BSP: Iterative-testing procedure for identifying communities in bipartite correlation networks
Finding Stable Groups of Cross-Correlated Features in Multi-View data, M. Dewaskar, J, Palowitch, M. He, M.I. Love, A.B. Nobel, arXiv:2009.05079, 2020
HT-eQTL: Hierarchical Bayes method for multi-tissue expression quantitative trait loci (eQTL) analysis in a large number of tissues
HT-eQTL: integrative expression quantitative trait loci analysis in a large number of human tissues, G. Li, D. Jima, F.A. Wright, A.B. Nobel, BMC Bioinformatics, 2018
MT-eQTL: Empirical Bayes method for multi-tissue expression quantitative trait loci (eQTL) analysis
An empirical Bayes approach for multiple tissue eQTL analysis, G. Li, A.A. Shabalin, I. Rusyn, F.A. Wright, A.B. Nobel, Biostatistics, 2018
DCM: Method for finding sets of differentially correlated variable sets in case-control data
A testing-based approach to the discovery of differentially correlated variable sets, K. Bodwin, K. Zhang, and A.B. Nobel, https://arxiv.org/abs/1509.08124v2.
ESSC: Community detection procedure for unweighted networks
A testing based extraction algorithm for identifying significant communities in networks, J.D. Wilson, S. Wang, P.J. Mucha, S. Bhamidi, and A.B. Nobel, Annals of Applied Statistics vol.8, No. 3, 1853-1891, 2015.
SigClust: Permutation based method for assessing the significance of clustering results
Statistical significance of clustering for high-dimension, low-sample size data, Y. Liu, D.N. Hayes, A.B. Nobel and J.S. Marron, Journal of the American Statistical Association, vol.103 pp.1281-1293, 2008.
Statistical significance of clustering using soft thresholding, H. Huang, Y. Liu, M. Yuan, and J. S. Marron, arXiv:1305.5879v2, 2013.
JIVE: Expresses multiple data matrices on a common set of samples into a sum of low-to-moderate rank matrices capturing joint and individual variation
Joint and Individual Variation Explained (JIVE) for Integrated Analysis of Multiple Datatypes, E.F. Lock, K.A. Hoadley, J.S. Marron and A.B. Nobel, Annals of Applied Statistics, vol.7, 523-542, 2013.
Non-iterative Joint and Individual Variation Explained, Q. Feng, J. Hannig, J.S. Marron, arXiv:1512.04060.
LAS: Biclustering algorithm that finds significant, large average submatrices in high dimensional data
Finding Large Average Submatrices in high dimensional data, A.A. Shabalin, V.J. Weigman, C.M. Perou and A.B. Nobel, Annals of Applied Statistics, vol.3, pp.985-1012, 2009.
XDE: Bayesian method for assessing the differential expression of genes using data from multiple studies
A Bayesian model for cross-study differential gene expression (with discussion), R.B. Scharpf, H. Tjelmeland, G. Parmigiani and A.B. Nobel, Journal of the American Statistical Association, vol.104, pp.1295-1310, 2009.
XPN: Cross-platform normalization method for combining gene expression data from different studies
Merging two gene expression studies via cross platform normalization, A.A. Shabalin, H. Tjelmeland, C. Fan, C.M. Perou and A.B. Nobel, Bioinformatics, vol.24, pp.1154-1160, 2008.
FastMap: Fast method for performing eQTL analyses in homozygous populations, including permutation based assessment of significance
FastMap: Fast eQTL mapping in homozygous populations, D.M. Gatti, A.A. Shabalin, T-C. Lam, F.A. Wright, I. Rusyn and A.B. Nobel, Bioinformatics, vol.25, pp.482-489, 2008.
SAFE: Permutation based procedure for assessing the significance of functional categories in gene expression data
Significance analysis of functional categories in gene expression studies: a structured permutation approach, W.T. Barry, A.B. Nobel and F.A. Wright, Bioinformatics, 21:1943-1949, 2005.
ChIPOTle: Peak finding algorithm for ChIP-chip microarray data
ChIPOTle: A user-friendly tool for the analysis of ChIP-chip data, M.J. Buck, A.B. Nobel and J.D. Lieb, Genome Biology, 6:R97, 2005.