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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Wound healing, fibroblast heterogeneity, and fibrosis

Cell stem cell, Journal Year: 2022, Volume and Issue: 29(8), P. 1161 - 1180

Published: Aug. 1, 2022

Language: Английский

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Macrophages and microglia in glioblastoma: heterogeneity, plasticity, and therapy

Journal of Clinical Investigation, Journal Year: 2023, Volume and Issue: 133(1)

Published: Jan. 2, 2023

Glioblastoma (GBM) is the most aggressive tumor in central nervous system and contains a highly immunosuppressive microenvironment (TME). Tumor-associated macrophages microglia (TAMs) are dominant population of immune cells GBM TME that contribute to hallmarks, including immunosuppression. The understanding TAMs has been limited by lack powerful tools characterize them. However, recent progress on single-cell technologies offers an opportunity precisely at level identify new TAM subpopulations with specific tumor-modulatory functions GBM. In this Review, we discuss heterogeneity plasticity summarize current TAM-targeted therapeutic potential We anticipate use followed functional studies will accelerate development novel effective therapeutics for patients.

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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Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease

Nature Reviews Neurology, Journal Year: 2023, Volume and Issue: 19(6), P. 346 - 362

Published: May 17, 2023

Language: Английский

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Statistical and machine learning methods for spatially resolved transcriptomics data analysis

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: March 25, 2022

Abstract The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and locations. As the capacity efficiency experimental technologies continue to improve, there is an emerging need for development analytical approaches. Furthermore, with continuous evolution sequencing protocols, underlying assumptions current methods be re-evaluated adjusted harness increasing data complexity. To motivate aid future model development, we herein review statistical machine learning transcriptomics, summarize useful resources, highlight challenges opportunities ahead.

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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Comparison of transformations for single-cell RNA-seq data

Nature Methods, Journal Year: 2023, Volume and Issue: 20(5), P. 665 - 672

Published: April 10, 2023

Abstract The count table, a numeric matrix of genes × cells, is the basic input data structure in analysis single-cell RNA-sequencing data. A common preprocessing step to adjust counts for variable sampling efficiency and transform them so that variance similar across dynamic range. These steps are intended make subsequent application generic statistical methods more palatable. Here, we describe four transformation approaches based on delta method, model residuals, inferred latent expression state factor analysis. We compare their strengths weaknesses find latter three have appealing theoretical properties; however, benchmarks using simulated real-world data, it turns out rather simple approach, namely, logarithm with pseudo-count followed by principal-component analysis, performs as well or better than sophisticated alternatives. This result highlights limitations current assessed bottom-line performance benchmarks.

Language: Английский

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UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Nov. 3, 2022

Abstract The recent breakthrough of single-cell RNA velocity methods brings attractive promises to reveal directed trajectory on cell differentiation, states transition and response perturbations. However, the existing are often found return erroneous results, partly due model violation or lack temporal regularization. Here, we present UniTVelo, a statistical framework that models dynamics spliced unspliced RNAs via flexible transcription activities. Uniquely, it also supports inference unified latent time across transcriptome. With ten datasets, demonstrate UniTVelo returns expected in different biological systems, including hematopoietic differentiation those even with weak kinetics complex branches.

Language: Английский

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A relay velocity model infers cell-dependent RNA velocity

Nature Biotechnology, Journal Year: 2023, Volume and Issue: 42(1), P. 99 - 108

Published: April 3, 2023

Abstract RNA velocity provides an approach for inferring cellular state transitions from single-cell sequencing (scRNA-seq) data. Conventional models infer universal kinetics all cells in scRNA-seq experiment, resulting unpredictable performance experiments with multi-stage and/or multi-lineage transition of cell states where the assumption same kinetic rates no longer holds. Here we present cellDancer, a scalable deep neural network that locally infers each its neighbors and then relays series local velocities to provide resolution inference kinetics. In simulation benchmark, cellDancer shows robust multiple regimes, high dropout ratio datasets sparse datasets. We show overcomes limitations existing modeling erythroid maturation hippocampus development. Moreover, cell-specific predictions transcription, splicing degradation rates, which identify as potential indicators fate mouse pancreas.

Language: Английский

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