Skip to main content

Mapping Vector Field of Single Cells

Project description

upload conda download star build documentation upload_python_package test

Dynamo: Mapping Transcriptomic Vector Fields of Single Cells

Inclusive model of expression dynamics with metabolic labeling based scRNA-seq / multiomics, vector field reconstruction, potential landscape mapping, differential geometry analyses, and most probably paths / in silico perturbation predictions.

Installation - Ten minutes to dynamo - Tutorials - API - Citation - Theory

Dynamo

Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo, which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Highlights of dynamo

  • Robust and accurate estimation of RNA velocities for regular scRNA-seq datasets:
    • Three methods for the velocity estimations (including the new negative binomial distribution based approach)
    • Improved kernels for transition matrix calculation and velocity projection
    • Strategies to correct RNA velocity vectors (when your RNA velocity direction is problematic)
  • Inclusive modeling of time-resolved metabolic labeling based scRNA-seq:
    • Overcome intrinsic limitation of the conventional splicing based RNA velocity analyses
    • Explicitly model RNA metabolic labeling, in conjunction with RNA bursting, transcription, splicing and degradation
    • Comprehensive RNA kinetic rate estimation for one-shot, pulse, chase and mixture metabolic labeling experiments
  • Move beyond RNA velocity to continuous vector field function for gaining mechannistic insights of cell fate transitions:
    • Dynamical systems approaches to identify stable cell types (fixed points), boundaries of cell states (separatrices), etc
    • Calculate RNA acceleration (reveals early drivers), curvature (reveals master regulators of fate decision points), divergence (stability of cell states) and RNA Jacobian (cell-state dependent regulatory networks)
    • Various downstream differential geometry analyses to rank critical regulators/effectors, and visualize regulatory networks at key fate decision points
  • Non-trivial vector field predictions of cell fate transitions:
    • Least action path approach to predict the optimal paths and transcription factors of cell fate reprogrammings
    • In silico perturbation to predict the gene-wise perturbation effects and cell fate diversion after genetic perturbations

News

  • 5/30/2023: dynamo 1.3.0 released!
  • 3/1/2023: We welcome @Sichao25 to join the dynamo develop team!
  • 1/28/2023: We welcome @Ukyeon to join the dynamo develop team!
  • 15/12/2022: Thanks for @elfofmaxwell and @MukundhMurthy's contribution. dynamo 1.2.0 released
  • 11/11/2022: the continuing development of dynamo and the Aristotle ecosystem will be supported by CZI. See here
  • 4/14/2022: dynamo 1.1.0 released!
  • 3/14/2022: Since today dynamo has its own logo! Here the arrow represents the RNA velocity vector field, while the helix the RNA molecule and the colored dots RNA metabolic labels (4sU labeling). See readthedocs
  • 2/15/2022: primers and tutorials on least action paths and in silico perturbation are released.
  • 2/1/2022: after 3.5+ years of perseverance, our dynamo paper is finally online in Cell today!

Discussion

Please use github issue tracker to report coding related issues of dynamo. For community discussion of novel usage cases, analysis tips and biological interpretations of dynamo, please join our public slack workspace: dynamo-discussion (Only a working email address is required from the slack side).

Contribution

If you want to contribute to the development of dynamo, please check out CONTRIBUTION instruction: Contribution

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dynamo-release-1.4.1.tar.gz (638.4 kB view details)

Uploaded Source

Built Distribution

dynamo_release-1.4.1-py3-none-any.whl (718.8 kB view details)

Uploaded Python 3

File details

Details for the file dynamo-release-1.4.1.tar.gz.

File metadata

  • Download URL: dynamo-release-1.4.1.tar.gz
  • Upload date:
  • Size: 638.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.6

File hashes

Hashes for dynamo-release-1.4.1.tar.gz
Algorithm Hash digest
SHA256 53e246e89d8d4edf92e1d0ddebe1f5625d37933ce56542a9014bab6ce471f5a9
MD5 381d3d6418d14d00bac5a8cca9621bb2
BLAKE2b-256 47af09ba5443c2a77cae21c9e6531354199237facdae7f155463405e89e09669

See more details on using hashes here.

File details

Details for the file dynamo_release-1.4.1-py3-none-any.whl.

File metadata

File hashes

Hashes for dynamo_release-1.4.1-py3-none-any.whl
Algorithm Hash digest
SHA256 a1938d51dd78e0ffbebb405a9c92dafa21c50e7879498f2e24f05bab4bd766dd
MD5 75e64bfc9104d3384a6dd047677cd17d
BLAKE2b-256 8942624407a277b4d886649171b9529a573c6ca3f02a5e75fa6d57cd7145bd07

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page