{"id":13592350,"url":"https://github.com/yeolab/flotilla","last_synced_at":"2025-04-08T23:32:22.002Z","repository":{"id":16565687,"uuid":"19319564","full_name":"YeoLab/flotilla","owner":"YeoLab","description":"Reproducible machine learning analysis of gene expression and alternative splicing data","archived":false,"fork":false,"pushed_at":"2023-04-15T19:16:52.000Z","size":170329,"stargazers_count":121,"open_issues_count":111,"forks_count":26,"subscribers_count":41,"default_branch":"master","last_synced_at":"2024-09-29T12:34:57.341Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"http://yeolab.github.io/flotilla/docs","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/YeoLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2014-04-30T16:14:31.000Z","updated_at":"2024-03-14T00:48:07.000Z","dependencies_parsed_at":"2022-07-13T13:51:00.183Z","dependency_job_id":"db95ecd1-c529-4e8c-b36e-a650029a5ff1","html_url":"https://github.com/YeoLab/flotilla","commit_stats":{"total_commits":2285,"total_committers":12,"mean_commits":"190.41666666666666","dds":"0.19299781181619258","last_synced_commit":"31da64567e59003c2b9c03fc8f4eb27ee62e299c"},"previous_names":[],"tags_count":11,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/YeoLab%2Fflotilla","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/YeoLab%2Fflotilla/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/YeoLab%2Fflotilla/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/YeoLab%2Fflotilla/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/YeoLab","download_url":"https://codeload.github.com/YeoLab/flotilla/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223346702,"owners_count":17130480,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-01T16:01:08.260Z","updated_at":"2025-04-08T23:32:21.997Z","avatar_url":"https://github.com/YeoLab.png","language":"Jupyter Notebook","funding_links":[],"categories":["Software packages","Software"],"sub_categories":["RNA-seq"],"readme":"[![Stories in Ready](https://badge.waffle.io/yeolab/flotilla.png?label=ready\u0026title=Ready)](https://waffle.io/yeolab/flotilla)\n[![Build Status](https://travis-ci.org/YeoLab/flotilla.svg?branch=master)](https://travis-ci.org/YeoLab/flotilla)\n[![Coverage Status](https://img.shields.io/coveralls/YeoLab/flotilla.svg?style=flat)](https://coveralls.io/r/YeoLab/flotilla?branch=master)\n[![License](https://img.shields.io/pypi/l/flotilla.svg?style=flat)](https://pypi.python.org/pypi/flotilla/)\n[![Downloads](https://img.shields.io/pypi/dm/flotilla.svg?style=flat)](https://pypi.python.org/pypi/flotilla/)\n[![Latest Version](https://img.shields.io/pypi/v/flotilla.svg?style=flat)](https://pypi.python.org/pypi/flotilla/)\n[![DOI](https://img.shields.io/badge/DOI-10.5281%20%2F%20zenodo.12230-blue.svg)](http://dx.doi.org/10.5281/zenodo.12230)\n[![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/YeoLab/flotilla)\n\nflotilla\n========\n\n[![Gitter](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/YeoLab/flotilla?utm_source=badge\u0026utm_medium=badge\u0026utm_campaign=pr-badge\u0026utm_content=badge)\n\n![flotilla Logo](flotilla.png)\n\n\nWhat is flotilla?\n=================\n\n`flotilla` is a Python package for visualizing transcriptome (RNA expression) data from hundreds of\nsamples. We include utilities to perform common tasks on these large data matrices, including:\n \n  * Dimensionality reduction\n  * Classification and Regression\n  * Outlier detection\n  * Network graphs from covariance\n  * Hierarchical clustering  \n  \nAnd common tasks for biological data including:\n\n  * Renaming database features to gene symbols\n  * Coloring/marking samples based on experimental phenotype\n  * Removing poor-quality samples (technical outliers)\n  \n  \nFinally, `flotilla` is a platform for active collaboration between bioinformatics scientists and \ntraditional \"wet lab\" scientists. Leveraging [interactive widgets](https://github.com/ipython/ipython/tree/master/examples/Interactive%20Widgets) \nin the [iPython Notebook](http://ipython.org/notebook.html), \nwe have created tools for simple and streamlined data exploration including:\n\n  * Subsetting sample groups and feature (genes/splicing events) groups\n  * Dynamically adjusting parameters for analysis\n  * Integrating external lists of features from the web or local files\n\nThese empower the \"wet lab\" scientists to ask questions on their own and gives bioniformatics\nscientists a platform and share their analysis tools.\n\n\nWhat flotilla is **not**\n========================\n\n`flotilla` is not a genomics pipeline. We expect that you have already generated\ndata tables for gene expression, isoform expression and metadata. `flotilla` only makes \nit easy to integrate all those data parts together once you have the pieces.\n\nLearn how to use flotilla\n=========================\nPlease refer to our [talks](talks.md) to learn more\n about how you can\napply our tools to your data.\n\n\nInstallation\n============\n\nDocker Installation Instructions\n--------------------------------\n\n[Docker](https://www.docker.com/whatisdocker/) is the preferred method to obtain the most up-to-date\nversion of `flotilla`. Every change we make to the source code triggers a new build of a virtual\n machine that contains flotilla and all its dependencies.\n \nPlease follow instructions [here](docker/docker_instructions.md) to get, install, and run the `flotilla` image.\n\nLocal install (on your computer)\n--------------------------------\n\nTo install, first install the \n[Anaconda Python Distribution](http://continuum.io/downloads), which comes\npre-packaged with a bunch of the scientific packages we use all the time, \npre-installed.\n\n### Create a Flotilla sandbox\n    \nWe recommend creating a \"sandbox\" where you can install any and all packages\nwithout disturbing the rest of the Python distribution. You can do this with\n\n    conda create --yes --name flotilla_env --file conda_requirements.txt\n\nYou've now just created a \"virtual environment\" called `flotilla_env` (the first\nargument). Now activate that environment with,\n\n    source activate flotilla_env\n\nNow at the beginning of your terminal prompt, you should see:\n\n    (flotilla_env)\n\nWhich indicates that you are now in the `flotilla_env` virtual environment. Now\n that you're in the environment, follow along with the non-sandbox\ninstallation instructions.\n\n### Install and update all packages in your environment\n\nTo make sure you have the latest packages, on the command line in your \nterminal, enter this command:\n\n    conda install --yes --file conda_requirements.txt\n\nNot all packages are available using `conda`, so we'll install the rest using\n`pip`, which is a Python package installer and installs from \n[PyPI](https://pypi.python.org/), the Python Package Index.\n\n    pip install -r requirements.txt\n    \nNext, to install the latest release of `flotilla`, do\n\n    pip install flotilla\n    \nIf you want the bleeding-edge master version (that we work really hard to make\nsure it's always working but could be buggy!), then install the `git` master\nwith,\n\n    pip install git+git://github.com/yeolab/flotilla.git\n\n\nTest dataset\n============\n\nWe have prepared a slice of the full dataset for testing and demonstration purposes.\n\nRun each of the following code lines in its own IPython notebook cell for an interactive feature.\n\n```python\nimport flotilla\nstudy = flotilla.embark(flotilla._shalek2013)\n\nstudy.interactive_pca()\n\nstudy.interactive_graph()\n\nstudy.interactive_classifier()\n\nstudy.interactive_lavalamp_pooled_inconsistent()\n```\n\nIMPORTANT NOTE: for this test,several failures are expected since the test set is small.\nAdjust parameters to explore valid parameter spaces.\nFor example, you can manually select `all_genes` as the `feature_subset`\nfrom the drop-down menu that appears after running these interactive functions.\n\n\n\nProblems? Questions?\n====================\n\nWe invite your input! Please leave any feedback on our [issues page](https://github.com/YeoLab/flotilla/issues).\n\n![NumFOCUS logo](https://www.continuum.io/sites/default/files/numfocus_logo.png)\n\nProudly sponsored by a NumFOCUS John Hunter Technical Fellowship to Olga\nBotvinnik.\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyeolab%2Fflotilla","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyeolab%2Fflotilla","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyeolab%2Fflotilla/lists"}