Multiscale Poisson process approaches for detecting and estimating differences from high-throughput sequencing assays-Reference-Cited by-同舟云学术

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Multiscale Poisson process approaches for detecting and estimating differences from high-throughput sequencing assays

Published:2024-09-01 Issue:3 Volume:18 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Shim Heejung¹,Xing Zhengrong²,Pantaleo Ester²,Luca Francesca³,Pique-Regi Roger⁴,Stephens Matthew⁵

Affiliation:

1. School of Mathematics and Statistics and Melbourne Integrative Genomics, University of Melbourne

2. Department of Statistics, University of Chicago

3. Department of Obstetrics and Gynecology and Center for Molecular Medicine and Genetics, Wayne State University

4. Center for Molecular Medicine and Genetics, Wayne State University

5. Department of Statistics and Human Genetics, University of Chicago

Publisher

Institute of Mathematical Statistics

Reference37 articles.

1. Lee, W. and Morris, J. S. (2016). Identification of differentially methylated loci using wavelet-based functional mixed models. Bioinformatics 32 664–672.

2. Morris, J. S., Brown, P. J., Herrick, R. C., Baggerly, K. A. and Coombes, K. R. (2008). Bayesian analysis of mass spectrometry proteomic data using wavelet-based functional mixed models. Biometrics 64 479–489, 667.

3. Donoho, D. L. and Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. J. Amer. Statist. Assoc. 90 1200–1224.

4. TIMMERMANN, K. E. and NOWAK, R. D. (1999). Multiscale modeling and estimation of Poisson processes with application to photon-limited imaging. IEEE Trans. Inf. Theory 45 846–862.

5. STEPHENS, M. (2017). False discovery rates: A new deal. Biostatistics 18 275–294.

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