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🌟 scGPT-spatial: модель для анализа данных о пространственной организации клеток в тканях.

scGPT-spatial - расширенная версия модели scGPT в помощь ученым-биологам для анализа пространственной транскриптомики. Основная цель scGPT-spatial — интегрировать информацию о пространственной локализации клеток и их транскриптомных профилях с знаниями scGPT для расширения понимания организации тканей и взаимодействия клеток в микроокружении.

scGPT-spatial обучалась с с учётом пространственных координат на наборе данных SpatialHuman30M (30 миллионов клеток и спотов из 4 протоколов секвенирования: Visium, Visium HD, MERFISH и Xenium) и использует архитектуру MoE.

В тестах scGPT-spatial показала отличные результаты в задачах кластеризации клеточных типов, деконволюции спотов и импутации генной экспрессии. В экспериментах на интеграцию данных из нескольких слайдов и модальностей модель обошла методы PCA и Seurat v4, достигнув показателя AvgBIO 0.86.

В задаче деконволюции клеточных типов scGPT-spatial превзошла Tangram и Cell2location, со средним Macro F1 в 0.58, а медианный коэффициент корреляции Пирсона в импутации генной экспрессии составил значение 0.6.

Веса модели опубликованы в открытом доступе, а в репозитории проекта на Github - подробная инструкция по настройке окружения для scGPT и ipynb демо-ноутбук инференса.


📌Лицензирование

🟢Код : MIT License.
🟠Модель: CC-BY-4.0 License.


🟡Модель
🟡Техотчет
🖥GitHub

@ai_machinelearning_big_data

#AI #ML #MedML #ScGPT

Machine learning Interview
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🌟 scGPT-spatial: модель для анализа данных о пространственной организации клеток в тканях.

scGPT-spatial - расширенная версия модели scGPT в помощь ученым-биологам для анализа пространственной транскриптомики. Основная цель scGPT-spatial — интегрировать информацию о пространственной локализации клеток и их транскриптомных профилях с знаниями scGPT для расширения понимания организации тканей и взаимодействия клеток в микроокружении.

scGPT-spatial обучалась с с учётом пространственных координат на наборе данных SpatialHuman30M (30 миллионов клеток и спотов из 4 протоколов секвенирования: Visium, Visium HD, MERFISH и Xenium) и использует архитектуру MoE.

В тестах scGPT-spatial показала отличные результаты в задачах кластеризации клеточных типов, деконволюции спотов и импутации генной экспрессии. В экспериментах на интеграцию данных из нескольких слайдов и модальностей модель обошла методы PCA и Seurat v4, достигнув показателя AvgBIO 0.86.

В задаче деконволюции клеточных типов scGPT-spatial превзошла Tangram и Cell2location, со средним Macro F1 в 0.58, а медианный коэффициент корреляции Пирсона в импутации генной экспрессии составил значение 0.6.

Веса модели опубликованы в открытом доступе, а в репозитории проекта на Github - подробная инструкция по настройке окружения для scGPT и ipynb демо-ноутбук инференса.


📌Лицензирование

🟢Код : MIT License.
🟠Модель: CC-BY-4.0 License.


🟡Модель
🟡Техотчет
🖥GitHub

@ai_machinelearning_big_data

#AI #ML #MedML #ScGPT
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🌟 scGPT-spatial: модель для анализа данных о пространственной организации клеток в тканях.

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