Saravanan666/pypi-org huggingface dataset:
Package: cellanova
CellANOVA: Cell State Space Analysis of VarianceIntroductionInstallationTutorialsLicenseIntroductionCellANOVA is a Python package, for batch correction with signal recovery. It contructs a pool of control samples to estimate a latent linear space that captures cell- and gene-specific unwanted variations, which can then be used to remove batch effects from cells across all samples.  By using only samples in the control pool in the estimation of the batch variation space, CellANOVA preserves any biological differences in the non-control samples that lie outside this space.  Importantly, CellANOVA produces a batch corrected gene expression matrix which can be used for gene- and pathway-level downstream analyses, and is fast and scalable to data sets containing millions of cells.CellANOVA can be applied to multiple settings:Case-control designLongitudinal designIrregular block designFor more model details, validation results and real dataset analysis, please check out our paper (to add link). If you use our method, please use the following citation:InstallationDependenciesOur Python package has been tested on python=3.7, 3.8, 3.9. It depends on numpy>=1.20.3, scipy>=1.7.1, pandas>=1.3.2, scikit-learn>=1.0.2, anndata>=0.7.6, scanpy>=1.8.1, harmonypy>=0.0.6.If you use conda environment, you can use the following command for an easy setup. It will build a seperate cellanova environment, and have all dependencies installed.condaenvcreate-fenvironment.ymlDownloadTutorialsQuick StartThe following is an quick example showing CellANOVA integration pipeline.## load required packageimportanndataasadimportscanpyasscfromcellanovaimport*## load and preprocess dataadata=sc.read_h5ad('raw_data.h5ad')adata_prep=preprocess_data(adata,integrate_key='dataidx')## construct control poolcontrol_dict={'pool1':list(set(adata_prep[adata_prep.obs['condition']=='control',].obs['dataidx'])),}## model fittingadata_prep=calc_ME(adata_prep,integrate_key='dataidx')adata_prep=calc_BE(adata_prep,integrate_key,control_dict)adata_prep=calc_TE(adata_prep,integrate_key)## create an independent anndata object for cellanova-integrated dataintegrated=ad.AnnData(adata_prep.layers['denoised'],dtype=np.float32)integrated.obs=adata_prep.obs.copy()integrated.var_names=adata_prep.var_namesExample NotebooksFor more detailed examples, please refer to the following jupyter notebooks:CellANOVA integration workfolow (link)Evaluation of batch removal performance (link)Evaluation of distortion (link)Evaluation of signal preservation (link)License
Package: cellarpy
![Cellar powered by Bottle](https://raw.githubusercontent.com/JonathanHuot/cellarpy/master/doc/cellar.jpg)[![CircleCI](https://circleci.com/gh/JonathanHuot/cellarpy.svg?style=svg)](https://circleci.com/gh/JonathanHuot/cellarpy)[![Coverage Status](https://coveralls.io/repos/github/JonathanHuot/cellarpy/badge.svg?branch=master)](https://coveralls.io/github/JonathanHuot/cellarpy?branch=master)[![PyPI](https://img.shields.io/pypi/v/cellarpy.svg)](https://pypi.python.org/pypi/cellarpy)CellarPy: Enhance Bottle Web framework===============empower python web-applications based on bottlepy
Package: cellartracker
CellarTrackerPython package to export CellarTracker data.Free software: MIT licenseDocumentation:https://cellartracker.readthedocs.io.FeaturesUse theCellarTracker export datacapabilities.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-05-25)First release on PyPI.
Package: cellbell
Bell magic for IPython. Rings a bell when cell evaluation (IPython shell or
Jupyter notebook) is complete.pip install cellbellUse as cell or line magic:# Import
import cellbell

# line magic
%ding my_long_function()

# cell magic
%%ding
time.sleep(4)
my_long_function()
time.sleep(2)
print("done")Can also be used directly by callingding:# Import
import cellbell

cellbell.ding()Inspired byipython-bell, but
works on all systems by playing a wav.
Package: cellbender
CellBender is a software package for eliminating technical artifacts from
high-throughput single-cell RNA sequencing (scRNA-seq) data.The current release contains the following modules. More modules will be added in the future:remove-background:This module removes counts due to ambient RNA molecules and random barcode swapping from (raw)
UMI-based scRNA-seq count matrices.  Also works for snRNA-seq and CITE-seq.Please refer tothe documentationfor a quick start tutorial.Installation and UsageCellBender can be installed via$pipinstallcellbender(and we recommend installing in its owncondaenvironment to prevent
conflicts with other software).CellBender is run as a command-line tool, as in(cellbender)$cellbenderremove-background\--cuda\--inputmy_raw_count_matrix_file.h5\--outputmy_cellbender_output_file.h5Seethe usage documentationfor details.Using The Official Docker ImageA GPU-enabled docker image is available from the Google Container Registry (GCR) as:us.gcr.io/broad-dsde-methods/cellbender:latestAvailable image tags track release tags in GitHub, and includelatest,0.1.0,0.2.0,0.2.1,0.2.2, and0.3.0.WDL UsersA workflow written in theworkflow description language (WDL)is available for CellBender remove-background.ForTerrausers, a workflow calledcellbender/remove-backgroundisavailable from the Broad Methods repository.There is also aversion available on Dockstore.Advanced installationFrom source for developmentCreate a conda environment and activate it:$condacreate-ncellbenderpython=3.7$condaactivatecellbenderInstall thepytablesmodule:(cellbender)$condainstall-canacondapytablesInstallpytorchviathese instructions, for example:(cellbender)$pipinstalltorchand ensure that your installation is appropriate for your hardware (i.e. that
the relevant CUDA drivers get installed and thattorch.cuda.is_available()returnsTrueif you have a GPU available.Clone this repository and install CellBender (in editable-emode):(cellbender)$gitclonehttps://github.com/broadinstitute/CellBender.git(cellbender)$pipinstall-eCellBenderFrom a specific commitThis can be achieved via(cellbender)$pipinstall--no-cache-dir-Ugit+https://github.com/broadinstitute/CellBender.git@<SHA>where<SHA>must be replaced by any reference to a particular git commit,
such as a tag, a branch name, or a commit sha.Citing CellBenderIf you use CellBender in your research (and we hope you will), please consider
citing our paper in Nature Methods:Stephen J Fleming, Mark D Chaffin, Alessandro Arduini, Amer-Denis Akkad,
Eric Banks, John C Marioni, Anthony A Phillipakis, Patrick T Ellinor,
and Mehrtash Babadi. Unsupervised removal of systematic background noise from
droplet-based single-cell experiments using CellBender.Nature Methods, 2023.https://doi.org/10.1038/s41592-023-01943-7See alsoour preprint on bioRxiv.
Package: cell-bin
A framework for generating single-cell gene expression dataRelease NotesInstallationBoth on Windows or Linux (CentOS, Ubuntu), justpipinstall-rrequirements.txtPyvips linux installation# linux environ install pyvips using condacondainstall--channelconda-forgepyvipsPyvips windows install vips fromvipspipinstall--userpyvips==2.2.1# add vips path when running python scriptimportosvipsbin=r'c:\vips-dev-8.13\bin'os.environ['PATH']=vipsbin+';'+os.environ['PATH']Develop modegitclonehttps://gitlab.genomics.cn/biointelligence/implab/stero-rnd/cellbin/algorithms/cellbin.git
condaactivateenv# activate your envpythonsetup.pydevelop--no-deps# develop mode, if you already have deps installedUsageAPI docHow to get weights?contributeWe love your input! We want to make contributing to cellbin as easy and transparent as possible. Please see,When you need to change other people's modules, remember to note the change information in the change location. like,a = 0  #  parameter value to zero [by jack]Thank you to all our contributors!ContactFor cellbin bugs and feature requests please visit
Package: cellbrowser
The UCSC Cell Browser is an interactive browser for
single cell data, like mRNA or ATAC-seq data. You can display
dimensionality reductions, navigate them with the mouse or the cursor keys,
select cells, color by genes or meta annotations and make many other
changes. The main site runs athttps://cells.ucsc.edu, but using this
package you can also convert data yourself and build a Cell Browser HTML
directory that can be served through any University or any other webserver
for static webpages. You can try the Cell Browser athttps://cells.ucsc.eduor read about how to convert data with this package onhttps://cellbrowser.rtfd.org.We strongly recommend that to use Python3, but we make an effort to remain compatible with Python2.
Package: cellcharter
A Python package for the identification, characterization and comparison of spatial clusters from spatial -omics data.Documentation•Examples•PaperBackgroundSpatial clustering determines cellular niches characterized by specific admixing of these populations. It assigns cells to clusters based on both their intrinsic features (e.g., protein or mRNA expression), and the features of neighboring cells in the tissue.CellCharter is able to automatically identify spatial clusters, and offers a suite of approaches for cluster characterization and comparison.Getting startedPlease refer to thedocumentation. In particular, theAPI documentation.TutorialsInstallationCellCharter uses Python < 3.11 andPyTorch<= 1.12.1. If you are planning to use a GPU, make sure to download and install the correct version of PyTorch first.In CellCharter, only the dimensionality reduction and batch correction step is dependent on the data type. In particular, it uses:scVIfor spatial transcriptomics data such as 10x Visium and Xenium, Nanostring CosMx, Vizgen MERSCOPE, Stereo-seq, DBiT-seq, MERFISH and seqFISH data.A modified version ofscArches's TRVAE model for spatial proteomics data such as Akoya CODEX, Lunaphore COMET, CyCIF, IMC and MIBI-TOF data.By installing CellCharter without specifying the type of data, as in the following code, it will install without any of the two models.pipinstallcellcharterHowever, you can include in the installation the type of data (transcriptomics and/or proteomics) you are planning to analyze, and it will install the required dependencies.pipinstallcellcharter[transcriptomics]ContributionIf you found a bug or you want to propose a new feature, please use theissue tracker.
Package: cellcount
Cell CountThis package takes a binary array, and counts the number
of seperate objects in the array.InstallationRun the following to install:pipinstallcellcountUsageimportcellcountasccimportnumpyasnpimg=np.random.randint(2,size=(5,5))# Count the number of seperate cellsnum_of_cells=cc.countCells(img)
Package: celldancer
cellDancer - Estimating Cell-dependent RNA VelocitycellDanceris a modularized, parallelized, and scalable tool based on a deep learning framework for the RNA velocity analysis of scRNA-seq. Our website of tutorials is available atcellDancer Website.cellDancer’s key applicationsEstimate cell-specific RNA velocity for each gene.Derive cell fates in embedding space.Estimate pseudotime for each cell in embedding space.What’s newcellDancer is updated to v1.1.7Added progress bar for adata_to_df_with_embed() and adata_to_raw().Added try except to catch genes with low quality in velocity().InstallationcellDancer requires Python version >= 3.7.6 to run.To run cellDancer locally, create ancondaorAnacondaenvironment asconda create-ncellDancerpython==3.7.6, and activate the new environment withconda activate cellDancer. cellDancer could be installed withpip install celldancer.To install cellDancer from source code, run:pip install 'your_path/Source Code/cellDancer'.For M1 Mac users if you encountered a problem while installing bezier. Please refer to the following link:https://bezier.readthedocs.io/en/2021.2.12/#installingIf any other dependency could not be installed withpip install celldancer, trypip install--no-depscelldancer. Then install the dependencies bypip install-rrequirements.txt.
Package: cell-data-loader
Cell Data LoaderCell Data Loader is a simple AI support tool in Python that can take in images of cells (or other image types) and output them with minimal effort to formats that can be read by Pytorch (Tensor) or Tensorflow (Numpy) format. With Cell Data Loader, users have the option to output their cell images as whole images, sliced images, or, with the support ofCellPose, segment their images by cell and output those individually.To install Cell Data Loader, simply type into a standard UNIX terminalpip install cell-data-loaderThe simplest way to use Cell Data Loader is to instantiate a dataloader as such:fromcell_data_loaderimportCellDataloaderimfolder='/path/to/my/images'dataloader=CellDataloader(imfolder)forimageindataloader:...And viola!Lists of files are also supported:imfiles=['/path/to/image1.png','/path/to/image2.png','/path/to/image3.png']dataloader=CellDataloader(imfiles)forimageindataloader:...LabelsCell Data Loader has a few ways to support image labels. The simplest is whole images that are located in different folders, with each folder representing a label. This can be supported via the following:imfolder1='/path/to/my/images'imfolder2='/path/to/your/images'dataloader=CellDataloader(imfolder1,imfolder2)forlabel,imageindataloader:...Alternatively, if you have one folder or file list with images that have different naming conventions, a regex match is supported:imfiles=['/path/to/CANCER_image1.png','/path/to/CANCER_image2.png','/path/to/CANCER_image3.png','/path/to/HEALTHY_image1.png','/path/to/HEALTHY_image2.png','/path/to/HEALTHY_image3.png']dataloader=CellDataloader(imfiles,label_regex=["CANCER","HEALTHY"])forlabel,imageindataloader:...ArgumentsAdditional arguments taken by Cell Data Loader includeimfolder='/path/to/folder'dataloader=CellDataloader(imfolder,dim=(64,64),batch_size=32,dtype="numpy",# Can also be "torch"label_regex=None,n_channels=3,# This is detected in the first read image by default, if not provided; it re-samples all images to force this number of channelsmatch_labels=False,# Outputs proportional amounts of each label in the dataset)DependenciesStrict dependencies:numpy
torch
torchvision
opencv-python>=4.5.4
slideio==2.4.1
scipy
scikit-image
pillowSoft dependencies:CellPose # For cell segmentation support
TensorflowNote that some of the dependencies are not strict and vary depending on usage. Numpy is a hard requirement, but Tensorflow is not if the user only uses the Torch capabilities. If the user attempts to load cell images in "cell" mode without a working Cellpose installation, CellDataLoader will throw an error. Cellpose needs be be installed separately to use "cell" mode:pip install cellposeAnd GPU integration is a separate matter.
Package: celldict
CellDict基于Pickle的高效便捷变量保存读取!简洁快速的保存和读取变量, 方便的版本控制.安装pip install -U celldict简介celldict由CellDict和CellTable两个类构成.CellDict是文件型的 Key-value 数据结构, 支持版本控制, 支持多线程读写并发, 支持多进程读写与只读模式.CellTable是文件型的表数据结构, 支持不规整数据, 支持修改删除操作记录, 支持多线程读写并发, 支持多进程读写与只读模式.CellDictfrom celldict import CellDict

# 数据集名称为 "dataname", 修改记录保存三次
cdict = CellDict("dataname", version_record=3, root_path=".CellDict")

# 保存数据
cdict.set("key", "value")
cdict.set("Hello", "world")
cdict["test"] = "any data"

# 读取数据
cdict.get("key")
cdict["test"]

# 删除数据 成功返回 True
cdict.delete("test")
del cdict["test"]

# 获取 keys
cdict.keys()

# 多版本控制
# version_record=3 修改记录保存三次
cdict.set("test", 1) # 最旧版本 被覆盖
cdict.set("test", 2) # 覆盖后的最旧记录
cdict.set("test", 3)
cdict.set("test", 4) # 最新版本记录

# 读取最新记录 4
cdict.get("test", "last")
# 读取最旧记录 2
cdict.get("test", "former")

# 按索引读取
cdict.get("test", 0) # 4
cdict.get("test", 1) # 3
cdict.get("test", 2) # 2
cdict.get("test", -1) # 2
cdict.get("test", -2) # 3
cdict.get("test", -3) # 4

# 读取所有版本数据
cdict.getall("test")CellTablefrom celldict import CellTable

ctable = CellTable("dataname", root_path=".CellTable")

# 增加一条记录, 返回保存的主键
ctable.add("Hello World!") # 0
ctable.add([1, 2, 3, 4]) # 1
..
ctable.add("any data") # n

# 读取对应键值的数据
ctable.get(0) # "Hello World!"
ctable[1] # [1, 2, 3, 4]
ctable[2] # "any data"

# 读取指定键值的数据返回列表
ctable[0, 2] # ["Hello World!", "any data"]

# 通过切片获取值, 索引为键值
ctable[1:] # [[1, 2, 3, 4], 'any data']

# 通过时间取值
from datetime import datetime
# 获取 datetime 日期内插入的数据, 时分秒为零
key = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
ctable[key] # ['Hello World!', [1, 2, 3, 4], 'any data']
# 获取时间间隔内插入的数据
start = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
end = datetime.now().replace(hour=10, minute=0, second=0, microsecond=0)
ctable[start : end]

# 修改值, 修改记录保存在 modify 文件夹中
ctable.set(2, "modify data")
ctable[2] = "modify data"

# 删除值, 删除记录保存在 delete 文件夹中
ctable.delete(2)
del ctable[2]CellTable优势在于对数据结构没有限制, 可以方便的拓展和去除数据, 且不需要写sql语句, 效率也比 sqlite 高, 使用起来也很方便.
Package: cellenics-api
cellenics-apiAboutThis python package provides an easy way to create projects and upload samples into Cellenics.InstallationTo install the package execute the following line:pip install cellenics_apiUsageIn order to use the package you first need to create an account in Cellenics (https://scp.biomage.net/) if you don't have one yet.Then the package is used in the following way:importcellenics_api# 1. Authenticate user and create a connection tunnel with the apiconnection=cellenics_api.Connection('email','password')# 2. Create an experimentexperiment_id=connection.create_experiment()# 3. Upload samples associated with the experimentconnection.upload_samples(experiment_id,'local/path/to/samples')Once the upload is complete you can navigate toCellenicsand process your project there.TroubleshootingMax retries exceeded with url: / (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2396)')))Navigate to your project inCellenicsand manually delete the failed sampleRun step #3 againThis will be fixed when error handling is introduced
Package: cell-executed-alert
cell_executed_alertA JupyterLab extension.RequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstallcell_executed_alertUninstallTo remove the extension, execute:pipuninstallcell_executed_alertContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the cell_executed_alert directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstallcell_executed_alertIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedcell-executed-alertwithin that folder.Packaging the extensionSeeRELEASE
Package: cellfinder
cellfindercellfinder is software for automated 3D cell detection in very large 3D images (e.g., serial two-photon or lightsheet volumes of whole mouse brains).
There are three different ways to interact and use it, each with different user interfaces and objectives in mind.
For more details, head over tothe documentation website.At a glance:There is a command-line interface calledbrainmapperthat integrateswithbrainregfor automated cell detection and classification. You can install it throughbrainglobe-workflows.There is anapari pluginfor interacting graphically with the cellfinder tool.There is aPython APIto allow users to integrate BrainGlobe tools into their custom workflows.InstallationYou can findthe installation instructionson the BrainGlobe website, which will go into more detail about the installation process if you want to minimise your installation to suit your needs.
However, we recommend that users installcellfindereither through installing BrainGlobe version 1, or (if you also want the command-line interface) installingbrainglobe-workflows.# If you want to install all BrainGlobe tools, including cellfinder, in a consistent manner with one command:pipinstallbrainglobe>=1.0.0# If you want to install the brainmapper CLI tool as well:pipinstallbrainglobe-workflows>=1.0.0If you only want thecellfinderpackage by itself, you canpip installit alone:pipinstallcellfinder>=1.0.0Be sure to specify a version greater than versionv1.0.0- prior to this version thecellfinderpackage had a very different structure that is incompatible with BrainGlobe version 1 and the other tools in the BrainGlobe suite.
Seeour blog postsfor more information on the release of BrainGlobe version 1.ContributingIf you have encountered a bug whilst using cellfinder, pleaseopen an issue on GitHub.If you are interested in contributing to cellfinder (thank you!) - please head over to ourdeveloper documentation.