Zuo, C. & Keles, S. A statistical framework for power calculations in ChIP seq experiments.
Flutre, T., Wen, X., Pritchard, J. & Stephens, M. A statistical framework for joint eQTL analysis in multiple tissues.
Hu, Y, Li, M, Lu, Q, Weng, H, Wang, J, Zekavat, SM, Yu, Z, Li, B, Gu, J, Muchnik, S, Shi, Y, Kunkle, BW, Mukherjee, S, Natarajan, P, Naj, A, Kuzma, A, Zhao, Y, Crane, PK, Alzheimer's Disease Genetics Consortium,,, Lu, H, and Zhao, H. "A statistical framework for cross-tissue transcriptome-wide association analysis". Nature Genetics 51, no. 3 (March 2019): 568-576.
Moreover, a probabilistic model provides a statistical framework for inference.
Gong, T. & Szustakowski, J. D. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data.
Gong, T. & Szustakowski, J.D. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-seq data.
A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.
This paper presents a statistical framework for assessing wireless systems performance using hierarchical data mining techniques.
We formalize the approach and propose a statistical framework for two-way clustering.
Lanckriet, G. R. G., Bie, T. D., Cristianini, N., Jordan, M. I. & Noble, W. S. A statistical framework for genomic data fusion.
Here we provide a statistical framework for the analysis of excesses in de novo mutation per gene and gene set by calibrating a model of de novo mutation.
