Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks

Scientific Reports
Farzad AbdolhosseiniHamidreza Chitsaz

Abstract

Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on the whole gene expression profiles (GEPs). The accuracies of simple classification algorithms such as linear discriminators or support vector machines are limited due to the complexity of biological systems. We used deep neural networks to analyze 1040 GEPs from 16 different human tissues and cell types. After comparing different architectures, we identified a specific structure of deep autoencoders that can encode a GEP into a vector of 30 numeric values, which we call the cell identity code (CIC). The original GEP can be reproduced from the CIC with an accuracy comparable to technical replicates of the same experiment. Although we use an unsupervised approach to train the autoencoder, we show different values of the CIC are connected to different biological aspects of the cell, such as different pathways or biological processes. This network can use CIC to reproduce the GEP of the cell types it has never seen duri...Continue Reading

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Citations

Aug 1, 2020·Frontiers in Cell and Developmental Biology·Lisa M Julian, William L Stanford
Dec 22, 2020·Briefings in Bioinformatics·Apurva BadkasThomas Sauter
Jan 21, 2021·International Journal of Molecular Sciences·Monika Richter-LaskowskaAgata Wawrzkiewicz-Jałowiecka
Jan 14, 2021·Biotechnology and Bioprocess Engineering : BBE·Hyunho KimHojung Nam

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Datasets Mentioned

BETA
HG133

Methods Mentioned

BETA
PCA

Software Mentioned

Bioconductor
Infomap
SoftMax
Lua
Forest
Torch Tensor
REP
ToppCluster
Gene Set Enrichment Analysis ( GSEA )
Random

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