Title: Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks,
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 during the training. It also can resist some noise in
the measurement of the GEP. Furthermore, we introduce classifier autoencoder,
an architecture that can accurately identify cell type based on the GEP or the
CIC.