Saravanan666/pypi-org huggingface dataset:
Package: scButterfly
Recent advancements for simultaneously profiling multi-omic modalities within individual cells have enabled the interrogation of cellular heterogeneity and molecular hierarchy. However, technical limitations lead to highly noisy multi-modal data and substantial costs. Although computational methods have been proposed to translate single-cell data across modalities, b broad applications of the methods still remain impeded b by formidable challenges. Here, we proposed scButterfly, a versatile single-cell cross-modality translation method based on dual-aligned variational autoencoders and innovative data augmentation schemes. With comprehensive experiments on multiple datasets, we provide compelling evidence of scButterfly’s superiority over baseline methods in preserving cellular heterogeneity while translating d datasets o of various contexts and i in revealing cell type-specific biological insights.Besides, we d demonstrate the extensive applications of scButterfly for integrative multi-omics analysis of single-modality data, data enhancement of poor-quality single-cell multi-omics, and automatic cell type annotation of scATAC-seq data. Additionally, scButterfly can be generalized to unpaired data training and perturbation-response analysis via our data augmentation and optimal transport strategies. Moreover, scButterfly exhibits the capability i in consecutive translation from epigenome to transcriptome to proteome and has the potential to decipher novel biomarkers.
Package: scc
SCCIntroductionThe scc command provides tools for simplifying the Git(Hub) workflow.DocumentationSetup and usage instructions can be found on theOME-contributingpages.DependenciesDirect dependencies of scc are:PyGithubInstallationTo installscc, run:$ python setup.py installor using pip, run:$ pip install sccTo upgrade your pip installation, run:$ pip install -U sccUsageThe list of available commands can be listed with:$ scc -hFor each subcommand, additional help can be queried, e.g.:$ scc merge -hContributingPyGithub followsPEP 8, the Style Guide for Python Code. Please check your
code withpep8orflake8, the Python style guide checkers, by runningflake8-v.orpep8-v..Running testsThe tests are located under thetestdirectory. To run all the tests, use
thetesttarget ofsetup.py:python setup.py testUnit testsUnit tests are stored under thetest/unitfolder and can be run by calling:python setup.py test -t test/unitUnit tests are also run by theTravisbuild on every Pull Request opened
against the main repository.Integration testsIntegration tests are stored undertest/integration. Many integration tests
usesnoopys-sandboxandsnoopys-sandbox-2as sandbox repositories to test the
scc commands.Running the integration test suite requires:a GitHub accounta token-based GitHub connection, i.e. agithub.tokenstored under
the local Git configuration file (a global token is ignored):$ git config github.token xxxxthe user authenticated by the token defined above needs to own forks ofsnoopys-sandboxandsnoopys-sandbox-2Once this is set up, the integration tests can be run by calling:python setupy.py test -s test/integrationLicensescc is released under the GPL.Copyright2012-2019, The Open Microscopy Environment
Package: scCAMEL
scCAMEL: single cell Cross- Annotation and Multimodal Estimation on Lineage trajectoryLicense: GPL version 3https://pypi.org/project/scCAMEL/Developed by: Yizhou Hu, Patrik Ernfors lab, MBB, Karolinska InstitutetTutorials and other informations in :https://sccamel.readthedocs.io/
Package: scc-bin
pybinThis project was inspired by howMaturin packages rust binaries.  The key observation is that in the wheel format, thedistribution-1.0.data/scripts/ directory is copied to bin, which means we can leverage this to seamlessly copy binaries into a user's PATH.Combined with Python's platform-specific wheels, this allows us to use pip as a "cross-platform package manager" for distributing single-binary CLI applications.Wheel Building ProcessThe core of the logic lies in thebuildlib.pymodule.A mapping from download URL (often github releases) to pypi platform tag is required.For each platform:Download the release & extract the binary.Set file permissions and place into scripts directory within wheel archive.Create the METADATA and WHEEL files within wheel archive.Place wheel intodist/folder.Once all wheels are constructed, the distribution is uploaded to PyPI.CICD is configured to automatically recognize newPyPIreleases by looking for a diff on the PYPI_VERSION.  When this happens, a build-and-release cycle is performed for that release version.Catalogpip install dive-bin:pybin version,upstream sourcepip install just-bin:pybin version,upstream sourcepip install lazydocker-bin:pybin version,upstream sourcepip install scc-bin:pybin version,upstream source
Package: sc-cc-ng-models-python
SC-CC-NG Python Data ModelsData models for the SC-CC-NG project.Installationpipinstallsc-cc-ng-data-modelsUsagefromsc_cc_ng_data_modelsimport*# BitVal's are holding values and a contextbv=BitVal(context=["MODEL","ENGINE","HORSEPOWER",],value=50.0,)# ContextType's enum are known context types for easier usagect=ContextType.MODELThe context data type is a list of strings and not a list ofContextTypebecause of
they should be able to carry any context information.
Package: sc-cc-ng-sdk-python
Examplefromsc_cc_ng_sdk_pythonimportSCNGClientfromsc_cc_ng_models_pythonimportContextFilter,ContextRelation,SubContextFilter,ContextTypesdk_call=SCNGClient(url="http://localhost:8001/graphql")tokens_list=[["en-GB","MY2022","CT256","EN06"]]result=sdk_call(token_list=tokens_list,context_filter=ContextFilter(relation=ContextRelation.ALL,subContextFilters=[SubContextFilter(contexts=[ContextType.MODEL,],relation=ContextRelation.ALL)]))print(result.data)print(result.error)
Package: scce
SEEInstallationconda create -n scce python=3.8 libffi=3.3
pip install sccePaper ContentGet the codegit clone https://github.com/LMH0066/SEE.git --depth=1Prepare the environmentThe see environment can be installed via conda:conda env create -f environment.ymlDirectory structure.
|-- script                            # Obtain training data through public data
|-- train                             # Main code for training model
|-- analyse                           # Experiments
|   |-- 3DMax                         # 
|   |-- AD                            # Case analysis of Alzheimer's disease
|   |-- Data Analysis(PDGFRA).ipynb   # Case study of raw data
|   |-- analyse_util.py               # Some common functions used in the analysis process
|   |-- bulk                          # Case analysis of bulk RNA
|   |-- loss-effectiveness            # FocalLoss effectiveness
|   |-- quality                       # Method evaluation
|   |-- related-genes                 # Importance analysis of input features
|   \`-- velocity                     # Case analysis of pseudo-time
|-- environment.yml
\`-- README.mdTraintrainCUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 train_model.py -t /folder/to/train_file -e /folder/to/eval_file -o /folder/to/output_folder -g gene_namevalidatepython validate.py -e /folder/to/eval_file -m /path/to/model -g gene_name -o /folder/to/output_file -s output_sizeAnalyseAll the analysis results in the paper can be found in the code under the 'analyse' folder.
Package: scCellFie
Metabolic functionalities of mammalian cells from single-cell and spatial transcriptomicsAbout scCellFieSingle-cell CellFie is a Python implementation ofCellFie, a tool for studying metabolic tasks
originally developed in MATLAB by theLewis Lab. This version is designed to be
compatible with single-cell and spatial data analysis using Scanpy.InstallationTo install scCellFie, use pip:pip install sccellfieFeaturesSingle cell and spatial data analysis:Tailored for analysis of metabolic
tasks using fully single cell resolution and in space.Speed:This implementation further leverages the original CellFie. It is now memory
efficient and run much faster! A dataset of ~70k single cells can be analyzed in ~5 min.New analyses:From marker selection of relevant metabolic tasks to integration with
inference of cell-cell communication.User-friendly:Python-based for easier use and integration into existing workflows.Scanpy compatibility:Fully integrated with Scanpy, the popular single cell
analysis toolkit.AcknowledgmentsThis implementation is inspired by the originalCellFie tooldeveloped by
theLewis Lab. Please consider citing their work if you find this tool useful:Model-based assessment of mammalian cell metabolic functionalities using omics data.Cell Reports Methods, 2021.https://doi.org/10.1016/j.crmeth.2021.100040ImmCellFie: A user-friendly web-based platform to infer metabolic function from omics data.STAR Protocols, 2023.https://doi.org/10.1016/j.xpro.2023.102069Inferring secretory and metabolic pathway activity from omic data with secCellFie.Metabolic Engineering, 2024.https://doi.org/10.1016/j.ymben.2023.12.006
Package: sccircuitbuilder
Superconducting Circuit BuilderThis is a GUI for designing superconducting circuits in the lumped-element limit intended to accompany the Python package scQubits.Installationpip install sccircuitbuilderUsageRunimport sccircuitbuilder as sc, thensc.GUI(). The elements used to build a circuit areCapacitorInductorJosephson JunctionDrag the icons from the toolbox onto the canvas to place down an element. Drag nodes together to connect them. Left-click on wires to add anchor points and drag
the anchor points to guide the wires. Export the circuit to scqubits to diagonalize and simulate the circuit!KeyboardShift + Right Click- add to selectionShift + Right Drag- add box to selectionEnteron property entry box - change propertyMouseLeft Button Dragon whitespace - panScroll Wheel- zoomLeft Button Dragon element/selected group - move selectionRight Button Drag- box selectionLeft Button Clickon element - rotateRight Button Clickon element - select and show propertiesFeaturesSplit (reverse-join) nodesDelete elementsWire anchor pointsUndo / RedoSave / OpenExport to scQubits circuit formatPre-made circuit libraryFuture plansOffset charge / external fluxFurther integration with scqubitsAccompanying tutorialsKnown issuesRight click has no activation distance, so using a mouse will result in some dropped right clicksSometime import/undo/redo (the memento-based functionalities) crash, but usually they seem to workFile save is not very inteligent and adds.circuitto the end of the filename regardless of whether it is already there
Package: scClassifier2
scClassifier2Deep bag-of-genes model for single cell classificationCitationBin Zhou, Fan Yang, Ting Chen, Feng Zeng. Seeing cells as bags-of-genes: a simple deep generative model for platform-independent single cell classification. Submission. 2021InstallationInstallpytorchaccording to your computational platformInstall dependencies:pip3 install numpy scipy pandas scikit-learn pyro-ppl matplotlibPrepare matrix and label filesscClassifier2 accepts as input the log-transformed gene matrix in the MatrixMarket format usually end in ".mtx", where rows are cells and columns are genes.The label file can be either the CSV format or the TSV format, one label per line.Datagives some examples of matrix and label files.TutorialPlease refer to thePBMC68kexample.Usageusage: scClassifier    [-h] [--cuda] [--jit] [-n NUM_EPOCHS] [--aux-loss] [-alm AUX_LOSS_MULTIPLIER] [-enum ENUM_DISCRETE]
                        [--sup-data-file SUP_DATA_FILE] [--sup-label-file SUP_LABEL_FILE] [--unsup-data-file UNSUP_DATA_FILE]
                        [--unsup-label-file UNSUP_LABEL_FILE] [-64] [-lt] [--cross-validation-fold CROSS_VALIDATION_FOLD] [-zd Z_DIM]
                        [-hl HIDDEN_LAYERS [HIDDEN_LAYERS ...]] [-lr LEARNING_RATE] [-dr DECAY_RATE] [-de DECAY_EPOCHS] [-b1 BETA_1]
                        [-bs BATCH_SIZE] [-rt] [-log LOGFILE] [--seed SEED] [--best-valid-model BEST_VALID_MODEL] [--best-aux-model BEST_AUX_MODEL]
Package: scclient
scclientAbout this libraryscclient is a pythonSocketClusterclient implementation. It aims to be a level 4 implementation, according to theclient guidelines. The protocol is at least partially documentedhereand any missing pieces were figured out by analyzing theexisting python library.GoalsComprehensive unit testingPEP 8styleFull SocketCluster client implementationNon-blocking event driven architecture
Package: scCODA
scCODA - Single-cell differential composition analysisscCODA allows for identification of compositional changes in high-throughput sequencing count data, especially cell compositions from scRNA-seq.
It also provides a framework for integration of cell-type annotated data directly fromscanpyand other sources.
Aside from the scCODA model (Büttner, Ostneret al(2021)), the package also allows the easy application of other differential testing methods.The statistical methodology and benchmarking performance are described in:Büttner, Ostneret al(2021).scCODA is A Bayesian model for compositional single-cell data analysis(Nature Communications)Code for reproducing the analysis from the paper is availablehere.For further information on the scCODA package and model, please refer to thedocumentationand thetutorials.InstallationRunning the package requires a working Python environment (>=3.8).This package uses thetensorflow(>=2.8) andtensorflow-probability(>=0.16) packages.
The GPU computation features of these packages have not been tested with scCODA and are thus not recommended.To install scCODA via pip, call:pip install sccodaTo install scCODA from source:Navigate to the directory that you want to install scCODA inClone the repository from Github (https://github.com/theislab/scCODA):git clone https://github.com/theislab/scCODANavigate to the root directory of scCODA:cd scCODAInstall dependencies::pip install -r requirements.txtInstall the package:python setup.py installDocker container:We provide a Docker container image for scCODA (https://hub.docker.com/repository/docker/wollmilchsau/scanpy_sccoda).UsageImport scCODA in a Python session via:import sccodaTutorialsscCODA provides a number of tutorials for various purposes. Please also visit thedocumentationfor further information on the statistical model, data structure and API.The"getting started" tutorialprovides a quick-start guide for using scCODA.In theadvanced tutorial, options for model specification, diagnostics, and result interpretation are disccussed.Thedata import and visualization tutorialfocuses on loading data from different sources and visualizing their characteristics.Thetutorial on other methodsexplains how to apply other methods for differential abundance testing from within scCODA.
Package: sc-config
A simple Python configuration file operatorThis project provides a common way to read configurations usingconfig42.InstallationIt is possible to install the tool withpip:pip install sc-configConfigurationConfiguration files reading in this order, the first is the top most priority:
1. production.xml in current directory,
2. production.xml in <project_name> directory under User directory,
3. production.xml in <project_name> directory under /var/opt/sc/ directory,
4. default.xml in <project_name> directory under /var/opt/sc/ directory.Dependenciesconfig42>= 0.4.5LicenseThe script is released under theMIT License.
The MIT License is registered with and approved by theOpen Source Initiative.
Package: scconfluence
scConfluencescConfluence is a novel method for the integration of unpaired multiomics data combining
uncoupled autoencoders and Inverse Optimal Transport to learn low-dimensional cell
embeddings. These embeddings can then be used for visualization and clustering, useful
for discovering  subpopulations of cells, and for imputation of features across
modalities.
Read the preprint! (!!!!add link!!!!)Install the packagescConfluence is implemented as a Python package seamlessly integrated within the scverse
ecosystem, in particular Muon and Scanpy.via PyPI (!!!!!!!!! not yet available!!!!!!!)On all operating systems, the easiest way to install scConfluence is via PyPI.
Installation should typically take a minute and is continuously tested with Python 3.10
on an Ubuntu virtual machine.pipinstallscconfluencevia GitHub (development version)gitclonegit@github.com:cantinilab/scconfluence.gitcdscconfluence
pipinstall.Test your development installation (optional)pipinstallpytest
pytest.Getting startedscConfluence takes as an input a MuData object and populates itsobsmfield
with the latent embeddings. Visit (read the docs page currently under preparation) for
more documentation and tutorials (which can be found now in the tutorials folder of
this repository).You may download a preprocessed 10X Multiome demo datasethere.A GPU is not required for the method to run, but is strongly recommended.importscconfluenceimportmudataasmdimportscanpyasscfromscipy.spatial.distanceimportcdist# Load data into a Muon object.mdata=md.read_h5mu("my_data.h5mu")# Compute the cross-modality distance matrix using connected featuresmdata.uns["cross_modality1+modality2"]=cdist(mdata["modality1"].obsm["cm_features"],mdata["modality2"].obsm["cm_features"])mdata.uns["cross_keys"]=["cross_modality1+modality2"]# Initialize and train the model.autoencoders={"modality1":scconfluence.model.AutoEncoder(mdata["modality1"],modality="modality1"),"modality2":scconfluence.model.AutoEncoder(mdata["modality2"],modality="modality2")}model=scconfluence.model.ScConfluence(mdata,unimodal_aes=autoencoders)model.fit(save_path="results")mdata.obsm["latent"]=model.get_latent().loc[mdata.obs_names]# Visualize the embedding with UMAP.sc.pp.neighbors(mdata,use_rep="latent")sc.tl.umap(mdata)sc.pl.umap(mdata)Our preprint!!!! add citation !!!!If you're looking for the repository with code to reproduce the experiments in our
preprint,here it is!
Package: sc.contentrules.group
Table of Contentssc.contentrules.groupOverviewUse caseRequirementsInstallationSponsoringCreditsChangelogsc.contentrules.groupOverviewsc.contentrules.groupis a content rules action that creates a new
group in a Plone site when executed.Use caseIn a portal you have an area for Products. This area,/productswill
hold folders for each product available.Everytime a new folder (product) is added in this area we want a new group
of users to be created.This group can be used to give local roles to its users in this context.RequirementsPlone 3.3.x and above (http://plone.org/products/plone)InstallationTo enable this product,on a buildout based installation:Edit your buildout.cfg and addsc.contentrules.groupto the list of eggs to install[buildout]
...
eggs =
    sc.contentrules.groupAfter updating the configuration you need to run the ‘’bin/buildout’’,
which will take care of updating your system.SponsoringDevelopment of this product was sponsored by:Simples Consultoria.CreditsErico Andrei (erico at simplesconsultoria dot com dot br)Changelog0.5 (2011-09-15)Spanish translation [hvelarde]Initial release [erico_andrei]
Package: sc.contentrules.layout
ContentOverviewUse caseActionsSet layoutInstallationRequirementsMostly HarmlessContributorsChangelog1.0.1 (unreleased)1.0 (2013-04-13)1.0b2 (2012-11-14)1.0b1 (2012-11-14)OverviewContent Rules: Set Layout(sc.contentrules.layout) package provides
a content rule action to set a layout or default view for a content item.Use caseSome Plone content types allows the user to select one of many available layouts
to be used as default view. A good example is the Folder content type which
provides options like Folder Summary View and Tabular View.It is possible to change the default view for a content type but it will affect
the entire portal, so if an editor wants to, in an area of the portal, to change
the default view of newly created Folder content items to Folder Summary View,
he will need to do it manually.This package provides an action,Set Layoutthat allows content managers
, using content rules, to selectively apply layouts to content items.ActionsThis package provides a content rule action to set the layout (default view) for
a content object.Set layoutUsed to create a new user group this action have three options:LayoutA layout to be applied to the content item that triggered the content rule.
This action will inspect the content rule conditions and look for aContent Type conditionto select the available layouts. If no condition
is found the only available value in here will be Default View. Also, if
there is aContent Type conditionbut two or more content types are
selected, this action will provide you with an intersection of available
views for all selected content typesInstallationInclude this package in eggs and zcml section of your buildout. For further reference please refer to theofficial guide.RequirementsPlone 4.2.x and above (http://plone.org/products/plone)Mostly HarmlessHave an idea? Found a bug? Let us know byopening a support ticket.ContributorsÉrico Andrei [ericof]Hector Velarde [hvelarde]Avianca (for the space between seats)You can find an updated list of package contributors onGitHub.Development sponsored by Simples Consultoria.Changelog1.0.1 (unreleased)Add translations to plone domain.
[ericof]1.0 (2013-04-13)Support Plone 4.3 [ericof]Remove dependency on zope.app.publisher. [hvelarde]1.0b2 (2012-11-14)Fix packaging [ericof]1.0b1 (2012-11-14)Initial release [ericof]
Package: sc.contentrules.metadata
ContentLife, the Universe, and EverythingDon’t PanicActionsConditionsInstallationRequirementsMostly HarmlessContributorsChangelog1.0.1 (unreleased)1.0 (2013-04-13)1.0b2 (2013-01-15)1.0b1 (2013-01-14)Life, the Universe, and EverythingContent Rules: Metadata Conditions and Actions(sc.contentrules.metadata)
package provides a set of content rule conditions and actions to deal with
content item metadata.Don’t PanicActionsThis package provides the following content rules actions:Set TagsUsed to set Tags (Keywords, Subject) on a content item. This action relies
on a vocabulary of already used Tags so the user can select the ones to be
applied on the content item.Exclude from navigationExclude – or show – a content item from navigation.Set LanguageContent rule action to set the Language metadata field on a content item.Set RightsContent rule action to set the Rights metadata field on a content item. i.e.:
“Copyleft 2013 - Radio del Sur”Set CreatorsContent rule action to set the Creators metadata field on a content item. i.e.:
“Lawrence Lessig”Set ContributorsContent rule action to set the Contributors metadata field on a content item.
i.e.:  “et al.”ConditionsThis package provides the following content rules conditions:TagContent rule condition that will apply only when the current content item
contains the selected Tags.InstallationInclude this package in eggs and zcml section of your buildout. For further reference please refer to theofficial guide.RequirementsPlone 4.2.x and above (http://plone.org/products/plone)Mostly HarmlessGot an idea? Found a bug? Let us know byopening a support ticket.ContributorsÉrico Andrei [ericof] – Initial ideaHéctor Velarde [hvelarde]You can find an updated list of package contributors onGitHub.Development sponsored by OpenMultimedia and Simples Consultoria.Changelog1.0.1 (unreleased)Add translations to plone domain.
[ericof]Add Brazilian Portuguese translation.
[ericof]1.0 (2013-04-13)Support Plone 4.3 [ericof]1.0b2 (2013-01-15)Test compatibility with Plone 4.3. [hvelarde]Fix package distribution. [hvelarde]1.0b1 (2013-01-14)Initial release
[ericof]