Integrative omics analysis Qi Liu Center for Quantitative Sciences Vanderbilt University School of Medicine [email protected] Content • • • • • Introduction Data Sources Methods Tools Things to be aware Why? http://jdr.sagepub.com/content/90/5/561 What? at least two different types of omics data Patient Technologies Data Analysis Integration and interpretation point mutation Genomics WGS, WES Copy number variation Structural variation Functional effect of mutation Differential expression Transcriptomics RNA-Seq Gene fusion Network and pathway analysis Alternative splicing RNA editing Integrative analysis Epigenomics Bisulfite-Seq ChIP-Seq Methylation Histone modification Transcription Factor binding Further understanding of cancer and clinical applications Small indels Objectives 1. Understand relationships between different types of molecular data 2. Understand the phenotype – latent: disease subtype – Observable: patient outcome Data sources TCGA https://tcga-data.nci.nih.gov/tcga/ http://www.nature.com/ng/journal/v45/n10/full/ng.2764.html Firehose http://gdac.broadinstitute.org/ cBioPortal http://www.cbioportal.org/public-portal/index.do ICGC https://icgc.org/ COSMIC ENCODE http://genome.ucsc.edu/ENCODE/ http://genome-mirror.duhs.duke.edu/ENCODE/ http://www.nature.com/news/encode-the-human-encyclopaedia-1.11312 FANTOM http://fantom.gsc.riken.jp/5/ GTEX http://www.gtexportal.org/home/ Methods • • • • • • • Sequential or overlap analysis Clustering Correlation analysis Linear regression Network based analysis Bayesian ….. Sequential or overlap analysis • Confirmation or refinement of findings – Each data are independently analyzed to get a list of interesting entities – Lists of interesting entities are linked together • • • Chin, K. et al. Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell 10, 529–541 (2006). Lando, M. et al. Gene dosage, expression, and ontology analysis identifies driver genes in the carcinogenesis and chemoradioresistance of cervical cancer. PLoS Genet. 5, e1000719 (2009). Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010). Correlation analysis Reveal the relationships between different molecular layers – The strength of association indicates in trans-regulation. miRNA Integrative method GSE10843 miRNA-mRNA correlation mRNA decay protein/mRNA ratio miRNA-ratio correlation Translational repression protein miRNA-protein correlation i mRNA GSE10833 microRNA 79 miRNAs Combined effect 5144 genes Sequence features on site efficacy Association of sequence features with estimated mRNA decay or translation repression microRNA-target interactions Significant inverse Correlation (p<0.005) Supported by TargetScan, miRanda or MirTarget2 Site type Site location 7235 functional relationships Binding evidence Local AU-context Additional 3’ pairing microRNA-target interactions the relative contribution of translation repression 580 interactions 60miRNAs 423 genes Features on site efficacy for these two regulation types mRNA decay : 8mer is efficient Tanslational repression : 8mer site do not show significant efficacy mRNA decay : 3’UTR>ORF>5’UTR translational repression : marginal significance in ORF Features on site efficacy for these two regulation types AU-rich context appears to favor both mRNA decay and translational repression 3’ pairing enhance mRNA decay , but disfavor efficacy for translational repression miRNA-target Interactions 60 miRNAs , 423 genes 580 interactions , in which 332 (57.2%) was discovered by the integration of proteomics data miRNA-protein miRNA-mRNA miRNA-ratio Function 156 miRNA-protein 29 31 5 TargetScan Sequence 212 0 147 miRanda MirTarget2 miRNA-mRNA miRNA-ratio miR-138 prefers translational repression SW620 and SW480 (derived from the same patient) SW620 SW480 source lymph node primary metastasis high poor miR-138 (log2) 3.06 6.39 Linear regression • Estimate the strength of association between different data • Predict the outcome by modeling the combined effect of multiple types of data Linear regression • Linear regression • Ridge—L2 penalized • Lasso—L1 penalized • Elastic net—L1+L2 penalized Clustering Unsupervised clustering of omics data to find inherent structures – Using common latent variables among all data types Network based analysis --using inferred networks or known network interactions to guide analysis Illustrative example of SNF steps The advantage of the integrative procedure is that weak similarities (low-weight edges) disappear, helping to reduce the noise, and strong similarities (high-weight edges) present in one or more networks are added to the others. Additionally, low-weight edges supported by all networks are retained depending on how tightly connected their neighborhoods are across networks. Patient similarities for each data types compared to SNF fused similarity Comparison of SNF with icluster and concatenation Methods Methods Extension to more than 2 data types Tools • Sequential or overlap analysis • Clustering – R package icluster, iclusterPlus • Correlation based • Linear regression – http://cbio.mskcc.org/leslielab/RegulatorInference – R package glmnet • Network based – R package SNFtool • Bayesian • ….. Visualization: Circular map for omics data Chen et al. Cell 2012, 148(6):1293-1307 Circos plot Circos http://circos.ca/intro/genomic_data/ Rcircos http://cran.r-project.org/web/packages/RCircos/index.html OmicCircos http://www.bioconductor.org/packages/release/bioc/html/OmicCircos.html IGV http://www.broadinstitute.org/software/igv/home NetGestalt http://www.netgestalt.org/#2 Things to be aware • The importance • The challenge in integrative analyses – Dimensionality • Integration attempts are best carried out using known biological knowledge References • • • • • • • • Kristensen VN. et al. Principles and methods of integrative genomic analyses in cancer. Nat Rev Cancer. 2014, 14(5):299-313 Wang B, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014 ,11(3):333-7. Yuan Y, et al. Assessing the clinical utility of cancer genomic and proteomic data across tumor types. Nat Biotechnol. 2014 Jul;32(7):644-52. Shen R, et al. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics. 2009 Nov 15;25(22):2906-12. Liu Q, et al. Integrative omics analysis reveals the importance and scope of translational repression in microRNA-mediated regulation. Mol Cell Proteomics. 2013,12(7):1900-11. Setty M, et al. Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma. Mol Syst Biol. 2012;8:605 Lappalainen T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 2013, 501, 506–511 Jacobsen A, et al. Analysis of microRNA-target interactions across diverse cancer types. Nat Struct Mol Biol. 2013 , 20(11):1325-32.