Best practices for single-cell analysis across modalities

Nature Reviews Genetics, Journal Year: 2023, Volume and Issue: 24(8), P. 550 - 572

Published: March 31, 2023

Language: Английский

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Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain

Nature Neuroscience, Journal Year: 2022, Volume and Issue: 25(3), P. 306 - 316

Published: March 1, 2022

Language: Английский

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The specious art of single-cell genomics

PLoS Computational Biology, Journal Year: 2023, Volume and Issue: 19(8), P. e1011288 - e1011288

Published: Aug. 17, 2023

Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with to 2 or 3 dimensions produce "all-in-one" visuals of the that are amenable human eye, these subsequently used qualitative quantitative exploratory analysis. However, there little theoretical support this practice, we show extreme dimension reduction, from hundreds thousands 2, inevitably induces significant distortion high-dimensional datasets. We therefore examine practical implications low-dimensional embedding find extensive distortions inconsistent practices make such embeddings counter-productive exploratory, biological lieu this, discuss alternative approaches conducting targeted feature exploration enable hypothesis-driven discovery.

Language: Английский

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Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Aug. 29, 2022

Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only minimal loss information. PCA applications, implemented in well-cited packages like EIGENSOFT and PLINK, are extensively used as foremost analyses population genetics related fields (e.g., animal plant or medical genetics). outcomes to shape study design, identify, characterize individuals populations, draw historical ethnobiological conclusions origins, evolution, dispersion, relatedness. replicability crisis science has prompted us evaluate whether results reliable, robust, replicable. We analyzed twelve common test cases using an intuitive color-based model alongside human data. demonstrate artifacts easily manipulated generate desired outcomes. adjustment also yielded unfavorable association studies. may not replicable field assumes. Our findings raise concerns about validity reported literature place disproportionate reliance upon insights derived from them. conclude have biasing role genetic investigations 32,000-216,000 studies should reevaluated. An alternative mixed-admixture discussed.

Language: Английский

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The Specious Art of Single-Cell Genomics

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2021, Volume and Issue: unknown

Published: Aug. 26, 2021

Abstract Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with to two or three dimensions produce ‘all-in-one’ visuals of the that are amenable human eye, these subsequently used qualitative quantitative exploratory analysis. However, there little theoretical support this practice, we show extreme dimension reduction, from hundreds thousands two, inevitably induces significant distortion high-dimensional datasets. We therefore examine practical implications low-dimensional embedding data, find extensive distortions inconsistent practices make such embeddings counter-productive exploratory, biological lieu this, discuss alternative approaches conducting targeted feature exploration, enable hypothesis-driven discovery.

Language: Английский

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Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments

Nature Methods, Journal Year: 2023, Volume and Issue: 20(3), P. 375 - 386

Published: March 1, 2023

Language: Английский

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Single-cell transcriptomic and spatial landscapes of the developing human pancreas

Cell Metabolism, Journal Year: 2022, Volume and Issue: 35(1), P. 184 - 199.e5

Published: Dec. 12, 2022

Current differentiation protocols have not been successful in reproducibly generating fully functional human beta cells vitro, partly due to incomplete understanding of pancreas development. Here, we present detailed transcriptomic analysis the various cell types developing pancreas, including their spatial gene patterns. We integrated single-cell RNA sequencing with transcriptomics at multiple developmental time points and revealed distinct temporal-spatial cascades. Cell trajectory inference identified endocrine progenitor populations branch-specific genes as progenitors differentiate toward alpha or cells. Spatial trajectories indicated that Schwann are spatially co-located progenitors, cell-cell connectivity predicted they may interact via L1CAM-EPHB2 signaling. Our approach enabled us identify heterogeneity lineage dynamics within mesenchyme, showing it contributed exocrine acinar state. Finally, generated an interactive web resource for investigating development research community.

Language: Английский

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Zebrahub – Multimodal Zebrafish Developmental Atlas Reveals the State-Transition Dynamics of Late-Vertebrate Pluripotent Axial Progenitors

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: March 7, 2023

ABSTRACT Elucidating the developmental processes of organisms requires a comprehensive understanding cellular lineages in spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, dynamic atlas zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This offers high-resolution in-depth insights into development, achieved through individual embryos across ten stages, complemented trajectory reconstructions. Zebrahub also incorporates an interactive tool to navigate complex flows derived from microscopy data, enabling silico fate mapping experiments. To demonstrate versatility our multi-modal resource, utilize provide fresh pluripotency Neuro-Mesodermal Progenitors (NMPs). Our publicly accessible web-based platform, is foundational resource for studying at both transcriptional spatiotemporal levels, providing researchers integrated approach exploring analyzing complexities during embryogenesis.

Language: Английский

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Mapping cells through time and space with moscot

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: May 11, 2023

Abstract Single-cell genomics technologies enable multimodal profiling of millions cells across temporal and spatial dimensions. Experimental limitations prevent the measurement all-encompassing cellular states in their native dynamics or tissue niche. Optimal transport theory has emerged as a powerful tool to overcome such constraints, enabling recovery original context. However, most algorithmic implementations currently available have not kept up pace with increasing dataset complexity, so that current methods are unable incorporate information scale single-cell atlases. Here, we introduce multi-omics optimal (moscot), general scalable framework for applications genomics, supporting multimodality all applications. We demonstrate moscot’s ability efficiently reconstruct developmental trajectories 1.7 million mouse embryos 20 time points identify driver genes first heart field formation. The moscot formulation can be used dimensions well: To this, enrich transcriptomics datasets by mapping from profiles liver sample, align multiple coronal sections brain. then present moscot.spatiotemporal, new approach leverages gene expression uncover spatiotemporal embryogenesis. Finally, disentangle lineage relationships novel murine, time-resolved pancreas development using paired measurements chromatin accessibility, finding evidence shared ancestry between delta epsilon cells. Moscot is an easy-to-use, open-source python package extensive documentation at https://moscot-tools.org .

Language: Английский

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The benefits and pitfalls of machine learning for biomarker discovery

Cell and Tissue Research, Journal Year: 2023, Volume and Issue: 394(1), P. 17 - 31

Published: July 27, 2023

Prospects for the discovery of robust and reproducible biomarkers have improved considerably with development sensitive omics platforms that can enable measurement biological molecules at an unprecedented scale. With technical barriers to success lowering, challenge is now moving into analytical domain. Genome-wide presents a problem scale multiple testing as standard statistical methods struggle distinguish signal from noise in increasingly complex systems. Machine learning AI are good finding answers large datasets, but they tendency overfit solutions. It may be possible find local answer or mechanism specific patient sample small group samples, this not generalise wider populations due high likelihood false discovery. The rise explainable offers improve opportunity true by providing explanations predictions explored mechanistically before proceeding costly time-consuming validation studies. This review aims introduce some basic concepts machine biomarker focus on post hoc explanation predictions. To illustrate this, we consider how has already been used successfully, explore case study applies rheumatoid arthritis, demonstrating accessibility tools learning. We use discuss potential challenges solutions critically interrogate disease response mechanisms.

Language: Английский

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