GSEApy | Gene Set Enrichment Analysis in Python | Genomics library

 by   zqfang Python Version: v1.0.4 License: BSD-3-Clause

kandi X-RAY | GSEApy Summary

kandi X-RAY | GSEApy Summary

GSEApy is a Python library typically used in Artificial Intelligence, Genomics applications. GSEApy has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However GSEApy has 2 bugs. You can download it from GitHub.

Gene Set Enrichment Analysis in Python
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            kandi-support Support

              GSEApy has a low active ecosystem.
              It has 392 star(s) with 97 fork(s). There are 9 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 11 open issues and 131 have been closed. On average issues are closed in 41 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of GSEApy is v1.0.4

            kandi-Quality Quality

              GSEApy has 2 bugs (0 blocker, 0 critical, 1 major, 1 minor) and 182 code smells.

            kandi-Security Security

              GSEApy has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              GSEApy code analysis shows 0 unresolved vulnerabilities.
              There are 12 security hotspots that need review.

            kandi-License License

              GSEApy is licensed under the BSD-3-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              GSEApy releases are available to install and integrate.
              Build file is available. You can build the component from source.
              GSEApy saves you 1044 person hours of effort in developing the same functionality from scratch.
              It has 2369 lines of code, 123 functions and 13 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed GSEApy and discovered the below as its top functions. This is intended to give you an instant insight into GSEApy implemented functionality, and help decide if they suit your requirements.
            • Run Enrichr
            • enrich the background
            • Query gene ids
            • Return the set of background genes
            • Generate a tensorflow tensorflow problem
            • Compute the FDR distribution
            • Compute the significance of a GSE
            • Compute the p - value p - value
            • Run the analysis
            • Compute the heatmatrix for each phenotype
            • Load genesets from gmt
            • Load a Gmt file into memory
            • Compute the fitness scores for the given gene
            • Generate a ranking metric tensor
            • Calculate enrichment score tensor
            • Plot ring plot
            • Save the figure
            • Read a gmt file
            • Prepare the output directory for gseapy
            • Prepare the outdir for the gene_set
            • Run SSGSEA analysis
            • Plot a histogram of values
            • Creates an argument parser
            • Run prerank analysis
            • Query gene identifiers
            • Set the organism name
            • Find the python version string
            Get all kandi verified functions for this library.

            GSEApy Key Features

            No Key Features are available at this moment for GSEApy.

            GSEApy Examples and Code Snippets

            No Code Snippets are available at this moment for GSEApy.

            Community Discussions

            QUESTION

            Best way for installing non-conda dependencies in Snakemake conda environments
            Asked 2020-Nov-02 at 20:30

            I would like to be able to install R packages from GitHub in a R conda environment created by Snakemake, as well as python libraries via pip in a python environment. I'll use these environments in a whole set of rules thereafter.

            My initial thought was to create a rule running a script to install the specified packages.

            For instance, my initial run was: snakemake -j1 --use-conda -R create_r_environment.

            My Snakefile:

            ...

            ANSWER

            Answered 2020-Nov-01 at 09:18

            I think there are quite a few wrong things:

            • remotes::install_github("ramiromagno/gwasrapidd", upgrade = "never"): In your r.yaml you should include the remotes package.

            • !pip install gseapy is not valid python code. If anything, it is code to be executed by shell but I'm not sure that leading ! is correct. Also, gseapy is available from bioconda I don;t see why you should install it with pip.

            Before OP edited the question

            My envs/r.yaml file:

            Source https://stackoverflow.com/questions/64617670

            QUESTION

            Is it possible to check the OS compatibility of a conda .yaml file?
            Asked 2020-Jul-21 at 04:09

            Given a .yaml file such as

            ...

            ANSWER

            Answered 2020-Jul-21 at 04:09

            Automatically? No, not in terms of a built-in CLI option for that purpose. But one could parse out all the package names (e.g., via grep/awk/sed), then check each of them with conda search. The trick to check platform-specific branches in Anaconda Cloud repositories is to use the subdir key for whatever platform you are validating for. For example,

            Source https://stackoverflow.com/questions/62996112

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

            Vulnerabilities

            No vulnerabilities reported

            Install GSEApy

            You can download it from GitHub.
            You can use GSEApy like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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