Saravanan666/pypi-org huggingface dataset:
Package: cellmaps-ppidownloader
Cell Maps for AI PPI DownloaderDownloads protein-protein interaction dataFree software: MIT licenseDocumentation:https://cellmaps-ppidownloader.readthedocs.io.Dependenciescellmaps_utilsrequestsmygenetqdmCompatibilityPython 3.8+Installationgit clone https://github.com/idekerlab/cellmaps_ppidownloader
cd cellmaps_ppidownloader
make dist
pip install dist/cellmaps_ppidownloader*whlRunmakecommand with no arguments to see other build/deploy options including creation of Docker imagemakeOutput:clean                remove all build, test, coverage and Python artifacts
clean-build          remove build artifacts
clean-pyc            remove Python file artifacts
clean-test           remove test and coverage artifacts
lint                 check style with flake8
test                 run tests quickly with the default Python
test-all             run tests on every Python version with tox
coverage             check code coverage quickly with the default Python
docs                 generate Sphinx HTML documentation, including API docs
servedocs            compile the docs watching for changes
testrelease          package and upload a TEST release
release              package and upload a release
dist                 builds source and wheel package
install              install the package to the active Python's site-packages
dockerbuild          build docker image and store in local repository
dockerpush           push image to dockerhubNeeded filesbait list file: TSV file of baits used for AP-MS experimentsedge list file: TSV file of edges for protein interaction networkprovenance: file containing provenance information about input files in JSON format (see sample provenance file in examples folder)UsageFor information invokecellmaps_ppidownloadercmd.py -hExample usagecellmaps_ppidownloadercmd.py ./cellmaps_ppidownloader_outdir --edgelist examples/edgelist.tsv --baitlist examples/baitlist.tsv --provenance examples/provenance.jsonVia DockerExample usageComing soon...CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2024-01-01)First release on PyPI.
Package: cellmaps-ppi-embedding
Cell Maps PPI EmbedderGenerate embeddings from Protein-Protein interaction networks usingnode2vecFree software: MIT licenseDocumentation:https://cellmaps-ppi-embedding.readthedocs.io.Dependenciescellmaps_utilsnode2vecnetworkxCompatibilityPython 3.8+Installationgit clone https://github.com/idekerlab/cellmaps_ppi_embedding
cd cellmaps_ppi_embedding
make dist
pip install dist/cellmaps_ppi_embedding*whlRunmakecommand with no arguments to see other build/deploy options including creation of Docker imagemakeOutput:clean                remove all build, test, coverage and Python artifacts
clean-build          remove build artifacts
clean-pyc            remove Python file artifacts
clean-test           remove test and coverage artifacts
lint                 check style with flake8
test                 run tests quickly with the default Python
test-all             run tests on every Python version with tox
coverage             check code coverage quickly with the default Python
docs                 generate Sphinx HTML documentation, including API docs
servedocs            compile the docs watching for changes
testrelease          package and upload a TEST release
release              package and upload a release
dist                 builds source and wheel package
install              install the package to the active Python's site-packages
dockerbuild          build docker image and store in local repository
dockerpush           push image to dockerhubFor developersTo deploy development versions of this packageBelow are steps to make changes to this code base, deploy, and then run
against those changes.Make changesModify code in this repo as desiredBuild and deploy# From base directory of this repo cellmaps_ppi_embedding
pip uninstall cellmaps_ppi_embedding -y ; make clean dist; pip install dist/cellmaps_ppi_embedding*whlNeeded filesThe output directory for the PPI download is required (seeCell Maps PPI Downloader).UsageFor information invokecellmaps_ppi_embeddingcmd.py -hExample usagecellmaps_ppi_embeddingcmd.py ./cellmaps_ppi_embedding_outdir --inputdir ./cellmaps_ppidownloader_outdirVia DockerExample usageComing soon...CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2024-02-01)First release on PyPI.
Package: cellmaps-utils
Cell Maps Pipeline UtilitiesContains utilities needed by various tools in the cell maps toolkit.Free software: MIT licenseDocumentation:https://cellmaps-utils.readthedocs.io.Source code:https://github.com/idekerlab/cellmaps_utilsDependenciesfairscape-cliscipyscikit-learnpandasnumpydillCompatibilityPython 3.8+Installationgit clone https://github.com/idekerlab/cellmaps_utils
cd cellmaps_utils
make dist
pip install dist/cellmaps_utils*whlRunmakecommand with no arguments to see other build/deploy options including creation of Docker imagemakeOutput:clean                remove all build, test, coverage and Python artifacts
clean-build          remove build artifacts
clean-pyc            remove Python file artifacts
clean-test           remove test and coverage artifacts
lint                 check style with flake8
test                 run tests quickly with the default Python
test-all             run tests on every Python version with tox
coverage             check code coverage quickly with the default Python
docs                 generate Sphinx HTML documentation, including API docs
servedocs            compile the docs watching for changes
testrelease          package and upload a TEST release
release              package and upload a release
dist                 builds source and wheel package
install              install the package to the active Python's site-packagesFor developersTo deploy development versions of this packageBelow are steps to make changes to this code base, deploy, and then run
against those changes.Make changesModify code in this repo as desiredBuild and deploy# From base directory of this repo cellmaps_hierarchyeval
pip uninstall cellmaps_utils -y ; make clean dist; pip install dist/cellmaps_utils*whlCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject templateHistory0.2.0 (2024-02-20)Bumped fairscape-cli dependency to0.1.14to support schemasAdded support forschematodata_dictparameter inProvenanceUtil.register_dataset()Added--releaseflag tocellmaps_utilscmd.py rocratetableand
in output table renamed “Name of Computation” to “Name” as well as
added “Type”, “Cell Line”, “Treatment”, “Gene set”, and “Version” to
table outputSet default logging level toERRORforcellmaps_utilscmd.pycommand0.1.0 (2024-01-01)First release on PyPI.
Package: cellmaps-vnn
cellmaps_vnnCell Maps Visual Neural Network ToolkitFree software: MIT licenseDocumentation:https://cellmaps-vnn.readthedocs.io.Dependenciescellmaps_utilsndex2optunascikit-learnnetworkxpandastorchtorchvisiontorchaudioCompatibilityPython 3.8+Installationgit clone https://github.com/idekerlab/cellmaps_vnn
cd cellmaps_vnn
make dist
pip install dist/cellmaps_vnn*whlRunmakecommand with no arguments to see other build/deploy options including creation of Docker imagemakeOutput:clean                remove all build, test, coverage and Python artifacts
clean-build          remove build artifacts
clean-pyc            remove Python file artifacts
clean-test           remove test and coverage artifacts
lint                 check style with flake8
test                 run tests quickly with the default Python
test-all             run tests on every Python version with tox
coverage             check code coverage quickly with the default Python
docs                 generate Sphinx HTML documentation, including API docs
servedocs            compile the docs watching for changes
testrelease          package and upload a TEST release
release              package and upload a release
dist                 builds source and wheel package
install              install the package to the active Python's site-packages
dockerbuild          build docker image and store in local repository
dockerpush           push image to dockerhubFor developersTo deploy development versions of this packageBelow are steps to make changes to this code base, deploy, and then run
against those changes.Make changesModify code in this repo as desiredBuild and deploy# From base directory of this repo cellmaps_vnn
pip uninstall cellmaps_vnn -y ; make clean dist; pip install dist/cellmaps_vnn*whlNeeded filesTODO:Add description of needed filesUsageFor information invokecellmaps_vnncmd.py -hExample usageTODO:Add information about example usagecellmaps_vnncmd.py # TODO Add other needed arguments hereVia DockerExample usageTODO:Add information about example usageComing soon ...CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2024-02-01)First release on PyPI.
Package: cellmirage
MIRAGEEfficient and scalable image registration (alignment) for multiplexed imaging data. Currently only available for GPU.Installationgit clone https://github.com/dpeerlab/MIRAGE.git
cd mirage
pip install -e .ExampleAlso, check out the example intests/quickstart.ipynb:Before alignment:After alignment:# load package
import mirage

# Load images
image = mirage.tl.get_data("sample2_image.tiff")
reference = mirage.tl.get_data("sample2_reference.tiff")
# Images must be 2D numpy arrays (grayscale only) and scaled to 0-1.

# Initialise model
mirage_model = mirage.MIRAGE(
    images=image,
    references=reference,
    bin_mask=bin_mask,
    pad=12,
    offset=12,
    num_neurons=196,  # more for larger images
    num_layers=2,  # more for larger images
    pool=1, 
    loss="SSIM"
)

# Train model
mirage_model.train(batch_size=256, num_steps=256, lr__sched=True, LR=0.005)

# Apply transformation
mirage_model.compute_transform()
image_aligned = mirage_model.apply_transform(image)

# Inspect results pre/post alignment of a smale mesh
mesh = mirage.tl.Mesh(x=80, y=160, pad=35)
mirage.pl.plot_before_after(reference, image, image_aligned, mesh=mesh)
Package: cellmlmanip
cellmlmanipCellML loading and model equation manipulation libraryInstallingWe recommend installing cellmlmanip in a virtual environment (e.g. using conda or pipenv or virtualenv).Users installcellmlmanipusing pip.pip install cellmlmanipCellmlmanip is tested and developed on Python 3.5 and higher.Release notesFor release notes seeRELEASE.mdDocumentationAPI documentation explaining how to use cellmlmanip can be found onreadthedocsContributingFor developer instructions, seeCONTRIBUTING.md
Package: cell-models
Cell Models PackageThis package is meant to contain all Christini Lab models that are implemented in Python. As of writing this, there are 3 models: O'Hara Rudy, Paci, and Kernik.Using and Installing this ProjectThis Python project is written inPython Packaging format. If you want to play around with the models quickly, you cancdintocell-models/and edit themain.pyfile.If you want to install these models locally, you can runpython3 setup.pyfrom the project root. This will make the files incell-models/globally available.TutorialMuch of the code below can be found incell-models/main.py.Instantiate and generate a response from modelsThe following will plot spontaneous Kernik and Paci models and a paced O'Hara Rudy model.# Spontaneous / StimulatedKERNIK_PROTOCOL=protocols.SpontaneousProtocol(2000)kernik_baseline=KernikModel()tr_b=kernik_baseline.generate_response(KERNIK_PROTOCOL)plt.plot(tr_b.t,tr_b.y)plt.show()PACI_PROTOCOL=protocols.SpontaneousProtocol(2)paci_baseline=PaciModel()tr_bp=paci_baseline.generate_response(PACI_PROTOCOL)plt.plot(tr_bp.t,tr_bp.y)plt.show()OHARA_RUDY=protocols.PacedProtocol(model_name="OR")or_baseline=OharaRudyModel()tr=or_baseline.generate_response(OHARA_RUDY)plt.plot(tr.t,tr.y)plt.show()Update parameters for a modelThe code below will plot the baseline Kernik model and an example model with new parameter values.KERNIK_PROTOCOL=protocols.SpontaneousProtocol(2000)kernik_baseline=KernikModel()kernik_updated=KernikModel(updated_parameters={'G_K1':1.2,'G_Kr':0.8,'G_Na':2.2})tr_baseline=kernik_baseline.generate_response(KERNIK_PROTOCOL)tr_updated=kernik_updated.generate_response(KERNIK_PROTOCOL)plt.plot(tr_baseline.t,tr_baseline.y,label="Baseline")plt.plot(tr_updated.t,tr_updated.y,label="Updated")plt.legend()plt.show()
Package: cellnopt.wrapper
The packagecellnopt.wrapperprovides a wrapper to the CellNOpt R packages using rpy2.
It allows to easily access toCellNOptRpackage
via python. In addition, a python class is provided to manipulate the package in
an object-oriented fashion. Similarly, other related R package are wrapped
(e.g., CNORode and CNORfuzzy, coming soon on bioconductor)For instance, to access all CellNOptR functionalities, type:from cellnopt.wrapper import *For a full documentation, see the sphinx documentation in the ./doc directory.Please visit alsowww.cellnopt.orgfor more information about CellNOpt.
Package: cellocity
CellocityCellocity is a python package that allows the user to perform Optical Flow and PIV analysis on microscopy data. It is intended to be helpful in both the exploratory and in the data acquisition phases of a cell layer dynamics project. In the exploratory phase, several different algorithms, settings, and preprocessing functions can be evaluated. In the acquisition phase, a pipeline can be "hard coded" for processing of multiple data sets through a pre-determined series of steps.Velocity and vector analysis of microscopy dataCellocity can create vector field visualizations, speed graphs, and multiple advanced vector field analyses, such as calculating the instantaneous order parameter, alignment index and correlation lengths of migrating cells.Documentation & Software validationDocumentation is available atRead the Docs.
There you can also find an extensive chapter on the validation of the Cellocity software.
Package: cello-classify
CellO (Cell Ontology-based classification) is a Python package for performing cell type classification of human RNA-seq data. CellO makes hierarchical predictions against the Cell Ontology (http://www.obofoundry.org/ontology/cl.html). These classifiers were trained on nearly all of the human primary cell, bulk RNA-seq data in the NCBI’s Sequence Read Archive.For instructions on installing and running CellO, see the GitHub repository:https://github.com/deweylab/CellOFor more details regarding the underlying method, see the paper:Bernstein, M.N., Ma, J., Gleicher, M., Dewey, C.N. (2020). CellO: Comprehensive and hierarchical cell type classification of human cells with the Cell Ontology. iScience, 24(1), 101913.https://doi.org/10.1016/j.isci.2020.101913.
Package: celloracle
CellOracle is a python library for in silico gene perturbation analyses
using single-cell omics data and Gene Regulatory Network models.For more information, please read our paper:Dissecting cell identity
via network inference and in silico gene
perturbation.Documentation, Codes, and TutorialsCellOracle documentation is available through the link below.Web
documentationQuestions and errorsIf you have a question, error, bug, or problem, please use theGithub
issue page.Supported Species and reference genomesHuman: [‘hg38’, ‘hg19’]Mouse: [‘mm39’, ‘mm10’, ‘mm9’]S.cerevisiae: [“sacCer2”, “sacCer3”]Zebrafish: [“danRer7”, “danRer10”, “danRer11”]Xenopus tropicalis: [“xenTro2”, “xenTro3”]Xenopus laevis: [“Xenopus_laevis_v10.1”]Rat: [“rn4”, “rn5”, “rn6”]Drosophila: [“dm3”, “dm6”]C.elegans: [“ce6”, “ce10”]Arabidopsis: [“TAIR10”]Chicken: [“galGal4”, “galGal5”, “galGal6”]Guinea Pig: [“Cavpor3.0”]Pig: [“Sscrofa11.1”]ChangelogPlease go tothis
page.
Package: cellpack
cellPackalgorithm to pack molecular recipesSetupcreate a virtual env:conda create -n autopackactivate autopackpip install -e .[dev]Run analysis codeBy default analyze will run all packing methods on `cellpack/test-recipes/NM_Analysis_FigureB1.0.json
Examples:analyze -o [PATH/TO/OUTPUT/FOLDER]will create subfolders for each packing method at your output folderTo run just one packing method:analyze -o [PATH/TO/OUTPUT/FOLDER] -p jitterTo change the dimension of the packing:analyze -r cellpack/test-recipes/NM_Analysis_FigureC1.json  -o /Users/meganriel-mehan/Dropbox/cellPack/NM_Analysis_C_1 -d 3Turn off plotly plot:analyze -o [PATH/TO/OUTPUT/FOLDER] -ngoranalyze -o [PATH/TO/OUTPUT/FOLDER] --no-grid-plotRun conversion codeTo convert to simularium and view athttps://staging.simularium.allencell.org/viewerconvert -r [FULL_PATH_TO_INPUT_RECIPE_FILE] -p [FULL_PATH_TO_PACKING_RESULT] -o [OUTPUT_PATH]InstallationStable Release:pip install cellpackDevelopment Head:pip install git+https://github.com/mesoscope/cellpack.gitDocumentationFor full package documentation please visitmesoscope.github.io/cellpack.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.Contributing cheat sheetpip install -e .[dev]This will install your package in editable mode with all the required development
dependencies (i.e.tox).make buildThis will runtoxwhich will run all your tests in both Python 3.7
and Python 3.8 as well as linting your code.make cleanThis will clean up various Python and build generated files so that you can ensure
that you are working in a clean environment.make docsThis will generate and launch a web browser to view the most up-to-date
documentation for your Python package.Suggested Git Branch Strategymainis for the most up-to-date development, very rarely should you directly
commit to this branch. GitHub Actions will run on every push and on a CRON to this
branch but still recommended to commit to your development branches and make pull
requests to main. If you push a tagged commit with bumpversion, this will also release to PyPI.Your day-to-day work should exist on branches separate frommain. Even if it is
just yourself working on the repository, make a PR from your working branch tomainso that you can ensure your commits don't break the development head. GitHub Actions
will run on every push to any branch or any pull request from any branch to any other
branch.It is recommended to use "Squash and Merge" commits when committing PR's. It makes
each set of changes tomainatomic and as a side effect naturally encourages small
well defined PR's.MIT license
Package: cellplm
CellPLMThis is the official codebase forCellPLM: Pre-training of Cell Language Model Beyond Single Cells.CellPLMis the first single-CellPre-trainedLanguageModel that encodes cell-cell relations and it consistently outperforms existing pre-trained and non-pre-trained models in diverse downstream tasks, with 100x higher inference speed compared to existing pre-trained models.InstallationWe recommend PyPI for quick installation. We recommend usingpython 3.9andcuda>=11.7but they are adjustable.Quick Installation with PyPIMake sure gpu version of pytorch (>=1.13.0) has been installed before installing CellPLM.pip install cellplmFull Installation (recommended for HPC users and developers)conda create -n cellplm python=3.9 -y && conda activate cellplm
conda install cudatoolkit=11.7 -c pytorch -c nvidia
pip install -r requirements.txtThe full installation will install the same environment as we used during development. This includesrapidsused to accelerate evaluation.TutorialsWe offer severalnotebooksfor various downstream tasks as introductory tutorials.We are also working on developing more streamlined protocols for supported tasks and a comprehensive documentation. We aim to release these by the end of the year.Pretrained CellPLM Model CheckpointsThe checkpoint can be acquired from ourdropbox. We might update our checkpoints from time to time.[10/10/2023] The latest version is20230926_85M.Citation@article{wen2023cellplm,
  title={CellPLM: Pre-training of Cell Language Model Beyond Single Cells},
  author={Wen, Hongzhi and Tang, Wenzhuo and Dai, Xinnan and Ding, Jiayuan and Jin, Wei and Xie, Yuying and Tang, Jiliang},
  journal={bioRxiv},
  pages={2023--10},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}
Package: cellrank
CellRank 2: Unified fate mapping in multiview single-cell dataCellRankis a modular framework to study cellular dynamics based on Markov state modeling of
multi-view single-cell data. See ourdocumentation, and theCellRank 1andCellRank 2 manuscriptto learn more.
Seeherefor how to properly cite our work.CellRank scales to large cell numbers, is fully compatible with thescverseecosystem, and easy to use.
In the backend, it is powered bypyGPCCA(Reuter et al. (2018)). Feel
free to open anissueor send us anemailif you encounter a bug, need our help or just
want to make a comment/suggestion.CellRank’s key applicationsEstimate differentiation direction based on a varied number of biological priors, including RNA velocity
(La Manno et al. (2018),Bergen et al. (2020)), any pseudotime or developmental potential,
experimental time points, metabolic labels, and more.Compute initial, terminal and intermediate macrostates.Infer fate probabilities and driver genes.Visualize and cluster gene expression trends.… and much more, check out ourdocumentation.
Package: cellreel
CellReelis a graphical user interface for performing fluorescence and
refractive index tomography of biological samples, particularly
if the acquired data must be aligned temporarily and spatially
post-measurement.DocumentationThe documentation, including the code reference and examples, is available atcellreel.readthedocs.io.Installationpip install cellreelTestingpip install -e .
pip install pytest pytest-qt
pytest tests
Package: cellregmap
CellRegMapCellular Regulatory Map (CellRegMap) is a linear mixed model approach to perform multi-context eQTL mapping by leveraging single cell RNA sequencing (scRNA-seq) data.
It is related to the previously proposedStructLMMbut importantly it can account for sample structure, including population structure and repeated observations for the same samples, e.g., multiple cells for the same donor.The CellRegMap model and its applications to both real and simulated data are described in theCellRegMap manuscript.We are working on more instructions and tutorials to facilitate usage of the package atthis link!InstallFrom your command line, enterpython3 -m pip install cellregmapin your command line.DevelopmentTo install it in development mode, entergit clone https://github.com/limix/CellRegMap.git
cd CellRegMap
python3 -m pip install -e .in your command line.Running testsFrom your command line, enterpython3 setup.py test
Package: cellseg
cellseg: Multiclass Cell SegmentationIntroductioncellsegis a PyTorch (torch) based deep learning package aimed at multiclass cell segmentation.InstallationpipinstallcellsegOr if you want to build from sourcegitclonegit@github.com:Nelson-Gon/cellseg.gitcdcellseg
pythonsetup.pyinstallDevelopment stageRead Tiff ImagesRead Non Tiff ImagesWrite Data Transformers and LoadersWrite functional modelModify model weights/layersUsage# imports data, utils, modelfromcellsegimport*