@inproceedings{c253db5b3f324cacbdca79856ba7c336,
title = "Tissue of origin classification from single cell mRNA expression by Artificial Neural Networks",
abstract = "Single cell transcriptomics (SCT) enables high-throughput measurement of mRNA expression concurrently from tens of thousands of single cells. Gene expression profiles in single cells cover only a small fraction of expressed genes and these data are inherently noisy. We developed a method that utilizes artificial neural networks (ANN) for classification of single cells by their tissue of origin. Data sets representing 10 different organs and tissues from C57BL/6 laboratory mice were standardized and used for training and testing ANN models. Each organ was represented by at least two datasets derived from different mice. We achieved 80% accuracy in 10-class classification. After combining data sets from spleen, bone marrow, and lung into one super-class and mammary tissue and muscle into another, we achieved overall cell classification accuracy of 98% across two tissue super-classes and five organs.",
keywords = "classification of single cells, hierarchical classification, single cell gene expression, super-class, supervised machine learning",
author = "Bangrui Zheng and Minjie Lyu and Sen Lin and Vladimir Brusic",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 ; Conference date: 16-12-2020 Through 19-12-2020",
year = "2020",
month = dec,
day = "16",
doi = "10.1109/BIBM49941.2020.9313427",
language = "English",
series = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1346--1350",
editor = "Taesung Park and Young-Rae Cho and Hu, {Xiaohua Tony} and Illhoi Yoo and Woo, {Hyun Goo} and Jianxin Wang and Julio Facelli and Seungyoon Nam and Mingon Kang",
booktitle = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",
address = "United States",
}