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in-silico-fate-mapping

https://github.com/royerlab/in-silico-fate-mapping

Science Score: 44.0%

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    Low similarity (15.5%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: royerlab
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: main
  • Size: 12.1 MB
Statistics
  • Stars: 7
  • Watchers: 1
  • Forks: 1
  • Open Issues: 2
  • Releases: 2
Created about 3 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

in silico fate mapping

License BSD-3 PyPI Python Version tests codecov napari hub

Interactive in silico fate mapping from tracking data.

This napari plugin estimates the cell fates from tracking data by building a radial regression model per time point. The user can select an area of interest using a Points layer; the algorithm will advent the probed coordinates forward (or backward) in time, showing the estimated fate.

Video example below:

https://user-images.githubusercontent.com/21022743/216478216-89c1c35f-2ce4-44e8-adb8-9aeea75b5833.mp4

Installation

We suggest you create a fresh conda environment to avoid conflicts with your existing package. To do this, you need to:

conda create -n fatemap python=3.11
conda activate fatemap

And then, you can install in-silico-fate-mapping via pip and other additional useful packages:

pip install napari-ome-zarr napari[all] in-silico-fate-mapping

To install the latest development version :

pip install git+https://github.com/royerlab/in-silico-fate-mapping.git

IO file format

This plugin does not depend on a specific file format, the only requirement is using a Track layer from napari.

Despite this, we ship a reader and writer interface. It supports .csv files with the following reader track_id, t, (z), y, x, z is optional. Such that each tracklet has a unique track_id and it's composed of a sequence o time and spatial coordinates.

This is extremely similar to how napari store tracks, more information can be found here.

Divisions are not supported at the moment.

Usage Example

Minimal example

Minimal example using a track file following the convention described above.

```python3 import napari import pandas as pd from insilicofatemapping.fatemapping import FateMapping

tracks = pd.read_csv("tracks.csv")

fatemap = FateMapping(radius=5, nsamples=25, bindtoexisting=False, sigma=1) fatemap.data = tracks[["trackid", "t", "z", "y", "x"]]

source = tracks[tracks["t"] == 0].sample(n=1)

tracks = fate_map(source[["t", "z", "y", "x"]])

napari.view_tracks(tracks) napari.run() ```

Zebrahub example

Zebrafish embryo tail example. This example requires the package napari-ome-zarr.

```python3 import napari import pandas as pd from insilicofate_mapping import FateMappingWidget

imagepath = "http://public.czbiohub.org/royerlab/zebrahub/imaging/single-objective/ZSNS001tail.ome.zarr" trackspath = "http://public.czbiohub.org/royerlab/zebrahub/imaging/single-objective/ZSNS001tail_tracks.csv"

viewer = napari.Viewer() viewer.window.adddockwidget(FateMappingWidget(viewer))

viewer.open(image_path, plugin="napari-ome-zarr")

tracks = pd.readcsv(trackspath) viewer.addtracks(tracks[["trackid", "t", "z", "y", "x"]]) viewer.add_points(name="Markers", ndim=4)

napari.run() ```

Citing

If used please cite:

@article{lange2023zebrahub, title={Zebrahub-Multimodal Zebrafish Developmental Atlas Reveals the State Transition Dynamics of Late Vertebrate Pluripotent Axial Progenitors}, author={Lange, Merlin and Granados, Alejandro and VijayKumar, Shruthi and Bragantini, Jordao and Ancheta, Sarah and Santhosh, Sreejith and Borja, Michael and Kobayashi, Hirofumi and McGeever, Erin and Solak, Ahmet Can and others}, journal={bioRxiv}, pages={2023--03}, year={2023}, publisher={Cold Spring Harbor Laboratory} }

Issues

If you encounter any problems, please file an issue along with a detailed description.

Owner

  • Name: Royer Lab
  • Login: royerlab
  • Kind: organization
  • Email: loic.royer@czbiohub.org

Official repository of the Royer Lab where all the freaking cool stuff is cooked

Citation (CITATION.cff) 

type: article
title: Zebrahub-Multimodal Zebrafish Developmental Atlas Reveals the State Transition
  Dynamics of Late Vertebrate Pluripotent Axial Progenitors
authors:
- family-names: Lange
  given-names: Merlin
- family-names: Granados
  given-names: Alejandro
- family-names: VijayKumar
  given-names: Shruthi
- family-names: Bragantini
  given-names: Jordao
- family-names: Ancheta
  given-names: Sarah
- family-names: Santhosh
  given-names: Sreejith
- family-names: Borja
  given-names: Michael
- family-names: Kobayashi
  given-names: Hirofumi
- family-names: McGeever
  given-names: Erin
- family-names: Solak
  given-names: Ahmet Can
- name: others
journal: bioRxiv
year: '2023'
publisher:
  name: Cold Spring Harbor Laboratory
start: '2023'
end: '03'

GitHub Events

Total
  • Push event: 1
Last Year
  • Push event: 1

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 37
  • Total Committers: 1
  • Avg Commits per committer: 37.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Jordao Bragantini j****i@c****g 37
Committer Domains (Top 20 + Academic)
czbiohub.org: 1

Issues and Pull Requests

Last synced: 9 months ago

All Time
  • Total issues: 2
  • Total pull requests: 5
  • Average time to close issues: 2 months
  • Average time to close pull requests: 8 days
  • Total issue authors: 2
  • Total pull request authors: 2
  • Average comments per issue: 1.0
  • Average comments per pull request: 0.2
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • goanpeca (1)
  • JoOkuma (1)
Pull Request Authors
  • JoOkuma (4)
  • aganders3 (2)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 32 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 4
  • Total maintainers: 3
pypi.org: in-silico-fate-mapping

TODO

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 32 Last month
Rankings
Dependent packages count: 6.6%
Average: 18.6%
Dependent repos count: 30.6%
Maintainers (3)
royerloic acs-czbiohub JoOkuma
Last synced: 8 months ago