Statistical methods for integrating multiple types of high-throughput data.

Large-scale sequencing, copy number, mRNA, and protein data have given great promise to the biomedical research, while posing great challenges to data management and data analysis. Integrating different types of high-throughput data from diverse sources can increase the statistical power of data analysis and provide deeper biological understanding. This chapter uses two biomedical research examples to illustrate why there is an urgent need to develop reliable and robust methods for integrating the heterogeneous data. We then introduce and review some recently developed statistical methods for integrative analysis for both statistical inference and classification purposes. Finally, we present some useful public access databases and program code to facilitate the integrative analysis in practice.

[1]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

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

[3]  Rainer Spang,et al.  Molecular decomposition of complex clinical phenotypes using biologically structured analysis of microarray data , 2005, Bioinform..

[4]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[5]  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.

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

[7]  Wei Pan,et al.  Incorporating Biological Information as a Prior in an Empirical Bayes Approach to Analyzing Microarray Data , 2005, Statistical applications in genetics and molecular biology.

[8]  D. Botstein,et al.  Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF , 2001, Nature.

[9]  Wei Pan,et al.  Statistical significance analysis of longitudinal gene expression data , 2003, Bioinform..

[10]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[11]  J. Besag,et al.  On conditional and intrinsic autoregressions , 1995 .

[12]  John D. Storey,et al.  Empirical Bayes Analysis of a Microarray Experiment , 2001 .

[13]  Guanghua Xiao,et al.  Improved Detection of Differentially Expressed Genes Through Incorporation of Gene Locations , 2009, Biometrics.

[14]  Geoffrey J. McLachlan,et al.  A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays , 2006, Bioinform..

[15]  Baolin Wu.  Erratum: Differential gene expression detection and sample classification using penalized linear regression models (Bioinformatics (2006) vol. 22 (4) (472-476)) , 2006 .

[16]  E. Lander,et al.  Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.

[17]  Wei Pan,et al.  Incorporating prior knowledge of predictors into penalized classifiers with multiple penalty terms , 2007, Bioinform..

[18]  Shailesh V. Date,et al.  A Probabilistic Functional Network of Yeast Genes , 2004, Science.

[19]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[20]  Bradley P. Carlin,et al.  BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS , 1996, Stat. Comput..

[21]  E. Lander,et al.  Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Wei Pan,et al.  Gene expression A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data , 2005 .

[23]  C. Wijmenga,et al.  Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. , 2006, American journal of human genetics.

[24]  Wei Pan,et al.  Incorporating prior information via shrinkage: a combined analysis of genome‐wide location data and gene expression data , 2007, Statistics in medicine.

[25]  Elizabeth Garrett-Mayer,et al.  Cross-study validation and combined analysis of gene expression microarray data. , 2007, Biostatistics.

[26]  J. Nelder,et al.  Double hierarchical generalized linear models (with discussion) , 2006 .

[27]  Baolin Wu,et al.  Differential gene expression detection and sample classification using penalized linear regression models , 2006, Bioinform..

[28]  Christina Kendziorski,et al.  On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data , 2001, J. Comput. Biol..

[29]  Wei Pan,et al.  Linear regression and two-class classification with gene expression data , 2003, Bioinform..

[30]  J. Lieb,et al.  ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. , 2004, Genomics.

[31]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[32]  Hongzhe Li,et al.  A Markov random field model for network-based analysis of genomic data , 2007, Bioinform..

[33]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[34]  J. Foekens,et al.  Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer , 2005, The Lancet.

[35]  J. Welsh,et al.  Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. , 2001, Cancer research.

[36]  Wei Pan,et al.  On the Use of Permutation in and the Performance of A Class of Nonparametric Methods to Detect Differential Gene Expression , 2003, Bioinform..

[37]  Wei Pan,et al.  A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments , 2002, Bioinform..

[38]  Igor Jurisica,et al.  Gene expression–based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study , 2008, Nature Medicine.

[39]  Wei Pan,et al.  Bioinformatics Original Paper Incorporating Gene Functions as Priors in Model-based Clustering of Microarray Gene Expression Data , 2022 .

[40]  Yang Xie,et al.  Predicting the future for people with lung cancer , 2008, Nature Medicine.

[41]  Nicola J. Rinaldi,et al.  Serial Regulation of Transcriptional Regulators in the Yeast Cell Cycle , 2001, Cell.

[42]  J. Dow,et al.  The dictionary of cell and molecular biology , 1999 .

[43]  Sylvia Richardson,et al.  Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model , 2006, Bioinform..

[44]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[45]  Wei Pan,et al.  A Bayesian approach to joint modeling of protein–DNA binding, gene expression and sequence data , 2010, Statistics in medicine.

[46]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[47]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[48]  Sudha Rao,et al.  Of Chips and ChIPs , 2002, Science.

[49]  Wei Pan,et al.  BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm612 Systems biology , 2022 .

[50]  Tom Britton,et al.  Hierarchical Bayes models for cDNA microarray gene expression. , 2005, Biostatistics.

[51]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.