Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

CHOIR is now online Nature Genetics! CHOIR is a new clustering method for single-cell data that evaluates whether clusters represent statistically distinct cell populations. CHOIR scales to millions of cells and works with single-/multi-omic data of any type!

Cathrine Sant

1,158 subscribers

55,296 просмотров • 1 год назад •via X (Twitter)

Образование Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 6

Фото профиля Cathrine Sant (Petersen)

Cathrine Sant (Petersen)1 год назад

CHOIR is available as a user-friendly R package, compatible with Seurat, SingleCellExperiment, and ArchR input objects. We’ve compiled detailed documentation & tutorials on our website:

Фото профиля Cathrine Sant (Petersen)

Cathrine Sant (Petersen)1 год назад

We received great input during the review process, which led us to improve the tool in multiple ways. Single-cell datasets are getting larger by the day, and CHOIR is now compatible with atlas-scale data—as shown here on >3M cells from a human brain atlas snRNA-seq dataset!

Фото профиля Cathrine Sant (Petersen)

Cathrine Sant (Petersen)1 год назад

We’ve also made CHOIR compatible with count splitting ( wherein two independent matrices are used avoid the problem of “double dipping” wherein the same data is used to both generate and evaluate clusters.

Фото профиля Cathrine Sant (Petersen)

Cathrine Sant (Petersen)1 год назад

So thankful for the support of my PhD advisors Lennart Mucke & Ryan Corces @doctorcorces, as well as all of the people who have used CHOIR and provided input! Check out our GitHub page for the latest updates & to report issues—feedback is always welcome!

Фото профиля Lucid Scientific, Inc.

Lucid Scientific, Inc.1 год назад

Expand the possibilities of your metabolic research. Resipher tracks real-time cellular oxygen consumption in standard 96-well plates, delivering continuous real-time data directly from your incubator. Request a free virtual demo or quote today >>

Фото профиля Alessandro Crimi 🧠🧬🔬🩺( @alecrimi.bsky.social )

Alessandro Crimi 🧠🧬🔬🩺( @alecrimi.bsky.social )1 год назад

@NatureGenet impressive

Похожие видео

New preprint! So excited to present CHOIR, a new clustering method for single-cell data that evaluates whether clusters represent statistically distinct cell populations. CHOIR works with both single- and multi-omic data of any type. Check it out!

New preprint! So excited to present CHOIR, a new clustering method for single-cell data that evaluates whether clusters represent statistically distinct cell populations. CHOIR works with both single- and multi-omic data of any type. Check it out!

Cathrine Sant

74,124 просмотров • 2 лет назад

🔥😀Super-super excited to report the publication of "Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression" in Nature Genetics by Amin Abedini and Jon Levinsohn #kidney #diabetes #spatial transcriptomics

🔥😀Super-super excited to report the publication of "Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression" in Nature Genetics by Amin Abedini and Jon Levinsohn #kidney #diabetes #spatial transcriptomics

Katalin Susztak

16,286 просмотров • 2 лет назад

I'm creating 3D tours of open single cell RNA-seq data in UMAP space. Here, using data from 50k brain cells by the allen institute . ( The UMAP position and class of every cell is read into Blender, realized as a stylized, class-specific model I sculpted

I'm creating 3D tours of open single cell RNA-seq data in UMAP space. Here, using data from 50k brain cells by the allen institute . ( The UMAP position and class of every cell is read into Blender, realized as a stylized, class-specific model I sculpted

Brad Krajina

48,144 просмотров • 3 лет назад

A few talks at the intersection of single cell biology x agent engineering: - Understanding library prep + sequencing is important - Repurposing data infrastructure for agent consumption - How to benchmark frontier agents on single cell analysis

A few talks at the intersection of single cell biology x agent engineering: - Understanding library prep + sequencing is important - Repurposing data infrastructure for agent consumption - How to benchmark frontier agents on single cell analysis

Kenny Workman

19,047 просмотров • 2 месяцев назад

1/ The biggest blocker to effective AI-in-bio is lack of quality data, but Vevo Therapeutics is open-sourcing their gigantic #Tahoe100M single cell dataset (on Arc Institute's new Virtual Cell Atlas)! I talk w/Vevo's founders and Arc CEO/CTO about "Virtual Cell Models" and significance

1/ The biggest blocker to effective AI-in-bio is lack of quality data, but Vevo Therapeutics is open-sourcing their gigantic #Tahoe100M single cell dataset (on Arc Institute's new Virtual Cell Atlas)! I talk w/Vevo's founders and Arc CEO/CTO about "Virtual Cell Models" and significance

sarah guo

44,512 просмотров • 1 год назад

Live-cell chromatin imaging is no longer a question of HOW. It is a question of WHY: why dynamics is crucial for cells. Yanyu Zhu (@yanyu_zhu), a brilliant postdoc, leads our new Nature Reviews Genetics review covering a decade of this field to answer the question

Live-cell chromatin imaging is no longer a question of HOW. It is a question of WHY: why dynamics is crucial for cells. Yanyu Zhu (@yanyu_zhu), a brilliant postdoc, leads our new Nature Reviews Genetics review covering a decade of this field to answer the question

Stanley Qi

18,891 просмотров • 4 месяцев назад

We are thrilled to share that our first paper from my new lab, Spateo ( for spatiotemporal modeling of molecular holograms, is now online in Cell: Spateo is a comprehensive analytical framework for 3D whole-embryo spatiotemporal modeling. Its advanced features include: • 3D alignment and reconstruction at the whole-mouse-embryo scale (see the animation). • 3D spatial domain digitization and cell-cell communication analysis to understand spatial gene expression gradients and both inter- and intracellular communication. • 3D morphometric and volumetric analyses along with 3D morphogenesis vector field modeling to quantify dynamics such as surface area, volume, and cell density across organs, and to dissect the interplay between morphogenesis factors and cell migration. • A “Google Earth”-like browser, Spateo-viewer ( and for interactive and intuitive exploration of 3D spatial data. • Additional features, such as RNA signal-based single-cell segmentation. We are also honored that Nature “News and Views” has highlighted this work as well: This is really an amazing outcome after two years' heroic revision process that rewrite the entire paper using a new data ( for whole mouse embryos.

We are thrilled to share that our first paper from my new lab, Spateo ( for spatiotemporal modeling of molecular holograms, is now online in Cell: Spateo is a comprehensive analytical framework for 3D whole-embryo spatiotemporal modeling. Its advanced features include: • 3D alignment and reconstruction at the whole-mouse-embryo scale (see the animation). • 3D spatial domain digitization and cell-cell communication analysis to understand spatial gene expression gradients and both inter- and intracellular communication. • 3D morphometric and volumetric analyses along with 3D morphogenesis vector field modeling to quantify dynamics such as surface area, volume, and cell density across organs, and to dissect the interplay between morphogenesis factors and cell migration. • A “Google Earth”-like browser, Spateo-viewer ( and for interactive and intuitive exploration of 3D spatial data. • Additional features, such as RNA signal-based single-cell segmentation. We are also honored that Nature “News and Views” has highlighted this work as well: This is really an amazing outcome after two years' heroic revision process that rewrite the entire paper using a new data ( for whole mouse embryos.

evo-devo

58,452 просмотров • 1 год назад

How does an embryo reliably "compute" its form - "cell by cell" - using only local interactions and mechanics, yet produce a precise global body plan? I’m excited to share our Nature Methods paper "MultiCell: geometric learning in multicellular development", presenting #AIxBiology research led by Haiqian Yang and the result of a great collaboration with Ming Guo, George Roy, Tomer Stern, Anh Nguyen and Dapeng Bi. A long-standing challenge in developmental biology is to predict how thousands of cells collectively self-organize as tissues fold, divide, and rearrange. In MultiCell, we represent a developing embryo as a dual graph that unifies two complementary views of tissue mechanics with single-cell resolution: cells as moving points (granular) and cells as a connected foam (junction network). This lets the model learn dynamics from both geometry and cell–cell connectivity. On whole-embryo 4D light-sheet movies of Drosophila gastrulation (~5,000 cells), our model predicts key cell behaviors and the timing of events, including junction loss, rearrangements, and divisions with high accuracy, at single-cell resolution. Beyond prediction, the same representation supports robust time alignment across embryos and offers interpretable activation maps that highlight the morphogenetic "drivers" of development. The broader goal is a foundation for cell-by-cell forecasting in more complex tissues, and eventually for detecting subtle dynamical signatures of disease. Kudos to the team for this inspiring collaboration with brilliant researchers to push the boundary of AI for biology! Citation: Yang, H., Roy, G., Nguyen, A.Q., Buehler, M.J., et al. MultiCell: geometric learning in multicellular development. Nature Methods (2025), DOI: 10.1038/s41592-025-02983-x Code/data links are in the manuscript.

How does an embryo reliably "compute" its form - "cell by cell" - using only local interactions and mechanics, yet produce a precise global body plan? I’m excited to share our Nature Methods paper "MultiCell: geometric learning in multicellular development", presenting #AIxBiology research led by Haiqian Yang and the result of a great collaboration with Ming Guo, George Roy, Tomer Stern, Anh Nguyen and Dapeng Bi. A long-standing challenge in developmental biology is to predict how thousands of cells collectively self-organize as tissues fold, divide, and rearrange. In MultiCell, we represent a developing embryo as a dual graph that unifies two complementary views of tissue mechanics with single-cell resolution: cells as moving points (granular) and cells as a connected foam (junction network). This lets the model learn dynamics from both geometry and cell–cell connectivity. On whole-embryo 4D light-sheet movies of Drosophila gastrulation (~5,000 cells), our model predicts key cell behaviors and the timing of events, including junction loss, rearrangements, and divisions with high accuracy, at single-cell resolution. Beyond prediction, the same representation supports robust time alignment across embryos and offers interpretable activation maps that highlight the morphogenetic "drivers" of development. The broader goal is a foundation for cell-by-cell forecasting in more complex tissues, and eventually for detecting subtle dynamical signatures of disease. Kudos to the team for this inspiring collaboration with brilliant researchers to push the boundary of AI for biology! Citation: Yang, H., Roy, G., Nguyen, A.Q., Buehler, M.J., et al. MultiCell: geometric learning in multicellular development. Nature Methods (2025), DOI: 10.1038/s41592-025-02983-x Code/data links are in the manuscript.

Markus J. Buehler

388,252 просмотров • 8 месяцев назад

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource

Bo Wang

199,747 просмотров • 2 лет назад

Today in nature, we report a spatial single-cell atlas of human cortical development, revealing surprisingly early specification of human cortical layers and areas. We built an interactive browser to explore the spatial data: Paper link below👇

Today in nature, we report a spatial single-cell atlas of human cortical development, revealing surprisingly early specification of human cortical layers and areas. We built an interactive browser to explore the spatial data: Paper link below👇

Xuyu Qian

53,373 просмотров • 1 год назад

Here's how we made the Music Box of Fate Charity Single together with a full VTuber orchestra, a bunch of Ironmouse's friends, and a choir of nearly 100 of her fans! Listen to the full song now to support the Immune Deficiency Foundation❤️ #ironmouseart

Here's how we made the Music Box of Fate Charity Single together with a full VTuber orchestra, a bunch of Ironmouse's friends, and a choir of nearly 100 of her fans! Listen to the full song now to support the Immune Deficiency Foundation❤️ #ironmouseart

Rosa Nagashi🎹❤️ Vtuber/Musician/Miliastra Dev

19,422 просмотров • 10 месяцев назад

🚨Here's what a lot of people misunderstand about cancer treatment, says drpaulmarik: "Cancer is not homogeneous. The somatic mutation theory—which is the current theory in which treatment is based—posits that you have a mutation in a single cell, and that gives rise to a whole population of cells that look the same and have the same mutation. But the Cancer Genome Atlas has shown that that theory is completely wrong. The cancer cells are very heterogeneous, so they're made up of very different populations of cells with different mutations, and one of the populations is the cancer stem cell. It's a sub-population of the cancer. These are generally slow-growing, but they're distinct in that they have the ability to divide indefinitely and grow indefinitely, and can change their characteristics. Basically, if you get rid of the fast-dividing cells, which is the cancer, you're left with the stem cells, which then become the roots, which grow back to form the tumor" sometimes years later. Conventional chemotherapy gets rid of the fast-dividing regular cancer cells but *NOT* the stem cells. So the key question is: how do you get rid of the stem cells? “There are a number of repurposed drugs that do it, and this has been well-established in scientific medical literature. One of the most effective treatments to knock out the stem cell is the famous horse deworming medicine," says drpaulmarik. Yes, ivermectin. Independent Medical Alliance

🚨Here's what a lot of people misunderstand about cancer treatment, says drpaulmarik: "Cancer is not homogeneous. The somatic mutation theory—which is the current theory in which treatment is based—posits that you have a mutation in a single cell, and that gives rise to a whole population of cells that look the same and have the same mutation. But the Cancer Genome Atlas has shown that that theory is completely wrong. The cancer cells are very heterogeneous, so they're made up of very different populations of cells with different mutations, and one of the populations is the cancer stem cell. It's a sub-population of the cancer. These are generally slow-growing, but they're distinct in that they have the ability to divide indefinitely and grow indefinitely, and can change their characteristics. Basically, if you get rid of the fast-dividing cells, which is the cancer, you're left with the stem cells, which then become the roots, which grow back to form the tumor" sometimes years later. Conventional chemotherapy gets rid of the fast-dividing regular cancer cells but NOT the stem cells. So the key question is: how do you get rid of the stem cells? “There are a number of repurposed drugs that do it, and this has been well-established in scientific medical literature. One of the most effective treatments to knock out the stem cell is the famous horse deworming medicine," says drpaulmarik. Yes, ivermectin. Independent Medical Alliance

Jan Jekielek

96,002 просмотров • 1 год назад

It was truly a blessing for me and my wife to travel with The Tabernacle Choir and Orchestra at Temple Square to Brazil. I am grateful for this assignment as it is something I never dreamed of doing. Accompanying the The Tabernacle Choir in my home country, in the very city where I grew up, was an incredible experience. These concerts were invitations to come unto Christ, the true source of hope. I am thankful for the Choir and Orchestra for sharing their talents with thousands this week in Brazil and around the world.

It was truly a blessing for me and my wife to travel with The Tabernacle Choir and Orchestra at Temple Square to Brazil. I am grateful for this assignment as it is something I never dreamed of doing. Accompanying the The Tabernacle Choir in my home country, in the very city where I grew up, was an incredible experience. These concerts were invitations to come unto Christ, the true source of hope. I am thankful for the Choir and Orchestra for sharing their talents with thousands this week in Brazil and around the world.

Ulisses Soares

11,316 просмотров • 6 месяцев назад

Single-cell technologies now let us profile entire transcriptomes in individual cells. But how do we make sense of this complexity in a biologically meaningful way? Many methods summarise cells into a single embedding, but this often comes at the cost of interpretability, especially when multiple gene programs are active at once. We developed Tripso, a self-supervised transformer model that represents cells through multiple gene program-specific embeddings, while also uncovering new programs directly from the data. Instead of collapsing biology into a single vector, Tripso decomposes cell state into multiple representations, each reflecting a different gene program. We explored this across multiple systems. In human hematopoiesis, spanning development to aging, Tripso identified distinct age-associated program activity, including stronger JAK-STAT signalling in early life and dynamic IKZF1-related changes during B cell maturation. By comparing in vitro culture conditions with in vivo hematopoietic stem cell states, Tripso suggested that targeting the SEC61 translocon could enhance stem cell maintenance ex vivo, a prediction that we subsequently validated experimentally. In parallel, we identified a previously uncharacterised tissue-resident memory T-cell program associated with atopic dermatitis and mapped it to distinct spatial immune niches Together, these results show how modelling cells through gene programs can lead to interpretable and experimentally testable insights. More broadly, this work points toward a more interpretable and biologically grounded models of cell state. As single-cell datasets continue to grow, we hope approaches like Tripso will help bridge the gap between data-driven representations and biological insight. This work wouldn’t have been possible without the contributions of an amazing team. Thank you to co-first authors Marie, Tomoya Isobe, Amirhosein Vahidi, Carlo Leonardi, and everyone from roser's Lab, Haniffa Lab, Nicola Wilson and Bertie Gottgens's Lab, bringing together expertise across Cambridge Stem Cell Institute, Open Targets, Wellcome Sanger Institute and Cambridge University. Marie is one of the very best PhD students I have ever supervised. She is truly a force of nature, exceptionally resourceful, deeply innovative, and one of the most impressive scientists I have worked with. I am immensely proud of her and all that she has accomplished. As she begins her internship at Genentech , I have no doubt she will do amazing work there and continue to make her mark. paper: code:

Mo Lotfollahi

22,500 просмотров • 5 месяцев назад

Chris Camillo explains how Bloom Energy is solving AI data center energy usage problems… “ $BE is one of the only companies in the world that has a method to essentially bring almost an infinite amount of energy to these data centers." And we won't get into all the technology, but they are now working with hyperscalers and they are a resource for energy that will allow a data center to get ramped up quicker. "So, even though it costs a little bit more money today to put an energy cell from Bloom, an energy box from Bloom, which are energy cells, it essentially takes things like natural gas, but instead of burning the natural gas, there is a chemical process that turns that natural gas into energy for the data center."

Chris Camillo explains how Bloom Energy is solving AI data center energy usage problems… “ $BE is one of the only companies in the world that has a method to essentially bring almost an infinite amount of energy to these data centers." And we won't get into all the technology, but they are now working with hyperscalers and they are a resource for energy that will allow a data center to get ramped up quicker. "So, even though it costs a little bit more money today to put an energy cell from Bloom, an energy box from Bloom, which are energy cells, it essentially takes things like natural gas, but instead of burning the natural gas, there is a chemical process that turns that natural gas into energy for the data center."

The Iced Coffee Hour

277,703 просмотров • 3 месяцев назад

Who else got chills when "Unleashed" opened 🎻The Symphonic Synergy🎻 shows? That moment — with orchestra, choir, and a special new arrangement — is now live on our YouTube channel, ready for you to relive. 📺 Watch now – link in bio!

Who else got chills when "Unleashed" opened 🎻The Symphonic Synergy🎻 shows? That moment — with orchestra, choir, and a special new arrangement — is now live on our YouTube channel, ready for you to relive. 📺 Watch now – link in bio!

Epica

11,812 просмотров • 1 год назад