Cellpose: a generalist algorithm for cellular segmentation

Nature Methods, Год журнала: 2020, Номер 18(1), С. 100 - 106

Опубликована: Дек. 14, 2020

Язык: Английский

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CellProfiler 4: improvements in speed, utility and usability

BMC Bioinformatics, Год журнала: 2021, Номер 22(1)

Опубликована: Сен. 10, 2021

Abstract Background Imaging data contains a substantial amount of information which can be difficult to evaluate by eye. With the expansion high throughput microscopy methodologies producing increasingly large datasets, automated and objective analysis resulting images is essential effectively extract biological from this data. CellProfiler free, open source image program enables researchers generate modular pipelines with process into interpretable measurements. Results Herein we describe 4, new version software expanded functionality. Based on user feedback, have made several interface refinements improve usability software. We introduced modules expand capabilities also evaluated performance targeted optimizations reduce time cost associated running common large-scale pipelines. Conclusions 4 provides significantly improved in complex workflows compared previous versions. This release will ensure that continued access CellProfiler’s powerful computational tools coming years.

Язык: Английский

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Deep learning for cellular image analysis

Nature Methods, Год журнала: 2019, Номер 16(12), С. 1233 - 1246

Опубликована: Май 27, 2019

Язык: Английский

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A single-cell transcriptomic atlas characterizes ageing tissues in the mouse

Nature, Год журнала: 2020, Номер 583(7817), С. 590 - 595

Опубликована: Июль 15, 2020

Язык: Английский

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TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines

Nature Methods, Год журнала: 2022, Номер 19(7), С. 829 - 832

Опубликована: Июнь 2, 2022

Язык: Английский

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Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl

Nature Methods, Год журнала: 2019, Номер 16(12), С. 1247 - 1253

Опубликована: Окт. 21, 2019

Abstract Segmenting the nuclei of cells in microscopy images is often first step quantitative analysis imaging data for biological and biomedical applications. Many bioimage tools can segment but need to be selected configured every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide make attempt build a segmentation method that could applied any two-dimensional light image stained across experiments, with no human interaction. Top participants challenge succeeded this task, developing deep-learning-based models identified cell many types experimental conditions without manually adjust parameters. This represents an important toward configuration-free software tools.

Язык: Английский

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Squidpy: a scalable framework for spatial omics analysis

Nature Methods, Год журнала: 2022, Номер 19(2), С. 171 - 178

Опубликована: Янв. 31, 2022

Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools required to store, integrate visualize large diversity spatial data. Here, we present Squidpy, a Python framework that brings together from image analysis enable scalable description molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure numerous methods allow efficiently manipulate interactively is extensible can be interfaced with variety already existing libraries for

Язык: Английский

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Plasticity of ether lipids promotes ferroptosis susceptibility and evasion

Nature, Год журнала: 2020, Номер 585(7826), С. 603 - 608

Опубликована: Сен. 16, 2020

Язык: Английский

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Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Nature Biotechnology, Год журнала: 2021, Номер 40(4), С. 555 - 565

Опубликована: Ноя. 18, 2021

Язык: Английский

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Best practices for single-cell analysis across modalities

Nature Reviews Genetics, Год журнала: 2023, Номер 24(8), С. 550 - 572

Опубликована: Март 31, 2023

Язык: Английский

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