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Activity Number: 593 - Statistical Challenges and New Developments in Genomics
Type: Invited
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #300478 Presentation
Title: Transfer Learning in Single Cell Transcriptomics
Author(s): Nancy Zhang and Divyansh Agarwal and Zilu Zhou and Mo Huang and Gang Hu and Chengzhong Ye and Jingshu Wang*
Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and Nankai University and Tsinghua University and The University of Chicago
Keywords: single cell; genomics; deep learning; transfer learning; imputation; RNA sequencing

Cells are the basic biological units of multicellular organisms. The development of single-cell RNA sequencing (scRNA-seq) technologies has enabled us to study the diversity of cell types in tissue and to elucidate the roles of individual cell types in disease. Yet, scRNA-seq data are noisy and sparse, with only a small proportion of the transcripts that are present in each cell represented in the final data matrix. We propose a transfer learning framework to borrow information across related single cell data sets for de-noising and expression recovery. Our goal is to leverage the expanding resources of publicly available scRNA-seq data, for example, the Human Cell Atlas which aims to be a comprehensive map of cell types in the human body. Our method is based on a Bayesian hierarchical model coupled to a deep autoencoder, the latter trained to extract transferable gene expression features across studies coming from different labs, generated by different technologies, and/or obtained from different species. Through this framework, we explore the limits of transfer learning: How much can be learned across cell types, tissues, and species? How to avoid introducing bias?

Authors who are presenting talks have a * after their name.

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