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توضیحات

Collection of BEDA internal python functions for analysing single cell RNAseq data.

ویژگی	مقدار
سیستم عامل	-
نام فایل	besca-2.5.3
نام	besca
نسخه کتابخانه	2.5.3
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	BEDA community
ایمیل نویسنده	manuel.kohler@roche.com
آدرس صفحه اصلی	https://github.com/bedapub/besca
آدرس اینترنتی	https://pypi.org/project/besca/
مجوز	GPLv3

# BESCA (BEyond Single Cell Analysis) [![Run doctests](https://github.com/bedapub/besca/actions/workflows/doc-tests.yml/badge.svg)](https://github.com/bedapub/besca/actions/workflows/doc-tests.yml) [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) The BESCA (BEyond Single Cell Analysis) package contains many useful python functions to use for your single-cell analysis. The package has been grouped into 3 categories: - preprocessing functions: this submodule contains all functions relevant to data preprocessing - plotting functions: additional plot types not available in the standard scanpy package - tools: contains additional tools to e.g. perform differential gene analysis or load/export data For more information please view the package documentation: https://bedapub.github.io/besca/ Please consider citing our publication if you use Besca for your research: - Mädler SC, Julien-Laferriere A, Wyss L, Phan M, Sonrel A, Kang ASW, Ulrich E, Schmucki R, Zhang JD, Ebeling M, Badi L, Kam-Thong T, Schwalie PC, Hatje K. <a href="https://doi.org/10.1093/nargab/lqab102" target="_blank">Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research</a>. <i>NAR Genom Bioinform</i>. 2021 If you are interested in contributing you can check the repository wiki for helpful information on contributing: https://github.com/bedapub/besca/wiki For faster/smaller download, in case of slow internet connection or low storage capacity, please use following command to clone this repository: ``` git clone --filter=blob:none git@github.com:bedapub/besca.git ``` ## Installation From version 2.2.5+, Besca requires Python 3.8 or above. If you are familiar with python packages simply install them using pip: ``` pip install besca ``` or ``` pip install git+https://github.com/bedapub/besca.git ``` Besca comes with a binary called reformat written in C and was compiled in linux-64. Therefore, besca runs exclusively on linux-64. ### Set the executable flag to the binary file `reformat` <a name="binary"></a> In some cases, the binary file needs to be made executable. To do so, run the following one-liner. ```bash pip show besca | grep Location | cut -f 2 -d ":" | awk -v OFS="" '{print "chmod u+x" $0 "/besca/export/reformat"}' | bash ``` If you want to avoid piping to bash, or want to it step by step, here is how to. Show the location of the path and navigate to the besca package. ``` pip show besca cd Location/besca ``` Navigate in the directory containing the binary and make it executeable. ``` cd export chmod u+x reformat ``` ### Python beginner guide If you are not very familiar with python packages here is a detailed description. If you don't have a conda python installation download and install [miniconda](https://docs.conda.io/en/latest/miniconda.html). While installing we recommend accepting everything asked by the miniconda installation. As a next step, we create a separate environment for besca which is also called besca. ``` conda create --name besca python=3.8 ``` We can activate this environment. ``` conda activate besca ``` Within this environment, we can install besca using pip. ``` pip install git+https://github.com/bedapub/besca.git ``` Now following the [instruction above](#binary) to set the executable flag to the binary file shipped with besca. You should now have successfully installed besca. In case you met any problems, please report an issue. To install [Jupyter Notebook](https://jupyter.readthedocs.io/en/latest/install/notebook-classic.html), type ``` conda install jupyter ``` and type ``` jupyter notebook ``` to start a Jupyter Notebook in your browser. See [documentation](https://jupyter.readthedocs.io/en/latest/running.html#running) for further details. ### R dependencies for additional methods [R-based functions are deprecated] Although the standard workflow can be run without any R dependencies, BESCA can run a selection of performant methods developed in R. These additional methods are : - [`isOutlier`](https://www.rdocumentation.org/packages/scater/versions/1.0.4/topics/isOutlier) from `scater`: for outlier detection and filtering recommendations. Implemented in the `besca.pp.valOutlier` function. - [`SCTransform`](https://rdrr.io/github/satijalab/seurat/man/SCTransform.html) : one of the normalization methods proposed by the `Seurat` package. Implemented in the `besca.pp.scTransform` function. - [`maxLikGlobalDimEst`](https://cran.r-project.org/web/packages/intrinsicDimension/intrinsicDimension.pdf) from `intrinsicDimension` : for an estimation of the number of dimensions to use for clustering. Implemented in the `besca.st.maxLikGlobalDimEst` function. - [`deviance`](https://rdrr.io/bioc/scry/man/devianceFeatureSelection.html) and [`VST`](https://rdrr.io/github/satijalab/seurat/man/SCTransform.html): for highly-variable genes selection. Implemented in the `besca.st.deviance` function. - [`DSB`](https://github.com/niaid/dsb): for denoising ADT counts data based on background noise. Implemented in the `besca.st.dsb_normalize` function. #### Conda installation If you used a conda enviroment it is possible to install most needed dependencies using Conda too. With an activated environment using: ``` conda activate besca ``` One can run the commands below: ``` conda install -y -c conda-forge r=4.0 rpy2 r-essentials r-base r-devtools r-withr r-vctrs r-tidyverse r-magrittr r-data.table r-Matrix r-ggplot2 r-readr r-seurat r-intrinsicdimension r-mclust r-sitmo r-patchwork --force-reinstall conda install -y -c bioconda anndata2ri R bioconductor-dropletutils bioconductor-scry conda install -c bioconda bioconductor-scater ``` This should install in your conda envrionment the dependencies under : *conda_path/lib/R/library* of your conda environment path. #### Pip installation If you want to run one of these methods in the workflow, please install the required libraries by running the following commands in the `besca` installation directory (or simply download the `Rlibs.R` file): ``` pip install rpy2 anndata2ri <conda_Rscript_bin_path> Rlibs.R <conda_R_library_path> ``` ### Location of conda Rscript bin Typically: <conda_Rscript_bin_path> = `~/.conda/envs/[environnement_name]/bin/Rscript` ### Location of the conda R library If you used conda, by default, libraries should be installed into your conda environment path, typically `~/.conda/envs/[environnement_name]/lib/R/library`. If this is not the right path, please verify the path to your conda enviroment using `conda list env`. To minimize risks conflicts between libraries, it is advised to set your `your_R_library_path` to such path also while using pip. In the standard workflow notebook, all of these methods are controlled through the `r_methods` option but it is of course possible to manually switch between them and the standard workflow. Please also specify the location of your R library with the `rlib_loc` option of the notebook. ## Running besca on an HPC with a SLURM workload manager If you have access to an HPC which uses SLURM as a workload manager you can run the jupyter notebooks coming with besca located in `workbooks/` with dedicated resources. To do so, start an interactive session on your HPC. ``` interactive -c 8 -m 16G -t 180 # This allocates 8 CPUs, 16 GB of memory for 3 hours ``` If you have installed besca in a conda environment like explained above activate the environment. ``` conda activate besca ``` Start a jupyter notebook. ``` jupyter-notebook --ip=* --no-browser ``` You can now run the jupyter notebooks coming with besca. ## Datasets and Analysis notebooks Besca run-examples and datasets annotation notebooks can be found in: [https://github.com/bedapub/besca_publication_results](https://github.com/bedapub/besca_publication_results) All processed datasets were uploaded to Zenodo, within the Besca community: [https://zenodo.org/communities/besca/](https://zenodo.org/communities/besca/)

نیازمندی

مقدار	نام
-	requests
>=1.5.4	scipy
-	flask-restful
-	dominate
>=1.18.0	numpy
>=1.7.2	scanpy
-	plotly
>=0.7.4	anndata
-	seaborn
-	mygene
>=1.1.0	pandas
>=3.5.0	matplotlib
-	bbknn
-	ipython
-	nbclean
>=1.0.2	scikit-learn
-	python-igraph
>=0.8.3	leidenalg
-	scanorama
-	scvelo
-	umap-learn
-	sinfo
-	pydot
-	scvi-tools
-	pytest
-	deprecation

نحوه نصب

نصب پکیج whl besca-2.5.3:

pip install besca-2.5.3.whl

نصب پکیج tar.gz besca-2.5.3:

pip install besca-2.5.3.tar.gz