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Classification of Heterogeneous Datasets of Single Cell RNA Sequencing Experiments using Deep Learning

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abstract
With the increasing adoption of single-cell RNA sequencing for profiling gene expression at a high resolution, there is a need for accurate and efficient methods for the prediction of cell types. To date, unsupervised and supervised machine learning have been used to identify cell types in the high-dimensional, zero-inflated experimental data sets. This research focused on performing a comprehensive and reproducible evaluation of the performance of several supervised deep learning networks for cell-type prediction. Overall, 19 benchmark data sets, with diverse characteristics, have been collected, preprocessed and annotated. Among them, one benchmark was constructed by integrating heterogeneous experimental data. Nine deep learning networks were designed, implemented and validated using 19 benchmarks. Results demonstrate that the feed forward neural network architecture achieved the best performance in cross-validation and hold-out validation experiments. Feed forward neural network obtained an average accuracy of 88% for multi-class and average auc score of 0.884 for binary-class benchmarks. Further insights were derived about the impact of training size dependency and sensitivity to changes in batch sizes for the proposed feed forward neural network.
subject
Conv1D
Deep Learning
FFNN
Machine Learning
scRNA
XGBoost
contributor
Bhandari, Sapan (author)
Khuri, Natalia (committee chair)
Thomas, Stan (committee member)
Pauca, Victor Paul (committee member)
date
2021-06-03T08:36:07Z (accessioned)
2021 (issued)
degree
Computer Science (discipline)
2022-06-02 (liftdate)
embargo
2022-06-02 (terms)
identifier
http://hdl.handle.net/10339/98806 (uri)
language
en (iso)
publisher
Wake Forest University
title
Classification of Heterogeneous Datasets of Single Cell RNA Sequencing Experiments using Deep Learning
type
Thesis

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