Cited by 137Single-cell and spatial transcriptomicsGene Regulatory Network AnalysisBioinformatics and Genomic Networks

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Machine learning for perturbational single-cell omics

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Ji, Yuge, Lotfollahi, Mohammad, Wolf, F. Alexander, Theis, Fabian J.·2021·Cell Systems

DOI 10.1016/j.cels.2021.05.016

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Abstract

Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.

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Attention Is All You Need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
MizAR 60 for Mizar 50
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Untitled work
(2008) · Journal of Machine Learning Research · Cited by 33,051
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372

Show all 174 references

Research works printed but not linked: 4

Burkhardt, 2019, Quantifying the effect of experimental perturbations in single-cell RNA-sequencing data using graph signal processing, bioRxiv
Cao, 2017, Comprehensive single-cell transcriptional profiling of a multicellular organism, Science, 357, 661, 10.1126/science.aam8940 · DOI · Paper page
Sohn, 2015, Learning structured output representation using deep conditional generative models, Adv. Neural Inf. Process. Syst., 28, 3483
Umarov, 2020, A DeepFake framework for prediction of cell type specific transcriptional states induced by drug treatment, bioRxiv

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Trellis Single-Cell Screening Reveals Stromal Regulation of Patient-Derived Organoid Drug ResponsesPreprint
Zapatero, M. R., Tong, A., Sufi, J., Vlckova, P., Rodriguez, F. C., Nattress, C. B., Qin, X., Hochhauser, D., Krishnaswamy, S., & Tape, C. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.19.512668 · Cited by 7
CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data
Wu, Y., Liu, J., Liu, S., Xiao, Y., Zhang, S., & Li, L. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf126 · Cited by 6
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Piochi, L. F., Gaspar, A. T., Rosário‐Ferreira, N., Preto, A. J., & Moreira, I. S. (2022) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2022.05.006 · Cited by 3
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Klumpe, H. E., Lugagne, J., Khalil, A. S., & Dunlop, M. J. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00203 · Cited by 5
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Authors: Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J.
Affiliations: Helmholtz Munich (DE)
Corresponding author: Theis, Fabian J.
ORCID: Ji, Yuge
Lotfollahi, Mohammad
Wolf, F. Alexander
Theis, Fabian J.
Venue: Cell Systems
Publisher: Elsevier BV
Published: 2021-06
Volume: 12
Issue: 6
Pages: 522-537
ISSN: 2405-4712 (ISSN-L)
DOI: 10.1016/j.cels.2021.05.016
OpenAlex: W3170265427
Cited by: 137
References: 174
Citations by year: 2026: 37 · 2025: 29 · 2024: 28 · 2023: 26 · 2022: 14 · 2021: 3
FWCI: 1.86
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 380 d)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Cell Systems (publishedVersion)
PubMed (publishedVersion)
Site cant be reached (submittedVersion, other-oa)
PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
Funders: BMBF Berlin
Helmholtz Association of German Research Centres (ZT-I-007, ZT-I-PF-5-01)
Silicon Valley Community Foundation
Bundesministerium für Bildung und Forschung (01IS18036B, 01IS18053A)
Chan Zuckerberg Initiative (2019- 002438)
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 Regulatory Network Analysis
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: single-cell omics, deep learning, cell phenotype, multi-omics integration
MeSH terms: Machine Learning, Neural Networks, Computer

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Machine learning for perturbational single-cell omics

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