Abstract
Integrative systems biology approaches build, evaluate, and combine data from thousands of diverse experiments. These strategies rely on methods that effectively identify and summarize gene-gene relationships within individual experiments. For gene-expression datasets, the Pearson correlation is often applied to build coexpression networks because it is both easily interpretable and quick to calculate. Here we develop and evaluate weighted Pearson correlation approaches that better summarize gene expression data into coexpression networks for synchronized cell cycle time-course experiments. These methods use experimental measurements of cell cycle synchrony to estimate appropriate weights through either sliding window or linear regression approaches. We show that these weights improve our ability to build coexpression networks capable of identifying phase-specific functional relationships between genes. We evaluate our method on diverse experiments and find that both weighted strategies outperform the traditional method. This weighted correlation approach is implemented in the Sleipnir library, an open source library used for integrative systems biology. Integrative approaches using properly weighted time-course experiments will provide a more detailed understanding of the processes studied in such experiments.
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References
Troyanskaya, O.G., Dolinski, K., Owen, A.B., Altman, R.B., Botstein, D.: A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proceedings of the National Academy of Sciences of the United States of America 100(14), 8348–8353 (2003)
Zhang, Z., Gerstein, M.: Reconstructing genetic networks in yeast. Nature Biotechnology 21(11), 1295–1297 (2003)
Lee, I., Date, S.V., Adai, A.T., Marcotte, E.M.: A probabilistic functional network of yeast genes. Science 306(5701), 1555–1558 (2004)
Myers, C.L., Troyanskaya, O.G.: Context-sensitive data integration and prediction of biological networks. Bioinformatics 23(17), 2322–2330 (2007)
Huttenhower, C., Haley, E.M., Hibbs, M.A., Dumeaux, V., Barrett, D.R., Coller, H.A., Troyanskaya, O.G.: Exploring the human genome with functional maps. Genome Research 19(6), 1093–1106 (2009)
Hess, D.C., Myers, C.L., Huttenhower, C., Hibbs, M.A., Hayes, A.P., Paw, J., Clore, J.J., Mendoza, R.M., Luis, B.S., Nislow, C., Giaever, G., Costanzo, M., Troyanskaya, O.G., Caudy, A.A.: Computationally driven, quantitative experiments discover genes required for mitochondrial biogenesis. PLoS Genetics 5(3), e1000407 (2009)
Hibbs, M.A., Myers, C.L., Huttenhower, C., Hess, D.C., Li, K., Caudy, A.A., Troyanskaya, O.G.: Directing experimental biology: a case study in mitochondrial biogenesis. PLoS Computational Biology 5(3), e1000322 (2009)
Wong, A.K., Park, C.Y., Greene, C.S., Bongo, L.A., Guan, Y., Troyanskaya, O.G.: IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic Acids Research 40(Web Server issue), W484–W490 (2012)
IMP: Integrative multi-species prediction (October 2012), http://imp.princeton.edu/networks/data/
Bar-Joseph, Z., Siegfried, Z., Brandeis, M., Brors, B., Lu, Y., Eils, R., Dynlacht, B.D., Simon, I.: Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells. Proceedings of the National Academy of Sciences of the United States of America 105(3), 955–960 (2008)
Cho, R.J., Huang, M., Campbell, M.J., Dong, H., Steinmetz, L., Sapinoso, L., Hampton, G., Elledge, S.J., Davis, R.W., Lockhart, D.J.: Transcriptional regulation and function during the human cell cycle. Nature Genetics 27(1), 48–54 (2001)
Sadasivam, S., Duan, S., DeCaprio, J.A.: The MuvB complex sequentially recruits B-Myb and FoxM1 to promote mitotic gene expression. Genes & Development 26(5), 474–489 (2012)
Whitfield, M.L., Sherlock, G., Saldanha, A.J., Murray, J.I., Ball, C.A., Alexander, K.E., Matese, J.C., Perou, C.M., Hurt, M.M., Brown, P.O., Botstein, D.: Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Molecular Biology of the Cell 13(6), 1977–2000 (2002)
Grant, G.D., Gamsby, J., Martyanov, V., Brooks, L., George, L.K., Mahoney, J.M., Loros, J.J., Dunlap, J.C., Whitfield, M.L.: Live-cell monitoring of periodic gene expression in synchronous human cells identifies Forkhead genes involved in cell cycle control. Molecular Biology of the Cell 23(16), 3079–3093 (2012)
Yeom, M., Pendergast, J.S., Ohmiya, Y., Yamazaki, S.: Circadian-independent cell mitosis in immortalized fibroblasts. Proceedings of the National Academy of Sciences of the United States of America 107(21), 9665–9670 (2010)
Nowrousian, M., Duffield, G.E., Loros, J.J., Dunlap, J.C.: The frequency gene is required for temperature-dependent regulation of many clock-controlled genes in Neurospora crassa. Genetics 164(3), 923–933 (2003)
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 9(12), 3273–3297 (1998)
Langmead, C.J., Yan, A.K., McClung, C.R., Donald, B.R.: Phase-independent rhythmic analysis of genome-wide expression patterns. Journal of Computational Biology: A Journal of Computational Molecular Cell Biology 10(3-4), 521–536 (2003)
Johansson, D., Lindgren, P., Berglund, A.: A multivariate approach applied to microarray data for identification of genes with cell cycle-coupled transcription. Bioinformatics 19(4), 467–473 (2003)
Wichert, S., Fokianos, K., Strimmer, K.: Identifying periodically expressed transcripts in microarray time series data. Bioinformatics 20(1), 5–20 (2003)
Straume, M.: DNA microarray time series analysis: automated statistical assessment of circadian rhythms in gene expression patterning. Methods in Enzymology 383, 149–166 (2004)
Chen, J.: Identification of significant periodic genes in microarray gene expression data. BMC Bioinformatics 16(1), 286 (2005)
Fan, X., Pyne, S., Liu, J.S.: Bayesian meta-analysis for identifying periodically expressed genes in fission yeast cell cycle. The Annals of Applied Statistics 4(2), 988–1013 (2010)
Johnson, D.G., Ohtani, K., Nevins, J.R.: Autoregulatory control of E2F1 expression in response to positive and negative regulators of cell cycle progression. Genes & Development 8(13), 1514–1525 (1994)
Alibés, A., Yankilevich, P., Cañada, A., DÃaz-Uriarte, R.: IDconverter and IDClight: conversion and annotation of gene and protein IDs. BMC Bioinformatics 8(1), 9 (2007)
Myers, C.L., Barrett, D.R., Hibbs, M.A., Huttenhower, C., Troyanskaya, O.G.: Finding function: evaluation methods for functional genomic data. BMC Genomics 7, 187 (2006)
Huttenhower, C., Schroeder, M., Chikina, M.D., Troyanskaya, O.G.: The Sleipnir library for computational functional genomics. Bioinformatics 24(13), 1559–1561 (2008)
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Tan, J., Grant, G.D., Whitfield, M.L., Greene, C.S. (2013). Time-Point Specific Weighting Improves Coexpression Networks from Time-Course Experiments. In: Vanneschi, L., Bush, W.S., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2013. Lecture Notes in Computer Science, vol 7833. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37189-9_2
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DOI: https://doi.org/10.1007/978-3-642-37189-9_2
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