single-cell-tutorial | Single cell current best practices | Genomics library

 by   theislab Jupyter Notebook Version: Current License: No License

kandi X-RAY | single-cell-tutorial Summary

kandi X-RAY | single-cell-tutorial Summary

single-cell-tutorial is a Jupyter Notebook library typically used in Artificial Intelligence, Genomics, Pytorch, Tensorflow applications. single-cell-tutorial has no bugs, it has no vulnerabilities and it has medium support. You can download it from GitHub.

Single cell current best practices tutorial case study for the paper:Luecken and Theis, "Current best practices in single-cell RNA-seq analysis: a tutorial"

            kandi-support Support

              single-cell-tutorial has a medium active ecosystem.
              It has 1076 star(s) with 399 fork(s). There are 44 watchers for this library.
              It had no major release in the last 6 months.
              There are 17 open issues and 82 have been closed. On average issues are closed in 35 days. There are 4 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of single-cell-tutorial is current.

            kandi-Quality Quality

              single-cell-tutorial has 0 bugs and 0 code smells.

            kandi-Security Security

              single-cell-tutorial has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              single-cell-tutorial code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              single-cell-tutorial does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              single-cell-tutorial releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.
              It has 72 lines of code, 2 functions and 1 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

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            single-cell-tutorial Key Features

            No Key Features are available at this moment for single-cell-tutorial.

            single-cell-tutorial Examples and Code Snippets

            No Code Snippets are available at this moment for single-cell-tutorial.

            Community Discussions

            Trending Discussions on single-cell-tutorial


            FileNotFoundError when using
            Asked 2020-Dec-28 at 06:27

            I'm following the single cell sequencing analysis tutorial from theislab on Jupyter notebook, link below




            Answered 2020-Dec-28 at 06:27

            I think you forgot the slash in file_base:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install single-cell-tutorial

            A docker container with a working sc-tutorial environment is now available here thanks to Leander Dony. If you would like to set up the environment via conda or manually outside of the docker container, please follow the instructions below. To run the tutorial case study, several packages must be installed. As both R and python packages are required, we prefer using a conda environment. To facilitate the setup of a conda environment, we have provided the sc_tutorial_environment.yml file, which contains all conda and pip installable dependencies. R dependencies, which are not already available as conda packages, must be installed into the environment itself. To set up a conda environment, the following instructions must be followed. These steps should set up an environment to perform single cell analysis with the tutorial workflow on a Linux system. Please note that we have encountered issues with conda environments on Mac OS. When using Mac OS we recommend installing the packages without conda using separately installed python and R versions. Alternatively, you can try using the base conda environment and installing all packages as described in the conda_env_instructions_for_mac.txt file. In the base environment, R should be able to find the relevant gsl libraries, so LDFLAGS and CFLAGS should not need to be set. Also note that conda and pip doesn't always play nice together. Conda developers have suggested first installing all conda packages and then installing pip packages on top of this where conda packages are not available. Thus, installing further conda packages into the environment may cause issues. Instead, start a new environment and reinstall all conda packages first. If you prefer to set up an environment manually, a list of all package requirements are given at the end of this document.
            Set up the conda environment from the sc_tutorial_environment.yml file. conda env create -f sc_tutorial_environment.yml
            Ensure that the environment can find the gsl libraries from R. This is done by setting the CFLAGS and LDFLAGS environment variables (see Here we set them so that they are correctly set every time the environment is activated. cd YOUR_CONDA_ENV_DIRECTORY mkdir -p ./etc/conda/activate.d mkdir -p ./etc/conda/deactivate.d touch ./etc/conda/activate.d/ touch ./etc/conda/deactivate.d/ Where YOUR_CONDA_ENV_DIRECTORY can be found by running conda info --envs and using the directory that corresponds to your conda environment name (default: sc-tutorail). WHILE NOT IN THE ENVIRONMENT(!!!!) open the file at ./etc/conda/activate.d/ and enter the following into the file: #!/bin/sh CFLAGS_OLD=$CFLAGS export CFLAGS_OLD export CFLAGS="`gsl-config --cflags` ${CFLAGS_OLD}" LDFLAGS_OLD=$LDFLAGS export LDFLAGS_OLD export LDFLAGS="`gsl-config --libs` ${LDFLAGS_OLD}" Also change the ./etc/conda/deactivate.d/ file to: #!/bin/sh CFLAGS=$CFLAGS_OLD export CFLAGS unset CFLAGS_OLD LDFLAGS=$LDFLAGS_OLD export LDFLAGS unset LDFLAGS_OLD Note again that these files should be written WHILE NOT IN THE ENVIRONMENT. Otherwise you may overwrite the CFLAGS and LDFLAGS environment variables in the base environment!
            Enter the environment by conda activate sc-tutorial or conda activate ENV_NAME if you changed the environment name in the sc_tutorial_environment.yml file.
            Open R and install the dependencies via the commands: install.packages(c('devtools', 'gam', 'RColorBrewer', 'BiocManager')) update.packages(ask=F) BiocManager::install(c("scran","MAST","monocle","ComplexHeatmap","slingshot"), version = "3.8")
            The following packages are required to run the first version of the case study notebook. For further versions see the in the latest_notebook/ and old_releases/ folders.
            Jupyter notebook
            R >= 3.4.3
            Python >= 3.5
            gprofiler-official (from Case study notebook 1906 version)
            python-gprofiler from Valentine Svensson's github (vals/python-gprofiler) only needed for notebooks before version 1906
            ComBat python implementation from Maren Buettner's github (mbuttner/maren_codes/ only needed for scanpy versions before 1.3.8 which don't include sc.pp.combat()
            slingshot (change DESCRIPTION file for R version 3.4.3)
            monocle 2


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