A Transformer for scATAC-scRNA Translation
Name
Jin-rogerjin-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
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2.58 MB
Format
Adobe PDF
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165cc42fc6fc087d5c64aca8b31cfe97
Author(s)
Jin, Roger
Advisor(s)
Kellis, Manolis
Date Issued
September 2022
Publisher
Massachusetts Institute of Technology
Abstract
scATAC-seq gives a comprehensive picture of the chromatin accessibility profile of a cell, covering not only protein-coding regions but also non-coding regulatory regions which are in theory missed by scRNA-seq. However, scATAC-seq data is highdimensional and noisy, aspects which when compounded with data scarcity present challenges for modeling on even seemingly-simple downstream tasks such as cell-type prediction. As such, researchers may benefit from access to a large library of models to evaluate. While we do not demonstrate state of the art results in any capacity, we provide an implementation of a simple representation of sparse tabular data that allows it to be inputted into the popular transformer family of architectures, and use this representation to train a transformer that predicts scRNA-seq given scATAC-seq. Our code is made available here https://github.com/rogershijin/GANOLI.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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