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Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions. (English) Zbl 1543.62585

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis

Software:

fda (R)

References:

[1] Aitkin, M. (1991) Posterior Bayes factors. Journal of the Royal Statistical Society: Series B (Methodological), 53(1), 111-128. · Zbl 0800.62167
[2] Aitkin, M. (1998) Simpson’s paradox and the Bayes factor. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 60(1), 269-270. · Zbl 0909.62001
[3] Berry, S.M., Carroll, R.J. & Ruppert, D. (2002) Functional data analysis and mixed effect models. Journal of the American Statistical Association, 97, 160-169. · Zbl 1073.62524
[4] Binder, A.M., LaRocca, J., Lesseur, C., Marsit, C.J. & Michels, K.B. (2015) Epigenome‐wide and transcriptome‐wide analyses reveal gestational diabetes is associated with alterations in the human leukocyte antigen complex. Clinical Epigenetics, 7(1), 1-12.
[5] Bjaanæs, M.M., Fleischer, T., Halvorsen, A.R., Daunay, A., Busato, F., Solberg, S., Jørgensen, L., Kure, E., Edvardsen, H., Børresen‐Dale, A.‐L., Brustugun, O.T., Tost, J., Kristensen, V. & Helland, Å. (2016) Genome‐wide DNA methylation analyses in lung adenocarcinomas: association with EGFR, KRAS and TP53 mutation status, gene expression and prognosis. Molecular Oncology, 10(2), 330-343.
[6] Boker, S.M., Rotondo, J.L., Xu, M. & King, K. (2002) Windowed cross‐correlation and peak picking for the analysis of variability in the association between behavioral time series. Psychological Methods, 7(3), 338.
[7] Denault, W.R. & Jugessur, A. (2021) Detecting differentially methylated regions using a fast wavelet‐based approach to functional association analysis. BMC Bioinformatics, 22, 1-15.
[8] Du, P., Zhang, X., Huang, C.‐C., Jafari, N., Kibbe, W.A., Hou, L. & Lin, S.M. (2010) Comparison of beta‐value and M‐value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics, 11(1), 1-9.
[9] Eckhardt, F., Lewin, J., Cortese, R., Rakyan, V.K., Attwood, J., Burger, M., Burton, J., Cox, T.V., Davies, R., Down, T.A., Haefliger, C., Horton, R., Howe, K., Jackson, D.K., Kunde, J., Koenig, C., Liddle, J., Niblett, D., Otto, T., Pettett, R., Seemann, S., Thompson, C., West, T., Rogers, J., Olek, A., Berlin, K. & Beck, S. (2006) DNA methylation profiling of human chromosomes 6, 20 and 22. Nature Genetics, 38(12), 1378-1385.
[10] Eubank, R.L. (1999) Nonparametric regression and spline smoothing. CRC Press. · Zbl 0936.62044
[11] Hastie, T. & Tibshirani, R. (1990) Generalized additive models. New York: Chapman and Hall. · Zbl 0747.62061
[12] Higdon, D., Kennedy, M., Cavendish, J.C., Cafeo, J.A. & Ryne, R.D. (2004) Combining field data and computer simulations for calibration and prediction. SIAM Journal on Scientific Computing, 26(2), 448-466. · Zbl 1072.62018
[13] Jaffe, A.E., Feinberg, A.P., Irizarry, R.A. & Leek, J.T. (2012) Significance analysis and statistical dissection of variably methylated regions. Biostatistics, 13(1), 166-178.
[14] Jaffe, A.E., Murakami, P., Lee, H., Leek, J.T., Fallin, M.D., Feinberg, A.P. & Irizarry, R.A. (2012) Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. International Journal of Epidemiology, 41(1), 200-209.
[15] Jiang, Y. & Xu, X. (2022) A two‐sample test of high‐dimensional means based on posterior Bayes factor. Mathematics, 10(10), 1741.
[16] Kass, R.E. & Raftery, A.E. (1995) Bayes factors. Journal of the American Statistical Association, 90(430), 773-795. · Zbl 0846.62028
[17] Keele, L.J. (2008) Semiparametric regression for the social sciences. John Wiley & Sons. · Zbl 1144.62109
[18] Lando, M., Fjeldbo, C.S., Wilting, S.M., Snoek, B.C., Aarnes, E.‐K., Forsberg, M.F., Kristensen, G.B., Steenbergen, R.D. & Lyng, H. (2015) Interplay between promoter methylation and chromosomal loss in gene silencing at 3p11‐p14 in cervical cancer. Epigenetics, 10(10), 970-980.
[19] Li, D., Xie, Z., Le Pape, M. & Dye, T. (2015) An evaluation of statistical methods for DNA methylation microarray data analysis. BMC Bioinformatics, 16(1), 1-20.
[20] Li, Y., Chen, J.A., Sears, R.L., Gao, F., Klein, E.D., Karydas, A., Geschwind, M.D., Rosen, H.J., Boxer, A.L., Guo, W., Pellegrini, M., Horvath, S., Miller, B.L., Geschwind, D.H. & Coppola, G. (2014) An epigenetic signature in peripheral blood associated with the haplotype on 17q21. 31, a risk factor for neurodegenerative tauopathy. PLOS Genetics, 10(3), e1004211.
[21] Liang, F. (2002) Dynamically weighted importance sampling in Monte Carlo computation. Journal of the American Statistical Association, 97(459), 807-821. · Zbl 1058.65006
[22] Limbach, M., Saare, M., Tserel, L., Kisand, K., Eglit, T., Sauer, S., Axelsson, T., Syvänen, A.‐C., Metspalu, A., Milani, L. & Peterson, P. (2016) Epigenetic profiling in CD4+ and CD8+ T cells from Graves’ disease patients reveals changes in genes associated with T cell receptor signaling. Journal of Autoimmunity, 67, 46-56.
[23] Liu, J.S. & Liu, J.S. (2001) Monte Carlo strategies in scientific computing, vol. 75. Springer. · Zbl 0991.65001
[24] Ma, P., Zhang, N., Huang, J.Z. & Zhong, W. (2017) Adaptive basis selection for exponential family smoothing splines with application in joint modeling of multiple sequencing samples. Statistica Sinica, 27(4), 1757-1777. · Zbl 1392.62324
[25] Mallik, S., Odom, G.J., Gao, Z., Gomez, L., Chen, X. & Wang, L. (2019) An evaluation of supervised methods for identifying differentially methylated regions in illumina methylation arrays. Briefings in Bioinformatics, 20(6), 2224-2235.
[26] Pedersen, B.S., Schwartz, D.A., Yang, I.V. & Kechris, K.J. (2012) Comb‐p: software for combining, analyzing, grouping and correcting spatially correlated P‐values. Bioinformatics, 28(22), 2986-2988.
[27] Peters, T.J., Buckley, M.J., Statham, A.L., Pidsley, R., Samaras, K., V Lord, R., Clark, S.J. & Molloy, P.L. (2015) De novo identification of differentially methylated regions in the human genome. Epigenetics & Chromatin, 8(1), 1-16.
[28] Qin, G. & Zhu, Z. (2009) Local asymptotic behavior of regression splines for marginal semiparametric models with longitudinal data. Science in China Series A: Mathematics, 52(9), 1982-1994. · Zbl 1191.62075
[29] Ramsay, J.O. & Silverman, B.W. (2005) Functional data analysis. Springer. · Zbl 1079.62006
[30] Robinson, M.D., Kahraman, A., Law, C.W., Lindsay, H., Nowicka, M., Weber, L.M. & Zhou, X. (2014) Statistical methods for detecting differentially methylated loci and regions. Frontiers in Genetics, 5, 324.
[31] Ryu, D., Li, E. & Mallick, B.K. (2011) Bayesian nonparametric regression analysis of data with random effects covariates from longitudinal measurements. Biometrics, 67(2), 454-466. · Zbl 1217.62054
[32] Ryu, D., Liang, F. & Mallick, B.K. (2013) Sea surface temperature modeling using radial basis function networks with a dynamically weighted particle filter. Journal of the American Statistical Association, 108(501), 111-123. · Zbl 06158329
[33] Ryu, D., Xu, H., George, V., Su, S., Wang, X., Shi, H. & Podolsky, R.H. (2016) Differential methylation tests of regulatory regions. Statistical Applications in Genetics and Molecular Biology, 15(3), 237-251. · Zbl 1343.92019
[34] Silverman, B.W. & Ramsay, J.O. (2001) Functional data analysis. International Encyclopedia of the Social and Behavioral Sciences. Amsterdam: Elsevier. · Zbl 1079.62006
[35] Wang, Z. & Xu, X. (2021) Testing high‐dimensional covariance matrices via posterior Bayes factor. Journal of Multivariate Analysis, 181, 104674. · Zbl 1461.62236
[36] West, S.G. & Hepworth, J.T. (1991) Statistical issues in the study of temporal data: daily experiences. Journal of Personality, 59(3), 609-662.
[37] Yue, Y.R., Speckman, P.L. & Sun, D. (2012) Priors for Bayesian adaptive spline smoothing. Annals of the Institute of Statistical Mathematics, 64(3), 577-613. · Zbl 1237.62037
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