Statistical challenges in the analysis of two-dimensional difference gel electrophoresis experiments using DeCyderTM

Fodor, Imola K.; Nelson, David; Alegria-Hartman, Michelle; Robbins, Kristin; Langlois, Richard G.; Turteltaub, Kenneth W.; Corzett, Todd H.; McCutchen-Maloney, Sandra L.

doi:10.1093/bioinformatics/bti612

Cited by 73 publications

(70 citation statements)

References 16 publications

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“…In biological study 1, to investigate the effect of a per2 knock-out mutation in mice, liver samples were harvested from per2 knock-out and wild type mice with synchronized circadian clocks and extracted 6 h after the onset of activity (circadian time 18) …”

Section: Methodsmentioning

confidence: 99%

Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-gel Electrophoresis

Karp

McCormick²,

Russell

et al. 2007

Molecular & Cellular Proteomics

152

137

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In quantitative proteomics, the false discovery rate (FDR) can be defined as the number of false positives within statistically significant changes in expression. False positives accumulate during the simultaneous testing of expression changes across hundreds or thousands of protein or peptide species when univariate tests such as the Student's t test are used. Currently most researchers rely solely on the estimation of p values and a significance threshold, but this approach may result in false positives because it does not account for the multiple testing effect. For each species, a measure of significance in terms of the FDR can be calculated, producing individual q values. The q value maintains power by allowing the investigator to achieve an acceptable level of true or false positives within the calls of significance. The q value approach relies on the use of the correct statistical test for the experimental design. In this situation, a uniform p value frequency distribution when there are no differences in expression between two samples should be obtained. Here we report a bias in p value distribution in the case of a three-dye DIGE experiment where no changes in expression are occurring. The bias was shown to arise from correlation in the data from the use of a common internal standard. With a two-dye schema, where each sample has its own internal standard, such bias was removed, enabling the application of the q value to two different proteomics studies. In the case of the first study, we demonstrate that 80% of calls of significance by the more traditional method are false positives. In the second, we show that calculating the q value gives the user control over the FDR. These studies demonstrate the power and ease of use of the q value in correcting for multiple testing. This work also highlights the need for robust experimental design that includes the appropriate application of statistical procedures. Molecular & Cellular Proteomics 6: 1354 -1364, 2007.Quantitative proteomics, the study of global changes in protein expression, is a rapidly growing field where two-dimensional (2D) 1 gel electrophoresis (1, 2), differential labeling of protein and peptides with stable isotopes (3), and label-free mass spectrometric peak intensity measurements (4) are pivotal approaches to measuring changes in the expression level of proteins. The output of large scale genomic sequencing and gene expression studies have driven the need to globally assess protein behavior, which is central to our understanding of cellular function and disease processes. In quantitative studies, the quantitation of the protein or peptide signal allows the comparison of protein levels from one state with another. Regardless of the technique used, the data generated can be analyzed with a variety of statistical approaches. In a simple comparison of two samples, changes in protein expression are considered to be significant when above a specified threshold (5). More rigorous studies incorporating replicates use univariate (1, 6) or multivari...

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Section: Methodsmentioning

confidence: 99%

Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-gel Electrophoresis

Karp

McCormick²,

Russell

et al. 2007

Molecular & Cellular Proteomics

152

137

View full text Add to dashboard Cite

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“…Dedicated algorithms have been developed, similar to those from microarrays, to eliminate dye influences [102][103][104]. Differentially expressed proteins are extracted by means of statistical tests, like t-test, ANOVA or cluster analysis [95,105].…”

Section: Modified Lysine Residuesmentioning

confidence: 99%

Protein stains for proteomic applications:Which, when, why?

Crawford²,

2006

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This review recollects literature data on sensitivity and dynamic range for the most commonly used colorimetric and fluorescent dyes for general protein staining, and summarizes procedures for the most common PTM-specific detection methods. It also compiles some important points to be considered in imaging and evaluation. In addition to theoretical considerations, examples are provided to illustrate differential staining of specific proteins with different detection methods. This includes a large body of original data on the comparative evaluation of several pre-and post-electrophoresis stains used in parallel on a single specimen, horse serum run in 2-DE (IPG-DALT). A number of proteins/protein spots are found to be over-or under-revealed with some of the staining procedures.

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“…FDR is used extensively in microarray transcript profiling experimentation and has also been used by a few groups to characterize proteome data sets. 9,31,34,35 In this paper, we applied FDR for selection of deferentially expressed spots. FDR is a statistical procedure that is effective in correcting for multiple simultaneous hypothesis tests.…”

Section: A Threementioning

confidence: 99%

Proteomic Analysis of Near-Isogenic Sunflower Varieties Differing in Seed Oil Traits

et al. 2007

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Near-isogenic sunflower lines containing 25% (inbred RHA280) and 48% (RHA801) oil by seed dry mass were comparatively analyzed in biological triplicate at 18 days after flowering using two-dimensional (both pI 3-10 and 4-7) Difference Gel Electrophoresis. Additionally, two inbred lines varying in oleic acid content, HA89 (18% oleic) and HA341 (89% oleic), were also analyzed in the same manner. Statistical analyses of these sunflower lines was performed beginning with fitting a mixed effects linear model to the log-transformed optical volume of each spot to account for gel variation, followed by testing the significance between varieties for mean transformed optical spot volumes. The p-values from the spot analysis procedures were then used to find the cutoff point for differential expression using a 10% false-discovery rate (FDR). Comparison of the oil content and oleic acid composition lines revealed 77 and 42 protein spots below the 10% FDR cutoff, respectively, and were therefore declared differentially expressed. Liquid chromatography-tandem mass spectrometry analysis of each of these protein spots resulted in assignments for 44 and 17 spots, respectively. Fructokinase, plastid phosphoglycerate kinase, and enolase proteins were determined to be up-regulated in the high oil line, while phosphofructokinase, cytosolic phosphoglucomutase, and cytsolic phosphoglycerate kinase were up-regulated in the low oil variety. Additionally, four activities involved in amino acid synthesis were up-regulated in the low oil variety in addition to 12S storage proteins and a protein similar to legumin storage protein. Interestingly, two 2-DE spots identified as 14-3-3 proteins were found to be up-regulated in high oleic acid variety. Alteration of glycolytic and amino acid biosynthetic enzymes, as well as storage protein levels, suggests seed oil content is tightly linked to carbohydrate metabolism and protein synthesis in a complex manner.

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Statistical challenges in the analysis of two-dimensional difference gel electrophoresis experiments using DeCyderTM

Cited by 73 publications

References 16 publications

Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-gel Electrophoresis

Experimental and Statistical Considerations to Avoid False Conclusions in Proteomics Studies Using Differential In-gel Electrophoresis

Protein stains for proteomic applications:Which, when, why?

Proteomic Analysis of Near-Isogenic Sunflower Varieties Differing in Seed Oil Traits

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