f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq.
Florian Buettner,Naruemon Pratanwanich,Davis J. McCarthy,Davis J. McCarthy,John C. Marioni,John C. Marioni,Oliver Stegle +6 more
TLDR
In this article, a method based on factor analysis that uses pathway annotations to guide the inference of interpretable factors underpinning the heterogeneity in gene expression in large cell populations is proposed.Abstract:
Single-cell RNA-sequencing (scRNA-seq) allows studying heterogeneity in gene expression in large cell populations. Such heterogeneity can arise due to technical or biological factors, making decomposing sources of variation difficult. We here describe f-scLVM (factorial single-cell latent variable model), a method based on factor analysis that uses pathway annotations to guide the inference of interpretable factors underpinning the heterogeneity. Our model jointly estimates the relevance of individual factors, refines gene set annotations, and infers factors without annotation. In applications to multiple scRNA-seq datasets, we find that f-scLVM robustly decomposes scRNA-seq datasets into interpretable components, thereby facilitating the identification of novel subpopulations.read more
Citations
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SCANPY: large-scale single-cell gene expression data analysis
TL;DR: This work presents Scanpy, a scalable toolkit for analyzing single-cell gene expression data that includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks, and AnnData, a generic class for handling annotated data matrices.
Journal ArticleDOI
Current best practices in single-cell RNA-seq analysis: a tutorial.
Malte D Luecken,Fabian J. Theis +1 more
TL;DR: The steps of a typical single‐cell RNA‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis, are detailed.
Journal ArticleDOI
Eleven grand challenges in single-cell data science
David Lähnemann,David Lähnemann,Johannes Köster,Johannes Köster,Ewa Szczurek,Davis J. McCarthy,Davis J. McCarthy,Stephanie C. Hicks,Mark D. Robinson,Catalina A. Vallejos,Catalina A. Vallejos,Kieran R Campbell,Kieran R Campbell,Niko Beerenwinkel,Niko Beerenwinkel,Ahmed Mahfouz,Ahmed Mahfouz,Luca Pinello,Luca Pinello,Pavel Skums,Alexandros Stamatakis,Alexandros Stamatakis,Camille Stephan Otto Attolini,Samuel Aparicio,Samuel Aparicio,Jasmijn A. Baaijens,Marleen Balvert,Marleen Balvert,Buys de Barbanson,Antonio Cappuccio,Giacomo Corleone,Bas E. Dutilh,Bas E. Dutilh,Maria Florescu,Victor Guryev,Rens Holmer,Katharina Jahn,Katharina Jahn,Thamar Jessurun Lobo,Emma M. Keizer,Indu Khatri,Szymon M. Kielbasa,Jan O. Korbel,Alexey M. Kozlov,Tzu Hao Kuo,Boudewijn P. F. Lelieveldt,Boudewijn P. F. Lelieveldt,Ion I. Mandoiu,John C. Marioni,John C. Marioni,John C. Marioni,Tobias Marschall,Tobias Marschall,Felix Mölder,Amir Niknejad,Lukasz Raczkowski,Marcel J. T. Reinders,Marcel J. T. Reinders,Jeroen de Ridder,Antoine-Emmanuel Saliba,Antonios Somarakis,Oliver Stegle,Oliver Stegle,Fabian J. Theis,Huan Yang,Alexander Zelikovsky,Alexander Zelikovsky,Alice C. McHardy,Benjamin J. Raphael,Sohrab P. Shah,Alexander Schönhuth,Alexander Schönhuth +71 more
TL;DR: This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years in single-cell data science.
Journal ArticleDOI
Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets
Ricard Argelaguet,Britta Velten,Damien Arnol,Sascha Dietrich,Thorsten Zenz,Thorsten Zenz,Thorsten Zenz,John C. Marioni,John C. Marioni,John C. Marioni,Florian Buettner,Wolfgang Huber,Oliver Stegle +12 more
TL;DR: Multi‐Omics Factor Analysis (MOFA) infers a set of (hidden) factors that capture biological and technical sources of variability that disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities.
Journal ArticleDOI
Orchestrating Single-Cell Analysis with Bioconductor
Robert A. Amezquita,Aaron T. L. Lun,Aaron T. L. Lun,Etienne Becht,Vincent J. Carey,Lindsay N. Carpp,Ludwig Geistlinger,Federico Marini,Kevin Rue-Albrecht,Davide Risso,Davide Risso,Charlotte Soneson,Charlotte Soneson,Levi Waldron,Hervé Pagès,Mike L. Smith,Wolfgang Huber,Martin Morgan,Raphael Gottardo,Stephanie C. Hicks +19 more
TL;DR: This Perspective highlights open-source software for single-cell analysis released as part of the Bioconductor project, providing an overview for users and developers.
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