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A probabilistic treatment of the missing spot problem in 2D gel electrophoresis experiments

Krogh, Morten LU ; Fernandez, Celine LU ; Teilum, Maria LU ; Bengtsson, Sofia and James, Peter LU (2007) In Journal of Proteome Research 6(8). p.3335-3343
Abstract
Two-dimensional SIDS-PAGE gel electrophoresis using post-run staining is widely used to measure the abundances of thousands of protein spots simultaneously. Usually, the protein abundances of two or more biological groups are compared using biological and technical replicates. After gel separation and staining, the spots are detected, spot volumes are quantified, and spots are matched across gels. There are almost always many missing values in the resulting data set. The missing values arise either because the corresponding proteins have very low abundances (or are absent) or because of experimental errors such as incomplete/over focusing in the first dimension or varying run times in the second dimension as well as faulty spot detection... (More)
Two-dimensional SIDS-PAGE gel electrophoresis using post-run staining is widely used to measure the abundances of thousands of protein spots simultaneously. Usually, the protein abundances of two or more biological groups are compared using biological and technical replicates. After gel separation and staining, the spots are detected, spot volumes are quantified, and spots are matched across gels. There are almost always many missing values in the resulting data set. The missing values arise either because the corresponding proteins have very low abundances (or are absent) or because of experimental errors such as incomplete/over focusing in the first dimension or varying run times in the second dimension as well as faulty spot detection and matching. In this study, we show that the probability for a spot to be missing can be modeled by a logistic regression function of the logarithm of the volume. Furthermore, we present an algorithm that takes a set of gels with technical and biological replicates as input and estimates the average protein abundances in the biological groups from the number of missing spots and measured volumes of the present spots using a maximum likelihood approach. Confidence intervals for abundances and p-values for differential expression between two groups are calculated using bootstrap sampling. The algorithm is compared to two standard approaches, one that discards missing values and one that sets all missing values to zero. We have evaluated this approach in two different gel data sets of different biological origin. An F-program, implementing the algorithm, is freely available at httP://bioinfo.thep.lu.se/MissingValues2Dgels.html. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
missing values, maximum likelihood, 2D-PAGE
in
Journal of Proteome Research
volume
6
issue
8
pages
3335 - 3343
publisher
The American Chemical Society
external identifiers
  • wos:000248683200041
  • scopus:34548149778
ISSN
1535-3893
DOI
10.1021/pr070137p
project
CREATE Health
language
English
LU publication?
yes
id
6c878b1c-f8e8-4973-bda6-8a1cfa05643c (old id 646358)
date added to LUP
2007-12-12 10:09:57
date last changed
2017-11-05 03:31:36
@article{6c878b1c-f8e8-4973-bda6-8a1cfa05643c,
  abstract     = {Two-dimensional SIDS-PAGE gel electrophoresis using post-run staining is widely used to measure the abundances of thousands of protein spots simultaneously. Usually, the protein abundances of two or more biological groups are compared using biological and technical replicates. After gel separation and staining, the spots are detected, spot volumes are quantified, and spots are matched across gels. There are almost always many missing values in the resulting data set. The missing values arise either because the corresponding proteins have very low abundances (or are absent) or because of experimental errors such as incomplete/over focusing in the first dimension or varying run times in the second dimension as well as faulty spot detection and matching. In this study, we show that the probability for a spot to be missing can be modeled by a logistic regression function of the logarithm of the volume. Furthermore, we present an algorithm that takes a set of gels with technical and biological replicates as input and estimates the average protein abundances in the biological groups from the number of missing spots and measured volumes of the present spots using a maximum likelihood approach. Confidence intervals for abundances and p-values for differential expression between two groups are calculated using bootstrap sampling. The algorithm is compared to two standard approaches, one that discards missing values and one that sets all missing values to zero. We have evaluated this approach in two different gel data sets of different biological origin. An F-program, implementing the algorithm, is freely available at httP://bioinfo.thep.lu.se/MissingValues2Dgels.html.},
  author       = {Krogh, Morten and Fernandez, Celine and Teilum, Maria and Bengtsson, Sofia and James, Peter},
  issn         = {1535-3893},
  keyword      = {missing values,maximum likelihood,2D-PAGE},
  language     = {eng},
  number       = {8},
  pages        = {3335--3343},
  publisher    = {The American Chemical Society},
  series       = {Journal of Proteome Research},
  title        = {A probabilistic treatment of the missing spot problem in 2D gel electrophoresis experiments},
  url          = {http://dx.doi.org/10.1021/pr070137p},
  volume       = {6},
  year         = {2007},
}