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. 2012;13 Suppl 16(Suppl 16):S12.
doi: 10.1186/1471-2105-13-S16-S12. Epub 2012 Nov 5.

1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data

Affiliations

1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data

Juergen Cox et al. BMC Bioinformatics. 2012.

Abstract

Quantitative proteomics now provides abundance ratios for thousands of proteins upon perturbations. These need to be functionally interpreted and correlated to other types of quantitative genome-wide data such as the corresponding transcriptome changes. We describe a new method, 2D annotation enrichment, which compares quantitative data from any two 'omics' types in the context of categorical annotation of the proteins or genes. Suitable genome-wide categories are membership of proteins in biochemical pathways, their annotation with gene ontology terms, sub-cellular localization, presence of protein domains or membership in protein complexes. 2D annotation enrichment detects annotation terms whose members show consistent behavior in one or both of the data dimensions. This consistent behavior can be a correlation between the two data types, such as simultaneous up- or down-regulation in both data dimensions, or a lack thereof, such as regulation in one dimension but no change in the other. For the statistical formulation of the test we introduce a two-dimensional generalization of the nonparametric two-sample test. The false discovery rate is stringently controlled by correcting for multiple hypothesis testing. We also describe one-dimensional annotation enrichment, which can be applied to single omics data. The 1D and 2D annotation enrichment algorithms are freely available as part of the Perseus software.

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Figures

Figure 1
Figure 1
Histogram of log protein intensities for all mouse proteins quantified in dendritic cells in Luber et al[3](blue). The green histogram indicates the ribosomal proteins within this distribution. They are significantly enriched at large values. Heights of the green bars were multiplied by five for better visibility.
Figure 2
Figure 2
Yeast protein ratios vs. mRNA ratios between the haploid and diploid populations from de Godoy et al[4]. The data points in red belong to the Gene Ontology (GO) biological process 'pheromone-dependent signal transduction during conjugation with cellular fusion'.
Figure 3
Figure 3
Schematic representation of the 2D annotation enrichment score. The score is a number pair inside the displayed rectangle. Significant terms will avoid a circular region around the origin. The green regions correspond to concordant up or down regulation. The blue regions correspond to terms that are up or down in one direction, but not in the other, while the terms in the red regions show anti-correlating behavior.
Figure 4
Figure 4
2D annotation enrichment based on the yeast protein and mRNA ratios displayed in Figure 2. 'Pheromone-dependent signal transduction' is located near the diagonal with positive values for both scores. 'Cell wall' has only a small mRNA score but a large protein score.
Figure 5
Figure 5
2D annotation enrichment for Comparative Genomic Hybridization (CGH) ratios (vertical) vs. protein ratios (horizontal) from Geiger et al[13]. Significant complexes, pathways and gene ontology terms are all distributed along the proteome change direction. Only the chromosome annotations have a major contribution in the vertical direction.
Figure 6
Figure 6
Parameter window of the 2D annotation enrichment in the Perseus software.

References

    1. Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature. 2003;422:198–207. - PubMed
    1. Ong SE. et al.Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics. 2002;1:376–386. - PubMed
    1. Luber CA et al. Quantitative proteomics reveals subset-specific viral recognition in dendritic cells. Immunity. pp. 279–289. - PubMed
    1. de Godoy LM. et al.Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature. 2008;455:1251–1254. - PubMed
    1. Cox J, Mann M. Is proteomics the new genomics? Cell. 2007;130:395–398. - PubMed