The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data
Open accessAbstract
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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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855
Self-citationMapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
Self-citationKEGG for representation and analysis of molecular networks involving diseases and drugs
Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., & Hirakawa, M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp896 · Cited by 2,322
Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast
de Godoy, L. M. F., Olsen, J. V., Cox, J., Nielsen, M. L., Hubner, N. C., Fröhlich, F., Walther, T. C., & Mann, M. (2008) · Nature · doi:10.1038/nature07341 · Cited by 873
Self-citationQuantitative proteomics reveals subset-specific viral recognition in dendritic cells
Luber, C. A., Cox, J., Lauterbach, H., Fancke, B., Selbach, M., Tschopp, J., Akira, S., Wiegand, M., Hochrein, H., O'Keeffe, M., et al. (2010) · Immunity · doi:10.1016/j.immuni.2010.01.013 · Cited by 576
Self-citationPan, C., Kumar, C., Bohl, S., Klingmueller, U., & Mann, M. (2009) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m800258-mcp200 · Cited by 561 · cited 1×
“When applied to ratios of protein abundances the method described here is similar to the quantile-based enrichment calculations introduced by Pan et al. [29] There the distribution of protein ratios was subdivided into bins and then all categories were tested for being enriched in these bins.”
MethodSelf-citationDefining the transcriptome and proteome in three functionally different human cell lines
Lundberg, E., Fagerberg, L., Klevebring, D., Matic, I., Geiger, T., Cox, J., Älgenäs, C., Lundeberg, J., Mann, M., & Uhlen, M. (2010) · Molecular Systems Biology · doi:10.1038/msb.2010.106 · Cited by 373
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The Perseus computational platform for comprehensive analysis of (prote)omics data
Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger, T., Mann, M., & Cox, J. (2016) · Nature Methods · doi:10.1038/nmeth.3901 · Cited by 8,803
Self-citationL-Arginine Modulates T Cell Metabolism and Enhances Survival and Anti-tumor Activity
Geiger, R., Rieckmann, J. C., Wolf, T., Basso, C., Feng, Y., Fuhrer, T., Kogadeeva, M., Picotti, P., Meissner, F., Mann, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.031 · Cited by 1,775
Self-citationKulak, N. A., Pichler, G., Paron, I., Nagaraj, N., & Mann, M. (2014) · Nature Methods · doi:10.1038/nmeth.2834 · Cited by 1,719
Self-citationPlasma Proteome Profiling to Assess Human Health and Disease
Geyer, P. E., Kulak, N. A., Pichler, G., Holdt, L. M., Teupser, D., & Mann, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.02.015 · Cited by 734
Self-citationAn atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics
Angelidis, I., Simon, L. M., Fernandez, I. E., Strunz, M., Mayr, C. H., Greiffo, F. R., Tsitsiridis, G., Ansari, M., Graf, E., Strom, T.-M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08831-9 · Cited by 637
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Metadata
- Authors
- Cox, J., & Mann, M.
- Venue
- BMC Bioinformatics
- Published
- 2012-11
- Volume
- 13
- Issue
- S16
- Pages
- S12-S12
- ISSN
- 1471-2105 (ISSN-L)
- OpenAlex
- W2124091237
- Cited by
- 626
- References
- 10
- FWCI
- 9.99
- Type
- journal article
- Language
- en
- Open access
- gold
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