Do you know how correct your 2D gel data is?

The problem

There is a need for a method of assessing the correctness of data that is generated from 2D gel image analysis. The correctness of the data is obviously essential for the validity of the statistical results building on the data.

A metric for correctness, and a way of practically applying this metric on data from 2D gel image analysis is highly relevant to the field of 2D gel based proteomics. However, such a metric is presently lacking, even though several attempts have been made (see below).

This initiative

This initiative aims to address this shortcoming by developing a free software tool that makes it possible to produce an estimate of the correctness of the data extracted from 2D gel images, based on a methodology suggested by scientists and experts in the field.

Criteria

The software tool that this initiative aim to develop shall:

  • Facilitate a quality control of data from 2D gel image analysis
  • Be independent of software used for the 2D gel image analysis
  • Be free for the proteomics community to use

Learn more

Read about the Combined Correctness 2D gel data quality metric

Find out how you can measure the quality of your own 2D gel data

 

Other approaches to quality control of 2D gel image data

Ad hoc methods used in various articles

Previous research has often involved comparisons of the performance of different software packages for 2D gel image analysis. Other studies have attempted to construct artificial gel images as input for the assessment of the performance of image analysis software.

In our opinion, there has been a lack of focus on the measurement of correctness itself, on real gel images, and different authors have used their own ad-hoc methodology to evaluate various aspects of image analysis. This makes comparisons between studies difficult.

Reproducibility vs correctness

Today there is a great deal of focus in the proteomics community on reproducibility, but reproducibility should not be confused with correctness. The concept of reproducibility does not take into account whether or not the data is correct, only if it is similar. A narrow focus on reproducibility is insufficient, because data may well be reproducible, but incorrect.

Reproducibility only makes sense if the reproducible data is correct. Correct data that can not be reproduced give valuable (if yet negative) feedback about the experiment. But reproducible data that is incorrect can be seriously misleading if the reproducibility of the data is used as an argument that it is trustable. In short, reproducibility without correctness has little if any value to researchers.