https://github.com/dev-ev/bokeh-qc-dashboard

QC dashboard for LC-MS proteomic equipment using python and bokeh
https://github.com/dev-ev/bokeh-qc-dashboard

bokeh bokeh-dashboard data-visualization mass-spectrometry orbitrap-ms performance-monitoring proteomics

Last synced: about 1 month ago
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QC dashboard for LC-MS proteomic equipment using python and bokeh

• Host: GitHub
• URL: https://github.com/dev-ev/bokeh-qc-dashboard
• Owner: dev-ev
• License: bsd-2-clause
• Created: 2020-10-03T06:56:53.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2022-03-19T19:57:22.000Z (almost 3 years ago)
• Last Synced: 2023-10-20T06:36:14.434Z (about 1 year ago)
• Topics: bokeh, bokeh-dashboard, data-visualization, mass-spectrometry, orbitrap-ms, performance-monitoring, proteomics
• Language: Python
• Homepage:
• Size: 780 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# bokeh-QC-dashboard

QC dashboard for proteomics using python and bokeh. Created and tested with Orbitrap mass spectrometers in mind.

The dashboard makes use of the key QC values that are stored in an SQLite database. During the development of the dashboard, the QC runs were injections of 50 ng of a HeLa cell tryptic digest, and the database is filled with the output values from Proteome Discoverer 2.4 searches that are summarized and saved into an SQLite database by [the integrated *QC_Script_PD2.4*](https://github.com/dev-ev/qc-script-PD24) script.

The file templates/index.html is added in order to change the background color of the app.

The app consists of one long page and is based on bokeh library:



Select the instrument using the dropdown menu on the left:



Hover over a bar on the "Service/Cleaning" plot to see the details about the procedure:



By default, the app displays the data for the whole time that is available in the database. Zoom in onto a plot to see a smaller region on both axes:



The time span on all the plots will change in sync:



Hover over a point on a plot to see the numbers: