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xpressplot

A toolkit for navigating and analyzing gene expression datasets

https://github.com/xpressyourself/xpressplot

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (15.5%) to scientific vocabulary

Keywords

analysis genomics microarray plotting rna-seq rnaseq visualization
Last synced: 4 months ago · JSON representation ·

Repository

A toolkit for navigating and analyzing gene expression datasets

Basic Info
Statistics
  • Stars: 3
  • Watchers: 0
  • Forks: 1
  • Open Issues: 0
  • Releases: 13
Topics
analysis genomics microarray plotting rna-seq rnaseq visualization
Created almost 7 years ago · Last pushed over 3 years ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

XPRESSplot

A toolkit for navigating and analyzing gene expression datasets

Build Status Documentation Status codecov.io PyPi Status Anaconda-Server Badge DOI Anaconda-Server Badge

Find documentation here

Development Notes:

XPRESSplot supports Python 2.7 and >=3.5

Citation:

Berg JA, et. al. (2020). XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data. PLoS Comp Biol. doi: https://doi.org/10.1371/journal.pcbi.1007625

Installation:

pip install xpressplot

Other Requirements:

  • Tested on 64-bit Linux, compatible with Mac OS X
  • Python and R are required
  • If PyPi and Conda are not already installed, these should be installed
  • If using the interactive notebook provided, Jupyter needs to be installed if not already

QuickStart:

  • Download the repository and modify the interactive Jupyter notebook to get started fast!
  • Read the instructions as you navigate through the code blocks for a guide on how to use the example code
  • Code blocks are run by selecting the block and pressing Shift + Enter
  • See documentation for more detailed instructions

Important Notes:

  • If working with XPRESSplot within an interactive notebook (i.e. Jupyter Notebook, Atom Hydrogen, etc), you must include the following line of code after importing XPRESSplot

import XPRESSplot as xp %matplotlib inline

  • Assumes all dataframes are i * j (or genes * samples, except in certain cases, see documentation for help)

```

geo.head() name GSM523242 GSM523243 GSM523244 GSM523245 GSM523246 GSM523247 ...
GeneA 8.98104 8.59941 8.25395 8.72981 8.70794 8.10693 ...
GeneB 5.84313 6.59168 8.27881 6.64005 4.65107 7.19090 ...
GeneC 6.17189 5.73603 5.55673 5.69374 6.77618 5.84524 ...
GeneD 6.97009 6.80003 5.56620 7.43816 7.36375 5.85687 ...
GeneE 10.24611 10.13807 8.84743 9.72365 10.42940 9.17510 ...

[5 rows x 145 columns]
```

Updates

Information on updates to the software can be found here.

Owner

  • Name: XPRESSyourself
  • Login: XPRESSyourself
  • Kind: organization
  • Location: University of Utah

Ribosome Profiling and RNA-seq processing and analysis made easy

Citation (CITATION.cff) 

cff-version: 0.0.1
message: "If you use this software, please cite it as below."
authors:
  - family-names: Berg
    given-names: Jordan A.
    orcid: https://orcid.org/0000-0002-5096-0558
title: "XPRESSyourself"
version: 0.6.3
doi: 10.1371/journal.pcbi.1007625
date-released: 2021-03-29

GitHub Events

Total
Last Year

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 229
  • Total Committers: 2
  • Avg Commits per committer: 114.5
  • Development Distribution Score (DDS): 0.004
Top Committers
Name Email Commits
j-berg j****g@u****m 228
Alex a****t@g****m 1

Issues and Pull Requests

Last synced: 5 months ago

All Time
  • Total issues: 2
  • Total pull requests: 1
  • Average time to close issues: about 1 year
  • Average time to close pull requests: 4 days
  • Total issue authors: 2
  • Total pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • j-berg (1)
  • alexbott (1)
Pull Request Authors
  • alexbott (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 16 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 10
  • Total maintainers: 1
pypi.org: xpressplot

A toolkit for navigating and analyzing gene expression datasets

  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 16 Last month
Rankings
Dependent packages count: 10.0%
Dependent repos count: 21.7%
Average: 22.2%
Forks count: 22.6%
Stargazers count: 25.0%
Downloads: 31.5%
Maintainers (1)
j-berg
Last synced: 4 months ago

Dependencies

.github/workflows/python-package.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • pypa/gh-action-pypi-publish master composite
requirements.txt pypi
  • matplotlib <3.0.0,>=2.1.1
  • numpy *
  • pandas *
  • plotly *
  • plotly_express *
  • scikit-learn *
  • scipy *
  • seaborn *
setup.py pypi
  • matplotlib <3.0.0,>=2.1.1
  • numpy *
  • pandas *
  • plotly *
  • plotly_express *
  • scikit-learn *
  • scipy *
  • seaborn *