Using neural networks for reducing the dimensions of single-cell RNA-Seq data
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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.
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Lin et al. (2017) predicted cell type and state using a deep neural network whose structure was determined based on the hierarchical organization of transcriptional regulatory factors in the nucleus.
“This idea was recently explored by Lin et al. (2017), who predicted cell type and state using a deep neural network for which the structure was determined based on the hierarchical organization of transcriptional regulatory factors in the nucleus.”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Lin et al., 2017
Direct incorporation of the biological structure of cells and tissues leads to a ready-made working model of how biological inputs, such as genotype, are compressed to determine outcomes.
“In contrast, direct incorporation of the biological structure of cells and tissues, such as explored by Lin et al. (2017) and Ma et al. (2018), leads to a ready-made working model of how biological inputs, such as genotype, are compressed to determine outcomes.”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Lin et al., 2017
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MethodGCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data
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BackgroundMicroalgae with Artificial Intelligence: A Digitalized Perspective on Genetics, Systems and Products
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Review of single-cell RNA-seq data clustering for cell-type identification and characterization
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BackgroundMethodInfluentialWhen causal inference meets deep learning
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Random forest based similarity learning for single cell RNA sequencing data
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MethodSparsely-connected autoencoder (SCA) for single cell RNAseq data mining
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“…that incorporating vast amounts of biological knowledge in the learning procedure lowers the number of model parameters, up to an order of magnitude (Figure 1), while preserving or even improving the model performance, compared to DNN models without biologically inspired architectures [14,28].”
BackgroundLatent representation learning in biology and translational medicine
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“Inferring cellular variation across several axes, including space, phenotype, or time – neural network RNA-seq clustering Lin et al.(49)”
BackgroundMethodDeep learning-based advances and applications for single-cell RNA-sequencing data analysis
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A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases
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“Different works suggested several methods by using either a classical AE [34] or a VAE [6,11,39,40,60–62].”
MethodAlgorithmic advances in machine learning for single-cell expression analysis
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MethodA data-driven clustering recommendation method for single-cell RNA-sequencing data
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InfluentialIntegration of Computational Analysis and Spatial Transcriptomics in Single-Cell Studies
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“Deep learning approaches have also been introduced into single-cell study, and neural network model (NNM) is a powerful mathematical algorithm that is able to capture and represent complex variable relationships [42,43].”
BackgroundRVAgene: generative modeling of gene expression time series data
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“Autoencoders have been developed to perform noise removal/batch correction (Deng et al. 2019, Eraslan et al. 2019, Wang et al. 2019), imputation (Talwar et al. 2018), and visualization & clustering (Lin et al. 2017).”
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Performance Evaluation of ANOVA and RFE Algorithms for Classifying Microarray Dataset Using SVM
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A Hybrid Clustering Algorithm for Identifying Cell Types from Single-Cell RNA-Seq Data
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The progressive application of single-cell RNA sequencing technology in cardiovascular diseases
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- Authors
- Lin, C., Jain, S., Kim, H., & Bar-Joseph, Z.
- Affiliations
- Carnegie Mellon University (US)
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-07-31
- Volume
- 45
- Issue
- 17
- Pages
- e156-e156
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC5737331
- OpenAlex
- W2741564801
- Semantic Scholar
- c4d33e602615608d29bb296591591fe4e51b7bf2
- Cited by
- 197
- References
- 65
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 15 · 2023: 14 · 2022: 21 · 2021: 31 · 2020: 43 · 2019: 37 · 2018: 23 · 2017: 1
- FWCI
- 11.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (1356505)
- National Institutes of Health
- National Heart, Lung, and Blood Institute (U01HL122626)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- single-cell RNA sequencing, dimensionality reduction, artificial neural networks, cell clustering, cell type annotation, heterogeneous cell populations, web server
- MeSH terms
- Gene Expression Regulation, Humans, RNA, Software, Cluster Analysis, Neural Networks, Computer, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Protein Interaction Mapping, Databases, Genetic, Multifactor Dimensionality Reduction, Single-Cell Analysis, Datasets as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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