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Modeling Species Specific Gene Expression Across Multiple Regions in the Brain

  • Liyang Diao
  • Ying Zhu
  • Nenad Sestan
  • Hongyu ZhaoEmail author
Chapter
  • 47 Downloads
Part of the Emerging Topics in Statistics and Biostatistics book series (ETSB)

Abstract

Motivation: The question of what makes the human brain functionally different from that of other closely related primates, such as the chimpanzee, has both philosophical as well as practical implications. One of the challenges faced with such studies, however, is the small sample size available. Furthermore, expression values for multiple brain regions have an inherent structure that is generally ignored in published studies.

Results: We present a new statistical approach to identify genes with species specific expression, that (1) avoids multiple pairwise comparisons, which can be susceptible to small changes in expression as well as intransitivity, and (2) pools information across related data sets when available to produce more robust results, such as in the case of gene expression across multiple brain regions. We demonstrate through simulations that our model can much better identify human specific genes than the naive approach. Applications of the model to two previously published data sets, one microarray and one RNA-Seq, suggest a moderately large benefit from our model. We show that our approach produces more robust gene classifications across regions, and greatly reduces the number of human specific genes previously reported, which we show were primarily due to the noise in the underlying data.

Keywords

Gene expression R code Posterior probabilities Markov random field RNA sequencing Akaike Bayes 

Notes

Acknowledgements

We would like to thank Zhixiang Lin, for discussion of the Markov random field model and its applications.

Funding LD was supported by the National Library of Medicine Informatics training grant. HZ was supported in part by NIH R01 GM59507.

References

  1. 1.
    Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.). Second International Symposium on Information Theory (pp. 267–281). Budapest: Akadémiai Kiado.Google Scholar
  2. 2.
    Anders, S., & Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biology, 11(10).Google Scholar
  3. 3.
    Besag, J. (1986). On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, 48(3), 259–302.MathSciNetzbMATHGoogle Scholar
  4. 4.
    Cáceres, M., Lachuer, J., Zapala, M. A., Redmond, J. C., Kudo, L., Geschwind, D. H., et al. (2003). Elevated gene expression levels distinguish human from non-human primate brains. Proceedings of the National Academy of Sciences of the United States of America, 100(22), 13030–13035. ISSN 0027-8424.  https://doi.org/10.1073/pnas.2135499100 CrossRefGoogle Scholar
  5. 5.
    Celeux, G., Forbes, F., & Peyrard, N. (2003). EM procedures using mean field-like approximations for Markov model-based image segmentation. Pattern Recognition, 36, 131–144.CrossRefGoogle Scholar
  6. 6.
    Dayton, C. M. (1998). Information criteria for the paired-comparisons problem. The American Statistician, 52(2), 144–151.Google Scholar
  7. 7.
    Dayton, C. M. (2003). Information criteria for pairwise comparisons. Psychological Methods, 8(1), 61–71.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Delmar, P., Robin, S., Daudin, J., Delmar, P., Robin, S., & Daudin, J. (2005). VarMixt: Efficient variance modelling for the differential analysis of replicated gene expression data. ISSN 1367-4803.  https://doi.org/10.1093/bioinformatics/bti023
  9. 9.
    Enard, W., Khaitovich, P., Klose, J., Zollner, S., Hessig, F., Giavalisco, P., et al. (2002). Intra- and interspecific variation in primate gene expression patterns. Science, New York, NY, 296(5566), 340–343. ISSN 0036-8075.  https://doi.org/10.1126/science.1068996 CrossRefGoogle Scholar
  10. 10.
    Florence, J., Guillemette, M., Séverine, D., Isabelle, H., & Jean-Louis, F. (2007). A structural mixed model for variances in differential gene expression studies. p. 19. ISSN 0016-6723. https://doi.org/10.1017/S0016672307008646
  11. 11.
    Hurvich, C. M., & Tsai, C.-L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297–307.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Jeanmougin, M., de Reynies, A., Marisa, L., Paccard, C., Nuel, G., & Guedj, M. (2010). Should we abandon the t-test in the analysis of gene expression microarray data: A comparison of variance modeling strategies. Plos One, 5(9), e12336.  https://doi.org/10.1371/journal.pone.0012336 CrossRefGoogle Scholar
  13. 13.
    Khaitovich, P., Muetzel, B., She, X., Lachmann, M., Hellmann, I., Dietzsch, J., et al. (2004). Regional patterns of gene expression in human and chimpanzee brains. Genome Research, 14(8), 1462–1473. ISSN 1088-9051. https://doi.org/10.1101/gr.2538704 CrossRefGoogle Scholar
  14. 14.
    Konopka, G., Friedrich, T., Davis-Turak, J., Winden, K., Oldham, M. C., Gao, F., et al. (2012). Human-specific transcriptional networks in the brain. Neuron, 75(4), 601–617. https://doi.org/10.1016/j.neuron.2012.05.034. http://www.ncbi.nlm.nih.gov/pubmed/22920253
  15. 15.
    Li, H., Wei, Z., & Maris, J. (2009). A hidden Markov random field model for genome-wide association studies. Biostatistics, 11(1), 139–150.  https://doi.org/10.1093/biostatistics/kxp043 CrossRefGoogle Scholar
  16. 16.
    Lin, Z., Li, M., Sestan, N., & Zhao, H. (2016). A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data. Statistical Applications in Genetics and Molecular Biology.  https://doi.org/10.1515/sagmb-2015-0070. http://www.ncbi.nlm.nih.gov/pubmed/26926866
  17. 17.
    Lin, Z., Sanders, S. J., Li, M., Sestan, N., State, M. W., & Zhao, H. (2015). A Markov random field-based approach to characterizing human brain development using spatial-temporal transcriptome data. Annals of Applied Statistics, 9(1), 429–451. https://doi.org/10.1214/14-AOAS802. http://www.ncbi.nlm.nih.gov/pubmed/26877824
  18. 18.
    Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.CrossRefGoogle Scholar
  19. 19.
    McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research, 40(10), 4288–4297.CrossRefGoogle Scholar
  20. 20.
    Oshlack, A., Chabot, A. E., Smyth, G. K., & Gilad, Y. (2007). Using DNA microarrays to study gene expression in closely related species. Methods of Biochemical Analysis, 23(10), 1235–1242. ISSN 1367-4803.  https://doi.org/10.1093/bioinformatics/btm111 Google Scholar
  21. 21.
    Robinson, M. D., & Smyth, G. K. (2008). Small-sample estimation of negative binomial dispersion, with applications to sage data. Biostatistics, 9(2), 321–332.CrossRefGoogle Scholar
  22. 22.
    Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464, 03.  https://doi.org/10.1214/aos/1176344136. http://dx.doi.org/10.1214/aos/1176344136
  23. 23.
    Semendeferi, K., Teffer, K.,Buxhoeveden, D. P., Park, M. S., Bludau, S., Amunts, K., et al. (2011). Spatial organization of neurons in the frontal pole sets humans apart from great apes. Cerebral Cortex, 21(7),1485–1497.  https://doi.org/10.1093/cercor/bhq191 CrossRefGoogle Scholar
  24. 24.
    Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, 3(1), 1–25. ISSN 1544-6115. https://doi.org/10.2202/1544-6115.1027 MathSciNetCrossRefGoogle Scholar
  25. 25.
    Tsujimoto, S., Genovesio, A., & Wise, S. P. (2010). Evaluating self-generated decisions in frontal pole cortex of monkeys. Nature Neuroscience, 13(1), 120–126. ISSN 1097-6256. https://doi.org/10.1038/nn.2453 CrossRefGoogle Scholar
  26. 26.
    Varki, A. (2000). A chimpanzee genome project is a biomedical imperative. Genome Research, 10(8), 1065-1070.CrossRefGoogle Scholar
  27. 27.
    Varki, A., & Altheide, T. K. (2005). Comparing the human and chimpanzee genomes: Searching for needles in a haystack. Genome Research, 15(12), 1746–1758. ISSN 1088-9051. https://doi.org/10.1101/gr.3737405 CrossRefGoogle Scholar
  28. 28.
    Wei, Z., & Li, H. (2008). A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data. Annals of Applied Statistics, 2(1), 408–429. https://doi.org/10.1214/07-AOAS145 MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Liyang Diao
    • 1
  • Ying Zhu
    • 1
    • 2
  • Nenad Sestan
    • 2
  • Hongyu Zhao
    • 3
    Email author
  1. 1.Department of BiostatisticsYale UniversityNew HavenUSA
  2. 2.Department of NeuroscienceYale UniversityNew HavenUSA
  3. 3.Department of Biostatistics, Program in Computational Biology and BioinformaticsYale UniversityNew HavenUSA

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