Saravanan666/pypi-org huggingface dataset:
Package: cellfinder-napari
This package has movedcellfinder-naparihas merged with it's backend code and is now available as asingle package calledcellfinder.
We recommend you uninstallcellfinder-napariand instead use the functionality provided in thecellfinderpackage.These changes are part of ourwider restructuringof the BrainGlobe suite of tools and analysis pipelines, which you cankeep up to date with on our blog.cellfinder-napariEfficient cell detection in large images (e.g. whole mouse brain images)cellfinder-napariis a front-end tocellfinder-coreto allow ease of use within thenaparimultidimensional image viewer. For more details on this approach, please seeTyson, Rousseau & Niedworok et al. (2021). This algorithm can also be used within the originalcellfindersoftware for
whole-brain microscopy analysis.cellfinder-napari,cellfinderandcellfinder-corewere developed byCharly RousseauandAdam Tysonin theMargrie Lab, based on previous work byChristian Niedworok, generously supported by theSainsbury Wellcome Centre.Visualising detected cells in the cellfinder napari pluginInstructionsInstallationOnce you haveinstalled napari.
You can install napari either through the napari plugin installation tool, or
directly from PyPI with:pipinstallcellfinder-napariUsageFull documentation can be
foundhere.This software is at a very early stage, and was written with our data in mind.
Over time we hope to support other data types/formats. If you have any
questions or issues, please get in touchon the forumor byraising an issue.IllustrationIntroductioncellfinder takes a stitched, but otherwise raw dataset with at least
two channels:Background channel (i.e. autofluorescence)Signal channel, the one with the cells to be detected:Raw coronal serial two-photon mouse brain image showing labelled cellsCell candidate detectionClassical image analysis (e.g. filters, thresholding) is used to find
cell-like objects (with false positives):Candidate cells (including many artefacts)Cell candidate classificationA deep-learning network (ResNet) is used to classify cell candidates as true
cells or artefacts:Cassified cell candidates. Yellow - cells, Blue - artefactsContributingContributions to cellfinder-napari are more than welcome. Please see thedevelopers guide.Citing cellfinderIf you find this plugin useful, and use it in your research, please cite the paper outlining the cell detection algorithm:Tyson, A. L., Rousseau, C. V., Niedworok, C. J., Keshavarzi, S., Tsitoura, C., Cossell, L., Strom, M. and Margrie, T. W. (2021) “A deep learning algorithm for 3D cell detection in whole mouse brain image datasets’ PLOS Computational Biology, 17(5), e1009074https://doi.org/10.1371/journal.pcbi.1009074If you use this, or any other tools in the brainglobe suite, pleaselet us know, and
we'd be happy to promote your paper/talk etc.
Package: cell-flower
Cell FLOWerCell FLOWer processes edge flows using cell complexes.
It was developed for the paperRepresenting Edge Flows on Graphs via Sparse Cell Complexes.
You can find the evaluation workflowhere.
Install it using:pip install cell-flowerHow to use it (Also check out the completeexamples):importcell_flowerascfimportnetworkxasnxG=...# nx.GraphCC,node_list,node_index_map=cf.nx_graph_to_cc(G)# converts nodes in G to int and also returns the mappingflows=...# np.ndarray, shape (samples, edges)CC_prime=cf.cell_inference_approximation(CC,flows,2,2,n_clusters=5)# Check to see the cells that were recovered; map back to node labels[cf.index_to_cell(cell,node_list)forcellinCC_prime.get_cells(2)]If you use Cell FLOWer, please cite the following paper:@inproceedings{
      hoppe2023representing,
      title={Representing Edge Flows on Graphs via Sparse Cell Complexes},
      author={Josef Hoppe and Michael T Schaub},
      booktitle={The Second Learning on Graphs Conference},
      year={2023},
      url={https://openreview.net/forum?id=qix189lq5D}
}AcknowledgementsFunded by the European Union (ERC, HIGH-HOPeS, 101039827). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
Package: cell-gears
GEARS: Predicting transcriptional outcomes of novel multi-gene perturbationsThis repository hosts the official implementation of GEARS, a method that can predict transcriptional response to both single and multi-gene perturbations using single-cell RNA-sequencing data from perturbational screens.InstallationInstallPyG, and then dopip install cell-gears.[New] Updates in v0.1.1Fixed training breakpoint bug from v0.1.0Preprocessed dataloader now available for Replogle 2022 RPE1 and K562 essential datasetsAdded custom split, fixed no-test splitCore API InterfaceUsing the API, you can (1) reproduce the results in our paper and (2) train GEARS on your perturbation dataset using a few lines of code.fromgearsimportPertData,GEARS# get datapert_data=PertData('./data')# load dataset in paper: norman, adamson, dixit.pert_data.load(data_name='norman')# specify data splitpert_data.prepare_split(split='simulation',seed=1)# get dataloader with batch sizepert_data.get_dataloader(batch_size=32,test_batch_size=128)# set up and train a modelgears_model=GEARS(pert_data,device='cuda:8')gears_model.model_initialize(hidden_size=64)gears_model.train(epochs=20)# save/load modelgears_model.save_model('gears')gears_model.load_pretrained('gears')# predictgears_model.predict([['CBL','CNN1'],['FEV']])gears_model.GI_predict(['CBL','CNN1'],GI_genes_file=None)To use your own dataset, create a scanpy adata object with agene_namecolumn inadata.var, and two columnscondition,cell_typeinadata.obs. Then run:pert_data.new_data_process(dataset_name='XXX',adata=adata)# to load the processed datapert_data.load(data_path='./data/XXX')DemosNameDescriptionDataset TutorialTutorial on how to use the dataset loader and read customized dataModel TutorialTutorial on how to train GEARSPlot top 20 DE genesTutorial on how to plot the top 20 DE genesUncertaintyTutorial on how to train an uncertainty-aware GEARS modelColabNameDescriptionUsing Trained ModelUse a model trained on Norman et al. 2019 to make predictions (Needs Colab Pro)Cite Us@article{roohani2023predicting,
  title={Predicting transcriptional outcomes of novel multigene perturbations with gears},
  author={Roohani, Yusuf and Huang, Kexin and Leskovec, Jure},
  journal={Nature Biotechnology},
  year={2023},
  publisher={Nature Publishing Group US New York}
}Paper:LinkCode for reproducing figures:Link
Package: cell-machine-levels
This library is currently in beta and can have major API changesCell Machine LevelsA library for manipulating modded cell machine level codes.To install do:pip3installcell_machine_levelsOr justpipon some OSesLevel classCurrently supports the main 3 types (V1, V2, V3), support is going to be added for more than just theseLevel creation example:fromcell_machine_levelsimportleveltest=level.Level(10,10,"test",level.WallEffect.wrap)print(test.save("V3"))Level importing example:fromcell_machine_levelsimportleveltest=level.open("V3;a;a;;;test;2")print(f"{test.name}:{test.size},{test.wall_effect}")
Package: cellmap
CellMap - RNA landscape inference methodThe CellMap is a RNA landscape inference method based on the graph Hodge decomposition.InstallationTo install CellMap package, usepipas follows:$ pip install cellmapRequirementsPython3matplotlibnumpyscipyscikit-learnTurtorialTurtorials are prepared by jupyter notebook.Tutorial for pancreas endocrine cell dataVisualizationWe prepare original visualization systemCellMap viewer.
Package: cellmaps-coembedding
Cell Maps CoEmbedderCreates Coembedding fromCell Maps ImmunoFluorscent Image EmbedderandCell Maps PPI Embedderusing an implementation ofMUSEFree software: MIT licenseDocumentation:https://cellmaps-coembedding.readthedocs.io.Dependenciescellmaps_utilsphenographnumpytorchpandasmatplotlibdilltqdmCompatibilityPython 3.8+Installationgit clone https://github.com/idekerlab/cellmaps_coembedding
cd cellmaps_coembedding
make dist
pip install dist/cellmaps_coembedding*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_coembedding
pip uninstall cellmaps_coembedding -y ; make clean dist; pip install dist/cellmaps_coembedding*whlNeeded filesThe output directories for the image embeddings (seeCell Maps Image Embedding) and protein-protein interaction network embeddings (seeCell Maps PPI Embedding) are required.UsageFor information invokecellmaps_coembeddingcmd.py -hExample usagecellmaps_coembeddingcmd.py ./cellmaps_coembedding_outdir --embeddings ./cellmaps_image_embedding_outdir ./cellmaps_ppi_embedding_outdirVia DockerExample usageComing soon...CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2024-02-12)First release on PyPI.
Package: cellmaps-generate-hierarchy
Cell Maps Generate HierarchyGenerates hierarchy fromCell Maps CoembeddingusingHiDeFFree software: MIT licenseDocumentation:https://cellmaps-generate-hierarchy.readthedocs.io.Dependenciescellmaps_utilstqdmpandasnumpyndex2HiDeFNote:If running HiDeF in parallel on same machine it must be built directly fromsourceCompatibilityPython 3.8+Installationgit clone https://github.com/idekerlab/cellmaps_generate_hierarchy
cd cellmaps_generate_hierarchy
make dist
pip install dist/cellmaps_generate_hierarchy*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_generate_hierarchy
pip uninstall cellmaps_generate_hierarchy -y ; make clean dist; pip install dist/cellmaps_generate_hierarchy*whlNeeded filesThe output directory for co-embedding is required (seeCell Maps Coembedding).UsageFor information invokecellmaps_generate_hierarchycmd.py -hExample usagecellmaps_generate_hierarchycmd.py ./cellmaps_generate_hierarchy --coembedding_dirs ./cellmaps_coembedding_outdirVia DockerExample usageComing soon...CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-05-19)First release on PyPI.
Package: cellmaps-hierarchyeval
Cell Maps Hierarchy Evaluationevaluate hidef hierarchyFree software: MIT licenseDocumentation:https://cellmaps-hierarchyeval.readthedocs.io.Dependenciescellmaps_utilsstatsmodelstqdmscipynumpyndex2CompatibilityPython 3.8+Installationgit clone https://github.com/idekerlab/cellmaps_hierarchyeval
cd cellmaps_hierarchyeval
make dist
pip install dist/cellmaps_hierarchyevalcmd*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_hierarchyeval
pip uninstall cellmaps_hierarchyeval -y ; make clean dist; pip install dist/cellmaps_hierarchy*whlNeeded filesTODO:Add description of needed filesUsageFor information invokecellmaps_hierarchyevalcmd.py -hExample usageTODO:Add information about example usagecellmaps_hierarchyevalcmd.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 (2023-08-04)First release on PyPI.
Package: cellmaps-imagedownloader
Cell Maps ImmunoFluorescent Image DownloaderDownloads ImmunoFluorescent image data fromHuman Protein AtlasFree software: MIT licenseDocumentation:https://cellmaps-imagedownloader.readthedocs.io.Dependenciescellmaps_utilsrequestsmygenetqdmCompatibilityPython 3.8+Installationpip install cellmaps_imagedownloaderOr directly from source:git clone https://github.com/idekerlab/cellmaps_imagedownloader
cd cellmaps_imagedownloader
make dist
pip install dist/cellmaps_imagedownloader*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 filessamples file: CSV file with list of IF images to download (see sample samples file in examples folder)unique file: CSV file of unique samples (see sample unique file in examples folder)provenance: file containing provenance information about input files in JSON format (see sample provenance file in examples folder)UsageFor information invokecellmaps_imagedownloadercmd.py -hExample usagecellmaps_imagedownloadercmd.py ./cellmaps_imagedownloader_outdir  --samples examples/samples.csv --unique examples/unique.csv --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-image-embedding
Cell Maps ImmunoFluorescent Image EmbedderGenerate embeddings from ImmunoFluorescent image data fromHuman Protein AtlasFree software: MIT licenseDocumentation:https://cellmaps-image-embedding.readthedocs.io.Dependenciescellmaps_utilstqdmnumpypandas>=0.23.1torchtorchvisionopencv-pythonmlcratescikit-imagescikit-learn>=0.19.0PillowCompatibilityPython 3.8+Installationgit clone https://github.com/idekerlab/cellmaps_image_embedding
cd cellmaps_image_embedding
make dist
pip install dist/cellmaps_image_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_image_embedding
pip uninstall cellmaps_image_embedding -y ; make clean dist; pip install dist/cellmaps_image_embedding*whlNeeded filesThe output directory for the image downloads is required (seeCell Maps Image Downloader). Optionally, a path to the image embedding model can be provided.UsageFor information invokecellmaps_image_embeddingcmd.py -hExample usagecellmaps_image_embeddingcmd.py ./cellmaps_image_embedding_outdir --inputdir ./cellmaps_imagedownloader_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-pipeline
Cell Maps PipelineFree software: MIT licenseDocumentation:https://cellmaps-pipeline.readthedocs.io.Source code:https://github.com/idekerlab/cellmaps_pipelineDependenciescellmaps_utilscellmaps_imagedownloadercellmaps_ppidownloadercellmaps_image_embeddingcellmaps_ppi_embeddingcellmaps_coembeddingcellmaps_generate_hierarchynetworkxCompatibilityPython 3.8+Installationgit clone https://github.com/idekerlab/cellmaps_pipeline
cd cellmaps_pipeline
make dist
pip install dist/cellmaps_pipeline*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_pipeline
pip uninstall cellmaps_pipeline -y ; make clean dist; pip install dist/cellmaps_pipeline*whlNeeded filessamples file: CSV file with list of IF images to download (see sample samples file in examples folder)unique file: CSV file of unique samples (see sample unique file in examples folder)bait 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_pipelinecmd.py -hExample usagecellmaps_pipelinecmd.py ./cellmaps_pipeline_outdir --samples examples/samples.csv --unique examples/unique.csv --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 (2023-05-22)First release on PyPI.