Using neural networks for reducing the dimensions of single-cell RNA-Seq data

By Chieh Hua Lin, Siddhartha Jain, Hannah Kim, Ziv Bar-Joseph. 2017

183 citations recorded by the source corpus.

Abstract While only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led to several important studies and findings. However, this technology has also raised several new computational challenges. These include questions about the best methods for clustering scRNA-Seq data, how to identify unique group of cells in such experiments, and how to determine the state or function of specific cells based on their expression profile. To address these issues we develop and test a method based on neural networks (NN) for the analysis and retrieval of single cell RNA-Seq data. We tested various NN architectures, some of which incorporate prior biological knowledge, and used these to obtain a reduced dimension representation of the single cell expression data. We show that the NN method improves upon prior methods in both, the ability to correctly group cells in experiments not used in the training and the ability to correctly infer cell type or state by querying a database of tens of thousands of single cell profiles. Such database queries (which can be performed using our web server) will enable researchers to better characterize cells when analyzing heterogeneous scRNA-Seq samples.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Clustering single-cell RNA-seq data with a model-based deep learning approach (2019, background): “Meanwhile, a study demonstrates that DNNs can reduce the dimensions of scRNA-seq data in a supervised manner 32 .”
Deep Generative Modeling for Single-cell Transcriptomics (2018, methodology): “In [18], neural networks are used as function approximators reducing the dimension of single-cell RNA sequencing data; however, this model is a supervised learning method and inherently relies on some labeling of the cells.”
Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing (2018, methodology): “In [20], neural networks are used as function approximators reducing the dimension of single-cell RNA sequencing data; however, this model is a supervised learning method and inherently relies on some labeling of the cells.”
A deep generative model for gene expression profiles from single-cell RNA sequencing (2017, background): “Recent articles apply neural networks, but without an architecture based on biology [10, 11].”
A Review of Challenges and Opportunities in Machine Learning for Health. (2018, background): “There has also been some initial exploration into learning representations of other data types that simultaneously capture hierarchy and similarity 110-112 .”
A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes (2017, background): “Recent articles apply neural networks, but without an architecture based on biology [10, 11].”