. In this talk, I will introduce our recent research on analyzing multi-sample scRNA-seq data. I will introduce TreeCorTreat, an open source R package that uses a tree-based correlation screen to help explore and visualize the association between samples’ phenotype and their transcriptomic features at multiple cell type resolutions. I will also introduce Lamian, a statistical framework for differential pseudotime analysis with multiple samples. I will demonstrate these tools using both simulation and real data including an integrative analysis of COVID-19 single-cell RNA-seq data.


Speaker:



Dr. Ji is a Professor, Associate Chair for Education and Graduate Program Director in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He received his Bachelor's and Master’s degree in Engineering from Tsinghua University in 1999 and 2002. He then studied at the Harvard University and received his Master’s and PhD degree in Statistics in 2004 and 2007. Between 2004 and 2007, he was a visiting student at the Stanford University. He joined the Johns Hopkins Department of Biostatistics as an Assistant Professor in 2007 and was later promoted to Associate Professor and Professor. He is also an affiliated faculty at the Johns Hopkins Institute for Data Intensive Engineering and Science, the Center for Computational Biology, and the High Throughput Biology Center. He is an elected Fellow of the American Statistical Association and Co-Editor-in-Chief of the ICSA sponsored journal Statistics in Biosciences. Dr. Ji’s research is focused on developing statistical and computational methods for analyzing big and complex data, particularly high-throughput genomic data. He applies these tools to study gene regulatory programs in development and diseases. His current research topics include single-cell genomics, analytical methods and software tools for gene regulation, machine learning for big data and scalable data integration, immunoinformatics in cancer and infectious disease, and public health data science.


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