Independent component analysis of 2‐D electrophoresis gels

Safavi, Haleh; Correa, Nicolle M.; Xiong, Wei; Korostyshevskiy, Valeriy R.; Whisnant, Carol C.; Seillier-Moiseiwitsch, Françoise

doi:10.1002/elps.200800028

Cited by 11 publications

(12 citation statements)

References 15 publications

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“…However, the difference is possibly different when female rats are compared with male control rats as opposed to male treatment rats. A similar observation which points toward the interaction between gender and treatment was made in Safavi et al (). In Safavi et al (), this interaction in MT group was observed in the display of the expression profile of independent component analysis (ICA).…”

Section: Results For Synthetic Gelssupporting

confidence: 83%

“…A similar observation which points toward the interaction between gender and treatment was made in Safavi et al (). In Safavi et al (), this interaction in MT group was observed in the display of the expression profile of independent component analysis (ICA). However, the observation was merely qualitative, based on diagnostic plots.…”

Section: Results For Synthetic Gelssupporting

confidence: 83%

“…To check the consistency of our methodology, we use two scenarios of background noises: low level and high level. The low noise case follow the noise settings of Safavi et al (), while for the high level, the noise is increased by a factor of 10.…”

Section: Methodsmentioning

confidence: 97%

“…Figure (a) and Figure (b) give an example of differences we build in FT group from MC group. Detailed experimental design of the simulated gels follows Safavi et al ().…”

Section: Methodsmentioning

confidence: 99%

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Bootstrap testing of 2D electrophoresis gels across groups

Huang

Correa

2012

Stat

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Statistical procedures are often used to pre-screen spots that may be differentially expressed across groups of 2-dimensional electrophoresis (2DE) gels. Commonly used inference procedures are univariate and have poor detection capabilities due to their reliance on unrealistic assumptions and result in a large number of false positives, thereby reducing the efficiency and costing effectiveness of the statistical pre-screening procedures. We develop a data-driven statistical approach that provides accurate identification of statistically significant differences in protein expression profiles across groups. Our methodology relies on a spatial bootstrap technique, and we develop a novel testing procedure using modified Kolmogorov-Smirnov type statistic that is appropriate for spatial dependence in data such as in gel images. Simulation study based on synthetic gels shows that the testing procedure has high efficiency in detecting group differences. The methodology is illustrated via a set of real 2DE gels.

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Section: Results For Synthetic Gelssupporting

confidence: 83%

Section: Results For Synthetic Gelssupporting

confidence: 83%

Section: Methodsmentioning

confidence: 97%

“…Figure (a) and Figure (b) give an example of differences we build in FT group from MC group. Detailed experimental design of the simulated gels follows Safavi et al ().…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Bootstrap testing of 2D electrophoresis gels across groups

Huang

Correa

2012

Stat

Self Cite

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“…In this field, two-dimensional gel electrophoresis (2-DE) is a well-established and widely used technique to separate proteins extracted from sample for identification and analysis of differential expression, according to their isoelectric points and molecular weight [1]. The result of that process is many dark spots on the gel, and each spot represents a protein or a group of proteins.…”

Section: Introductionmentioning

confidence: 99%

A Laplacian of Gaussian-Based Approach for Spot Detection in Two-Dimensional Gel Electrophoresis Images

Xiong

Sun

et al. 2011

Computer and Computing Technologies in Agriculture IV

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Abstract. Two-dimension gel electrophoresis (2-DE) is a proteomic technique that allows the analysis of protein profiles expressed in a given cell, tissue or biological system at a given time. The 2-DE images depict protein as spots of various intensities and sizes. Due to the presence of noise, the inhomogeneous background, and the overlap between the spots in 2-DE image, the protein spot detection is not a straightforward process. In this paper, we present an improved protein spot detection approach, which is based on Laplacian of Gaussian algorithm, and we extract the regional maxima by morphological grayscale reconstruction algorithm, which can reduce the impact of noisy and background in spot detection. Experiments on real 2-DE images show that the proposed approach is more reliable, precise and less sensitive to noise than the traditional Laplacian of Gaussian algorithm and it offers a good performance in our gel image analysis software.

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Image analysis tools and emerging algorithms for expression proteomics

et al. 2010

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Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-D Gel Electrophoresis (2-DE) technique of protein separation, and by first covering signal analysis for Mass Spectrometry (MS), we also explain the current image analysis workflow for the emerging high-throughput ‘shotgun’ proteomics platform of Liquid Chromatography coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whilst existing commercial and academic packages and their workflows are described from both a user’s and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.

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Independent component analysis of 2‐D electrophoresis gels

Cited by 11 publications

References 15 publications

Bootstrap testing of 2D electrophoresis gels across groups

Bootstrap testing of 2D electrophoresis gels across groups

A Laplacian of Gaussian-Based Approach for Spot Detection in Two-Dimensional Gel Electrophoresis Images

Image analysis tools and emerging algorithms for expression proteomics

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