Skip to main content
SpringerLink
Log in

Functional-Feature-Based Data Reduction Using Sparsely Connected Autoencoders

  • Protocol
  • First Online:
Single Cell Transcriptomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2584))

Abstract

Single-cell RNA sequencing (scRNA-seq) allows for the creation of large collections of individual cells transcriptome. Unsupervised clustering is an essential element for the analysis of these data, and it represents the initial step for the identification of different cell types to investigate the cell subpopulation structure of a biological sample. However, it is possible that the clustering aggregation features do not perfectly match the underlying biology since scRNA-seq data are characterized by high noise. In this chapter, we describe a functional feature-driven data reduction approach, which could provide a better link among cell clusters and their underlying cell biology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, Satija R, Smibert P (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 14(9):865–868. https://doi.org/10.1038/nmeth.4380

    Article  CAS  Google Scholar 

  2. Han H, Cho JW, Lee S, Yun A, Kim H, Bae D, Yang S, Kim CY, Lee M, Kim E, Lee S, Kang B, Jeong D, Kim Y, Jeon HN, Jung H, Nam S, Chung M, Kim JH, Lee I (2018) TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 46(D1):D380–D386. https://doi.org/10.1093/nar/gkx1013

    Article  CAS  Google Scholar 

  3. Huang HY, Lin YC, Li J, Huang KY, Shrestha S, Hong HC, Tang Y, Chen YG, Jin CN, Yu Y, Xu JT, Li YM, Cai XX, Zhou ZY, Chen XH, Pei YY, Hu L, Su JJ, Cui SD, Wang F, Xie YY, Ding SY, Luo MF, Chou CH, Chang NW, Chen KW, Cheng YH, Wan XH, Hsu WL, Lee TY, Wei FX, Huang HD (2020) miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res 48(D1):D148–D154. https://doi.org/10.1093/nar/gkz896

    Article  CAS  Google Scholar 

  4. Hu R, Xu H, Jia P, Zhao Z (2021) KinaseMD: kinase mutations and drug response database. Nucleic Acids Res 49(D1):D552–D561. https://doi.org/10.1093/nar/gkaa945

    Article  CAS  Google Scholar 

  5. Alessandri L, Cordero F, Beccuti M, Licheri N, Arigoni M, Olivero M, Di Renzo MF, Sapino A, Calogero R (2021) Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining. NPJ Syst Biol Appl 7(1):1. https://doi.org/10.1038/s41540-020-00162-6

    Article  CAS  Google Scholar 

  6. Alessandri L, Ratto ML, Contaldo SG, Beccuti M, Cordero F, Arigoni M, Calogero RA (2021) Sparsely connected autoencoders: a multi-purpose tool for single cell omics analysis. Int J Mol Sci 22(23):12755. https://doi.org/10.3390/ijms222312755

    Article  CAS  Google Scholar 

  7. Alessandri L, Cordero F, Beccuti M, Arigoni M, Olivero M, Romano G, Rabellino S, Licheri N, De Libero G, Pace L, Calogero RA (2019) rCASC: reproducible classification analysis of single-cell sequencing data. Gigascience 8(9):giz105. https://doi.org/10.1093/gigascience/giz105

    Article  CAS  Google Scholar 

  8. James KR, Gomes T, Elmentaite R, Kumar N, Gulliver EL, King HW, Stares MD, Bareham BR, Ferdinand JR, Petrova VN, Polanski K, Forster SC, Jarvis LB, Suchanek O, Howlett S, James LK, Jones JL, Meyer KB, Clatworthy MR, Saeb-Parsy K, Lawley TD, Teichmann SA (2020) Distinct microbial and immune niches of the human colon. Nat Immunol 21(3):343–353. https://doi.org/10.1038/s41590-020-0602-z

    Article  CAS  Google Scholar 

  9. Huang M, Wang J, Torre E, Dueck H, Shaffer S, Bonasio R, Murray JI, Raj A, Li M, Zhang NR (2018) SAVER: gene expression recovery for single-cell RNA sequencing. Nat Methods 15(7):539–542. https://doi.org/10.1038/s41592-018-0033-z

    Article  CAS  Google Scholar 

  10. Tian L, Dong X, Freytag S, Le Cao KA, Su S, JalalAbadi A, Amann-Zalcenstein D, Weber TS, Seidi A, Jabbari JS, Naik SH, Ritchie ME (2019) Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nat Methods 16(6):479–487. https://doi.org/10.1038/s41592-019-0425-8

    Article  CAS  Google Scholar 

  11. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36(5):411–420. https://doi.org/10.1038/nbt.4096

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Molecular Biotechnology Center, University of Torino, Turin, Italy

    Luca Alessandri

  2. Molecular Biotechnology Center, University of Torino, Torino, Italy

    Raffaele A. Calogero

Authors
  1. Luca Alessandri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raffaele A. Calogero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luca Alessandri .

Editor information

Editors and Affiliations

  1. Molecular Biotechnology Center, University of Torino, Torino, Italy

    Raffaele A. Calogero

  2. European Molecular Biology Laboratory, Heidelberg, Germany

    Vladimir Benes

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Alessandri, L., Calogero, R.A. (2023). Functional-Feature-Based Data Reduction Using Sparsely Connected Autoencoders. In: Calogero, R.A., Benes, V. (eds) Single Cell Transcriptomics. Methods in Molecular Biology, vol 2584. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2756-3_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2756-3_11

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2755-6

  • Online ISBN: 978-1-0716-2756-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation