QC dashboard

[mwalzer]

Abstract

A systematic approach to quality control (QC) is of crucial importance to endorse the results of a mass spectrometry experiment. Although several tools that compute QC information exist, making use of the QC metrics they generate to determine the quality of an experiment remains a hard task. The HUPO-PSI QC-dev working group is developing a QC file format to facilitate the collection and sharing of various computed quality metrics, from single run analytics to longitudinal control capture and complete experiment quality reports.
In this project, we will develop a tool for the visualization and analysis of different QC metrics. For example, a Hotelling T2 control chart can be used to highlight experiments for which a metric exhibits extreme values, while robust PCA or other outlier detection techniques can be used to identify low-performing experiments.
The aim of this project is to develop a dashboard to load QC data, analyze the QC data to detect experiments with a diminished performance, and visualize this information in a user-friendly and easy to access fashion.

Work plan

We will introduce qcML and explore platforms to create QC metrics for mass spectrometry. Following that, we will:

• Collect data from target platforms (analysis software, qcML)
• Develop visualisation for dashboard visuals to:
  1. Hotelling T2 control
  2. Outlier interpretation aides
  3. Various metrics from participants choice/HUPO-PSI QC metric controlled vocabulary
• Deposit calculated/adopted metrics in a qcML document(s)

Technical details

There is a plethora of ways, methods, and tools to achieve the described goals, including javascript / R / Python / C++

We will access (and write) data from qcML (p.r.n. mzID, mzML -which are HUPO-PSI XML formats- and use existing software that calculates QC metrics).

Data:

• https://www.ebi.ac.uk/pride/archive/projects/PXD002901 (Timeseries)
• https://www.ebi.ac.uk/pride/archive/projects/PXD002080 (CPTAC)
• https://www.ebi.ac.uk/pride/archive/projects/PXD000322 (Quality signatures)

Follow up:

• create a web service for retrieving the created visualisations and en-suite embedded use of our dashboard
• adapt various open software reporting on quality metrics analysis platforms that report quality to write qcML and get added value from our QC dashboard visualisations

Links:
https://rstudio.github.io/shinydashboard/
https://github.com/HUPO-PSI/qcML-development

Contact information

Mathias Walzer, EMBL-EBI
walzer@ebi.ac.uk