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Software‐induced variance in two‐dimensional gel electrophoresis image analysis
Author(s) -
Wheelock Åsa M.,
Buckpitt Alan R.
Publication year - 2005
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.200500253
Subject(s) - replicate , variance (accounting) , analysis of variance , software , subtraction , smoothing , background subtraction , statistics , image subtraction , biology , mathematics , image (mathematics) , computer science , artificial intelligence , image processing , pixel , arithmetic , binary image , accounting , business , programming language
Experimental variability in 2‐DE is well documented, but little attention has been paid to variability arising from postexperimental quantitative analyses using various 2‐DE software packages. The performance of two 2‐DE analysis software programs, Phoretix 2D Expression v2004 (Expression) and PDQuest 7.2 (PDQuest), was evaluated in this study. All available background subtraction and smoothing algorithms were tested using both data generated from one single 2‐DE gel image, thus excluding experimental variance, and with authentic sets of replicate gels ( n = 5). A slight shift of the image boundaries (the “cropping area”) caused both programs to induce variance in protein spot quantification of otherwise identical gel images. The resulting variance for PDQuest (CV mean = 8%) was approximately twice that for Expression (CV mean = 4%). In authentic sets of replicate 2‐DE gels ( n = 5), the experimental variance confounded the software‐induced variance to some extent. However, Expression still outperformed PDQuest, which exhibited software‐induced variance as high as 25% of the total observed variance. Surprisingly, the complete omission of background subtraction algorithms resulted in the least amount of software‐based variance. These data indicate that 2‐DE gel analysis software constitutes a significant source of the variance observed in quantitative proteomics, and that the use of background subtraction algorithms can further increase the variance.