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Abstract
Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.
Academic Publication CellRank 2: unified fate mapping in multiview single-cell data
Research Abstract & Technology Focus
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CellRank 2: unified fate mapping in multiview single-cell data
Abstract Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajector...
Calculate multilple stats for two or more columns in R data.table
An alternative where the function takes "all columns" (instead of individual columns). stats2 setNames(paste0(nm, c("_maximo", "_minimo"))) }, frm, names(frm)) |> unname() |> do.call(cbind.d...
CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data
Abstract Hundreds of millions of single cells have been analyzed using high-throughput transcriptomic methods. The cumulative knowledge within these datasets provides an exciting opp...
Inferring stochastic dynamics by biophysical Neural ODE using single-cell transcriptomics
Dou and colleagues present DynNet, a biophysically informed deep learning framework that reconstructs continuous cellular dynamics from time-resolved single-cell data. DynNet maps cell fate transit...
Clustering single-cell multi-omics data via weighted distance penalty and adaptive consistent graph regularization
Author summary Recent advancements in single-cell high-throughput technology have transformed single-cell multi-omics, enabling researchers to study cellular heterogeneity with high resolution. Thi...
Frequently Asked Questions (FAQ)
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What is the core focus of the research titled 'CellRank 2: unified fate mapping in multiview single-cell data'?
This literature focuses on: Abstract Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorp...
Are there open-source GitHub repositories related to CellRank 2: unified fate mapping in multiview single-cell data?
Yes, open-source projects like BigBodyCobain/Shadowbroker (Open-source intelligence for the global theater. Track everything from the corporate/private jets of the wealthy, and spy satellites, to seismic ev...) are actively building upon these concepts.
Which startups are commercializing the technology behind CellRank 2: unified fate mapping in multiview single-cell data?
Products like Cursor 3 are bringing this to market. Their focus is: Unified workspace for parallel local/cloud agents and MCPs.
What other academic literature is closely related to 'CellRank 2: unified fate mapping in multiview single-cell data'?
Yes, highly correlated activity was mapped. An entry titled 'CellRank 2: unified fate mapping in multiview single-cell data' discusses this: Abstract Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or ...
How is the concept of 'CellRank 2: unified fate mapping in multiview single-cell data' being discussed by engineers on StackExchange?
Yes, highly correlated activity was mapped. An entry titled 'Calculate multilple stats for two or more columns in R data.table' discusses this: An alternative where the function takes "all columns" (instead of individual columns). stats2 setNames(paste0(nm, c("_maximo", "_minimo"))) }, f...
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Commercial Realization
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GitHubBigBodyCobain/Shadowbroker
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