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

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A probabilistic treatment of the missing spot problem in 2D gel electrophoresis experiments. / Krogh, Morten; Fernandez, Celine; Teilum, Maria; Bengtsson, Sofia; James, Peter.

I: Journal of Proteome Research, Vol. 6, Nr. 8, 2007, s. 3335-3343.

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TY - JOUR

T1 - A probabilistic treatment of the missing spot problem in 2D gel electrophoresis experiments

AU - Krogh, Morten

AU - Fernandez, Celine

AU - Teilum, Maria

AU - Bengtsson, Sofia

AU - James, Peter

N1 - The information about affiliations in this record was updated in December 2015. The record was previously connected to the following departments: Computational biology and biological physics (000006113), Department of Immunotechnology (011029300), Laboratory for Experimental Brain Research (013041000), Molecular Endocrinology (013212018)

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

KW - missing values

KW - maximum likelihood

KW - 2D-PAGE

U2 - 10.1021/pr070137p

DO - 10.1021/pr070137p

M3 - Article

VL - 6

SP - 3335

EP - 3343

JO - Journal of Proteome Research

T2 - Journal of Proteome Research

JF - Journal of Proteome Research

SN - 1535-3893

IS - 8

ER -