Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies
暂无分享,去创建一个
[1] Jingyuan Fu,et al. Genetical Genomics: Spotlight on QTL Hotspots , 2008, PLoS genetics.
[2] D. Stephan,et al. A survey of genetic human cortical gene expression , 2007, Nature Genetics.
[3] Neil D. Lawrence,et al. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..
[4] D. Koller,et al. Population genomics of human gene expression , 2007, Nature Genetics.
[5] David Mackay,et al. Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks , 1995 .
[6] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[7] Cheng Li,et al. Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.
[8] John D. Storey,et al. Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[9] G. Churchill. Fundamentals of experimental design for cDNA microarrays , 2002, Nature Genetics.
[10] L. Kruglyak,et al. Gene–Environment Interaction in Yeast Gene Expression , 2008, PLoS biology.
[11] Leopold Parts,et al. A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies , 2010, PLoS Comput. Biol..
[12] Chun Jimmie Ye,et al. Accurate Discovery of Expression Quantitative Trait Loci Under Confounding From Spurious and Genuine Regulatory Hotspots , 2008, Genetics.
[13] Ying Liu,et al. FaST linear mixed models for genome-wide association studies , 2011, Nature Methods.
[14] L. Kruglyak,et al. Genetic Dissection of Transcriptional Regulation in Budding Yeast , 2002, Science.
[15] D. Heckerman,et al. Efficient Control of Population Structure in Model Organism Association Mapping , 2008, Genetics.
[16] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[17] Pooja Jain,et al. The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae , 2005, Nucleic Acids Res..
[18] R. Durbin,et al. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses , 2012, Nature Protocols.
[19] Tom Minka,et al. Automatic Choice of Dimensionality for PCA , 2000, NIPS.
[20] Jorge Nocedal,et al. A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..
[21] D. Clayton,et al. Extreme Clonality in Lymphoblastoid Cell Lines with Implications for Allele Specific Expression Analyses , 2008, PloS one.
[22] David Heckerman,et al. Correction for hidden confounders in the genetic analysis of gene expression , 2010, Proceedings of the National Academy of Sciences.
[23] J. Castle,et al. An integrative genomics approach to infer causal associations between gene expression and disease , 2005, Nature Genetics.
[24] M. McCarthy,et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges , 2008, Nature Reviews Genetics.
[25] M. McMullen,et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness , 2006, Nature Genetics.
[26] Simon C. Potter,et al. The Architecture of Gene Regulatory Variation across Multiple Human Tissues: The MuTHER Study , 2011, PLoS genetics.
[27] Vipin T. Sreedharan,et al. Multiple reference genomes and transcriptomes for Arabidopsis thaliana , 2011, Nature.
[28] D. Balding,et al. Handbook of statistical genetics , 2004 .
[29] Daniel Pinkel,et al. Large-scale variation among human and great ape genomes determined by array comparative genomic hybridization. , 2003, Genome research.
[30] Joseph K. Pickrell,et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing , 2010, Nature.
[31] John D. Storey,et al. Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.
[32] D. Reich,et al. Principal components analysis corrects for stratification in genome-wide association studies , 2006, Nature Genetics.
[33] H. Kang,et al. Variance component model to account for sample structure in genome-wide association studies , 2010, Nature Genetics.