Simultaneous inference for RNA-Seq data

In the last few years, RNA-Seq has become a popular choice for high-throughput studies of gene expression, revealing its potential to overcome microarrays and become the new standard for transcriptional profiling. At a gene-level, RNA-Seq yields counts rather than continuous measures of expression, leading to the need for novel methods to deal with count data in high-dimensional problems. In this Thesis, we aim at shedding light on the problems related to the exploration and modeling of RNA-Seq data. In particular, we introduce simple and effective ways to summarize and visualize the data; we define a novel algorithm for the clustering of RNA-Seq data and we implement simple normalization strategies to deal with technology-related biases. Finally, we present a hierarchical Bayesian approach to the modeling of RNA-Seq data. The model accounts for the difference in sequencing depth, as well as for overdispersion, automatically accounting for different types of normalization.

[1]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[2]  Gordon K Smyth,et al.  Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .

[3]  Trevor Hastie,et al.  Regularized linear discriminant analysis and its application in microarrays. , 2007, Biostatistics.

[4]  Wei Zheng,et al.  Bias detection and correction in RNA-Sequencing data , 2011, BMC Bioinformatics.

[5]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[6]  Ingrid Lönnstedt.  Replicated microarray data , 2001 .

[7]  Martin N. Rossor,et al.  Advanced online publication. , 2005, Nature structural biology.

[8]  R. A. Kempton,et al.  Log-Series and Log-Normal Parameters as Diversity Discriminants for the Lepidoptera , 1974 .

[9]  W. Cleveland,et al.  Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting , 1988 .

[10]  Paolo G. V. Martini,et al.  Statistical Test of Expression Pattern (STEPath): a new strategy to integrate gene expression data with genomic information in individual and meta-analysis studies , 2011, BMC Bioinformatics.

[11]  S. Dudoit,et al.  Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. , 2002, Nucleic acids research.

[12]  Chiara Romualdi,et al.  A-MADMAN: Annotation-based microarray data meta-analysis tool , 2009, BMC Bioinformatics.

[13]  J. Lawless.  Negative binomial and mixed Poisson regression , 1987 .

[14]  David Tritchler,et al.  Filtering Genes for Cluster and Network Analysis , 2009, BMC Bioinformatics.

[15]  J. Ibrahim,et al.  Bayesian Models for Gene Expression With DNA Microarray Data , 2002 .

[16]  I. Good.  The Bayes/Non-Bayes Compromise: A Brief Review , 1992 .

[17]  Nicola Torelli,et al.  Preserving the Clustering Structure by a Projection Pursuit Approach , 2010 .

[18]  David P. Dobkin,et al.  The quickhull algorithm for convex hulls , 1996, TOMS.

[19]  Cole Trapnell,et al.  Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.

[20]  Sandrine Dudoit,et al.  Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments , 2010, BMC Bioinformatics.

[21]  F. Crick.  Central Dogma of Molecular Biology , 1970, Nature.

[22]  F. J. Anscombe,et al.  THE TRANSFORMATION OF POISSON, BINOMIAL AND NEGATIVE-BINOMIAL DATA , 1948 .

[23]  Matthew D. Young,et al.  Gene ontology analysis for RNA-seq: accounting for selection bias , 2010, Genome Biology.

[24]  D. Slonim.  From patterns to pathways: gene expression data analysis comes of age , 2002, Nature Genetics.

[25]  Kenny Q. Ye,et al.  Sensitive and accurate detection of copy number variants using read depth of coverage. , 2009, Genome research.

[26]  Mark de Berg,et al.  Computational geometry: algorithms and applications , 1997 .

[27]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[28]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[29]  A. Oshlack,et al.  Transcript length bias in RNA-seq data confounds systems biology , 2009, Biology Direct.

[30]  A. Raftery,et al.  Model-based Gaussian and non-Gaussian clustering , 1993 .

[31]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[32]  Inderjit S. Dhillon,et al.  Clustering on the Unit Hypersphere using von Mises-Fisher Distributions , 2005, J. Mach. Learn. Res..

[33]  K. Hansen,et al.  Biases in Illumina transcriptome sequencing caused by random hexamer priming , 2010, Nucleic acids research.

[34]  M. Salit,et al.  Synthetic Spike-in Standards for Rna-seq Experiments Material Supplemental Open Access License Commons Creative , 2022 .

[35]  H. Rue,et al.  Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations , 2009 .

[36]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[37]  F. J. Anscombe,et al.  Sampling theory of the negative binomial and logarithmic series distributions. , 1950, Biometrika.

[38]  Geoffrey J. McLachlan,et al.  A mixture model-based approach to the clustering of microarray expression data , 2002, Bioinform..

[39]  B. Williams,et al.  Mapping and quantifying mammalian transcriptomes by RNA-Seq , 2008, Nature Methods.

[40]  Nicola Torelli,et al.  Clustering via nonparametric density estimation , 2007, Stat. Comput..

[41]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[42]  Emmanuel Barillot,et al.  Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization , 2010, Bioinform..

[43]  Juliane C. Dohm,et al.  Substantial biases in ultra-short read data sets from high-throughput DNA sequencing , 2008, Nucleic acids research.

[44]  Nancy F. Hansen,et al.  Accurate Whole Human Genome Sequencing using Reversible Terminator Chemistry , 2008, Nature.

[45]  I. Johnstone,et al.  On Consistency and Sparsity for Principal Components Analysis in High Dimensions , 2009, Journal of the American Statistical Association.

[46]  M. Bartlett.  The Square Root Transformation in Analysis of Variance , 1936 .

[47]  Krista Rizman Zalik,et al.  Biclustering of gene expression data , 2005 .

[48]  Gabriele Sales,et al.  A Hierarchical Bayesian Model for RNA-Seq Data , 2013 .

[49]  M. Stephens,et al.  RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. , 2008, Genome research.

[50]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[51]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[52]  D. Cox,et al.  Parameter Orthogonality and Approximate Conditional Inference , 1987 .

[53]  K. Hansen,et al.  Removing technical variability in RNA-seq data using conditional quantile normalization , 2012, Biostatistics.

[54]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[55]  Jean-Philippe Vert,et al.  The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures , 2011, PloS one.

[56]  Robert Gentleman,et al.  ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data , 2009, Bioinform..

[57]  Heather J. Ruskin,et al.  Techniques for clustering gene expression data , 2008, Comput. Biol. Medicine.

[58]  Gordon K. Smyth,et al.  Testing significance relative to a fold-change threshold is a TREAT , 2009, Bioinform..

[59]  W. Huber,et al.  which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets , 2011 .

[60]  Davide Risso,et al.  A novel approach to the clustering of microarray data via nonparametric density estimation , 2011, BMC Bioinformatics.

[61]  G. Sherlock,et al.  Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads , 2010, BMC Genomics.

[62]  M. Robinson,et al.  Small-sample estimation of negative binomial dispersion, with applications to SAGE data. , 2007, Biostatistics.