A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data.

RNA
Suhas SrinivasanDmitry Korkin

Abstract

Single-cell RNA sequencing (scRNA-seq) is a recent technology that enables fine-grained discovery of cellular subtypes and specific cell states. Analysis of scRNA-seq data routinely involves machine learning methods, such as feature learning, clustering, and classification, to assist in uncovering novel information from scRNA-seq data. However, current methods are not well suited to deal with the substantial amount of noise that is created by the experiments or the variation that occurs due to differences in the cells of the same type. To address this, we developed a new hybrid approach, deep unsupervised single-cell clustering (DUSC), which integrates feature generation based on a deep learning architecture by using a new technique to estimate the number of latent features, with a model-based clustering algorithm, to find a compact and informative representation of the single-cell transcriptomic data generating robust clusters. We also include a technique to estimate an efficient number of latent features in the deep learning model. Our method outperforms both classical and state-of-the-art feature learning and clustering methods, approaching the accuracy of supervised learning. We applied DUSC to a single-cell transcriptomics...Continue Reading

References

Nov 2, 2006·Genome Biology·Anne E CarpenterDavid M Sabatini
Feb 5, 2008·IEEE Transactions on Neural Networks·M S BartlettT J Sejnowski
Aug 9, 2008·Nature Biotechnology·Chuong B Do, Serafim Batzoglou
Nov 19, 2008·BMC Bioinformatics·Thouis R JonesAnne E Carpenter
Mar 6, 2009·Genome Biology·Ben LangmeadSteven L Salzberg
Jun 15, 2011·International Immunology·Emilie Narni-MancinelliYann M Kerdiles
Jul 31, 2013·Nature Reviews. Genetics·Ehud ShapiroSten Linnarsson
Sep 24, 2013·Nature Methods·Philip BrenneckeMarcus G Heisler
Jun 14, 2014·Science·Anoop P PatelBradley E Bernstein
Sep 28, 2014·Bioinformatics·Simon AndersWolfgang Huber
Nov 25, 2014·Nature Neuroscience·Dmitry UsoskinPatrik Ernfors
Jan 30, 2015·Nature Reviews. Genetics·Oliver StegleJohn C Marioni
May 23, 2015·Molecular Cell·Aleksandra A KolodziejczykSarah A Teichmann
Jun 11, 2015·Proceedings of the National Academy of Sciences of the United States of America·Spyros DarmanisStephen R Quake
Aug 20, 2015·Nature·Dominic GrünAlexander van Oudenaarden
Nov 23, 2015·Molecular Cell·Shany Koren, Mohamed Bentires-Alj
Mar 16, 2016·Scientific Reports·Claire Lifan ChenBahram Jalali
Mar 24, 2016·BMC Bioinformatics·Justina Žurauskienė, Christopher Yau
Mar 26, 2016·Cell·Mubeen GoolamMagdalena Zernicka-Goetz
Jan 20, 2017·Nature·Amos Tanay, Aviv Regev
Aug 9, 2017·Nature Reviews. Immunology·Efthymia Papalexi, Rahul Satija
Aug 11, 2017·Nature Communications·Ruli GaoNicholas Navin
Oct 4, 2017·Nucleic Acids Research·Chieh LinZiv Bar-Joseph
Dec 6, 2017·ELife·Aviv RegevUNKNOWN Human Cell Atlas Meeting Participants
Mar 2, 2018·Nature Protocols·Valentine SvenssonSarah A Teichmann
Jul 18, 2018·Nature Communications·Wuming GongDaniel J Garry
Sep 6, 2018·Nature Communications·Mihriban KaraayvazLeif W Ellisen
Oct 5, 2018·Nature·UNKNOWN Tabula Muris ConsortiumUNKNOWN Principal investigators
Dec 7, 2018·Nature Methods·Romain LopezNir Yosef

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Citations

May 16, 2021·Cell Death Discovery·Lili RenJing Tang
Dec 21, 2021·Briefings in Bioinformatics·Mario FloresYufei Huang

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