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singlecell-qtl

Discovery and characterization of variance QTLs in human induced pluripotent stem cells

https://github.com/jdblischak/singlecell-qtl

Science Score: 49.0%

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

  • ○
    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
  • ✓
    Committers with academic emails
    2 of 3 committers (66.7%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (14.0%) to scientific vocabulary

Keywords

single-cell
Last synced: 6 months ago · JSON representation

Repository

Discovery and characterization of variance QTLs in human induced pluripotent stem cells

Basic Info
Statistics
  • Stars: 9
  • Watchers: 6
  • Forks: 6
  • Open Issues: 1
  • Releases: 0
Topics
single-cell
Created over 8 years ago · Last pushed about 4 years ago
Metadata Files
Readme Contributing License Citation

README.md

singlecell-qtl

A workflowr project.

Now published in

Sarkar AK, Tung PY, Blischak JD, Burnett JE, Li YI, et al. (2019) Discovery and characterization of variance QTLs in human induced pluripotent stem cells. PLOS Genetics 15(4): e1008045. https://doi.org/10.1371/journal.pgen.1008045

Setup

To ensure all contributors are using the same computational environment, we use conda to manage software dependencies (made possible by the bioconda and conda-forge projects). Please complete the following steps to replicate the computing environment. Note that this is only guaranteed to work on a Linux-64 based architecture, but in theory should be able to work on macOS as well. All commands shown below are intended to be run in a Bash shell from the root of the project directory.

  1. Install Git and register for an account on GitHub

  2. Download and install Miniconda (instructions)

  3. Clone this repository (or your personal fork) using git clone

  4. Create the conda environment "scqtl" using environment.yaml conda env create --file environment.yaml

  5. To use the conda environment, you must first activate it by running source activate scqtl. This will override your default settings for R, Python, and various other software packages. When you are done working on this project, you can either logout of the current session or deactivate the environment by running source deactivate.

  6. Initialize git-lfs and download latest version of large data files git lfs install git lfs pull

If there are updates to environment.yaml, you can update the "scqtl" environment by running conda env update --file environment.yaml.

Warning: If you are using RStudio, you need to ensure that it recognizes your conda environment. If you launch RStudio by clicking on an icon, it doesn't use the current environment you have configured in your shell. On a Linux-based system, the solution is to launch RStudio directly from the shell with rstudio. On macOS, running open -a rstudio . from the shell causes RStudio to recognize most of the environment variables, but strangely it does not set the correct library path to the conda R packages. Suggestions for how to fix this are welcome.

Owner

  • Name: John Blischak
  • Login: jdblischak
  • Kind: user
  • Location: Ohio, USA

Freelance Scientific Software Developer

GitHub Events

Total
Last Year

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 543
  • Total Committers: 3
  • Avg Commits per committer: 181.0
  • Development Distribution Score (DDS): 0.492
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
John Blischak j****k@g****m 276
Abhishek Sarkar a****r@a****u 219
pytung p****g@u****u 48
Committer Domains (Top 20 + Academic)
uchicago.edu: 1 alum.mit.edu: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 0
  • Total pull requests: 51
  • Average time to close issues: N/A
  • Average time to close pull requests: about 19 hours
  • Total issue authors: 0
  • Total pull request authors: 2
  • Average comments per issue: 0
  • Average comments per pull request: 0.14
  • Merged pull requests: 49
  • Bot issues: 0
  • Bot pull requests: 2
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
Pull Request Authors
  • aksarkar (35)
  • dependabot[bot] (1)
Top Labels
Issue Labels
Pull Request Labels
dependencies (1)

Dependencies

environment.yaml pypi
  • scqtl ==0.1
  • tensorflow ==1.3.0
requirements.txt pypi
  • ConfigArgParse ==0.12.0
  • Cython ==0.27.3
  • Jinja2 ==2.10
  • Markdown ==2.6.9
  • MarkupSafe ==1.0
  • Pillow ==5.0.0
  • PyNaCl ==1.1.2
  • PySocks ==1.6.8
  • PyYAML ==3.12
  • Pygments ==2.2.0
  • Send2Trash ==1.5.0
  • Werkzeug ==0.14.1
  • aioeasywebdav ==2.2.0
  • aiohttp ==2.0.7
  • appdirs ==1.4.3
  • asn1crypto ==0.22.0
  • async-timeout ==1.2.1
  • backports.functools-lru-cache ==1.4
  • backports.weakref ==1.0rc1
  • bcrypt ==3.1.4
  • biopython ==1.70
  • bleach ==1.5.0
  • bokeh ==0.13.0
  • certifi ==2016.9.26
  • cffi ==1.11.2
  • chardet ==3.0.4
  • click ==6.7
  • colorcet ==1.0.0
  • colormath ==2.1.1
  • cryptography ==2.2.1
  • cycler ==0.10.0
  • decorator ==4.1.2
  • docopt ==0.6.2
  • docutils ==0.14
  • dropbox ==7.3.1
  • entrypoints ==0.2.3
  • filechunkio ==1.8
  • ftputil ==3.3.1
  • future ==0.16.0
  • html5lib ==0.9999999
  • idna ==2.6
  • ipykernel ==4.7.0
  • ipython ==6.2.1
  • ipython-genutils ==0.2.0
  • ipywidgets ==7.1.0
  • jedi ==0.10.2
  • jsonschema ==2.6.0
  • jupyter-client ==5.2.1
  • jupyter-console ==5.2.0
  • jupyter-core ==4.4.0
  • lzstring ==1.0.3
  • matplotlib ==2.1.1
  • mistune ==0.8.3
  • mkl-fft ==1.0.2
  • mkl-random ==1.0.1
  • mmtf-python ==1.0.10
  • msgpack-python ==0.4.8
  • multidict ==2.1.4
  • multiqc ==1.3
  • nbconvert ==5.3.1
  • nbformat ==4.4.0
  • networkx ==1.11
  • notebook ==5.4.1
  • numpy ==1.14.3
  • olefile ==0.44
  • packaging ==17.1
  • pandas ==0.22.0
  • pandocfilters ==1.4.1
  • paramiko ==2.3.1
  • pexpect ==4.3.1
  • pickleshare ==0.7.4
  • prompt-toolkit ==1.0.15
  • protobuf ==3.5.1
  • psutil ==5.4.0
  • ptyprocess ==0.5.2
  • pyOpenSSL ==17.4.0
  • pyasn1 ==0.4.2
  • pycparser ==2.18
  • pyparsing ==2.2.0
  • pysam ==0.11.2.2
  • pysftp ==0.2.9
  • pytabix ==0.0.2
  • python-dateutil ==2.6.1
  • pytz ==2017.3
  • pyzmq ==16.0.2
  • qtconsole ==4.3.1
  • ratelimiter ==1.2.0
  • regex ==2017.12.12
  • reportlab ==3.4.0
  • requests ==2.14.2
  • rpy2 ==2.8.5
  • scikit-learn ==0.19.1
  • scipy ==1.0.0
  • simplegeneric ==0.8.1
  • simplejson ==3.11.1
  • singledispatch ==3.4.0.3
  • six ==1.11.0
  • snakemake ==4.4.0
  • spectra ==0.0.7
  • tensorflow ==1.3.0
  • tensorflow-tensorboard ==0.1.5
  • terminado ==0.8.1
  • testpath ==0.3.1
  • tornado ==4.5.3
  • traitlets ==4.3.2
  • umi-tools ==0.5.3
  • urllib3 ==1.22
  • wcwidth ==0.1.7
  • webencodings ==0.5
  • widgetsnbextension ==3.1.0
  • wrapt ==1.10.11
  • yarl ==0.10.0