Integrative omics analysis

Qi LiuCenter for Quantitative Sciences

Vanderbilt University School of Medicineqi.liu@vanderbilt.edu

Content

• Introduction • Data Sources• Methods• Tools• Things to be aware

Why?

http://jdr.sagepub.com/content/90/5/561

GenomicsWGS, WES

TranscriptomicsRNA-Seq

Epigenomics Bisulfite-Seq

ChIP-Seq

Small indels

point mutation

Copy number variation

Structural variation

Differential expression

Gene fusion

Alternative splicing

RNA editing

Methylation

Histone modification

Transcription Factor binding

Functional effect of mutation

Network and pathway analysis

Integrative analysis

Further understanding of cancer and clinical applications

Technologies Data Analysis Integration and interpretationPatient

What? at least two different types of omics data

Objectives

1. Understand relationships between different types of molecular data

2. Understand the phenotype – latent: disease subtype– Observable: patient outcome

Data sourcesTCGA

https://tcga-data.nci.nih.gov/tcga/

http://www.nature.com/ng/journal/v45/n10/full/ng.2764.html

Firehosehttp://gdac.broadinstitute.org/

cBioPortalhttp://www.cbioportal.org/public-portal/index.do

ICGChttps://icgc.org/

COSMIC

ENCODEhttp://genome.ucsc.edu/ENCODE/

http://www.nature.com/news/encode-the-human-encyclopaedia-1.11312http://genome-mirror.duhs.duke.edu/ENCODE/

FANTOMhttp://fantom.gsc.riken.jp/5/

GTEXhttp://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 analysisReveal the relationships between different molecular layers

– The strength of association indicates in trans-regulation.

miRNA

GSE10843

GSE10833

microRNA

miRNA-mRNA correlation

miRNA-ratio correlation

miRNA-protein correlation

mRNA decay

Translational repression

Combined effect

Association of sequence features with estimated mRNA decay or translation

repression

Site type

Site location

Local AU-context

Additional 3’ pairing

Significant inverse Correlation (p<0.005)

Supported by TargetScan, miRanda or MirTarget2

microRNA-target interactions

7235 functional relationships

Binding evidence

580 interactions60miRNAs423 genes

Sequence features on site efficacymicroRNA-target interactions

mRNAi

protein/mRNAratio

protein

the relative contribution of translation repression

79 miRNAs

5144 genes

Integrative method

Features on site efficacy for these two regulation types

mRNA decay : 8mer is efficientTanslational repression :8mer site do not show significant efficacy

mRNA decay : 3’UTR>ORF>5’UTRtranslational 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 Interactions60 miRNAs , 423 genes580 interactions , in which 332 (57.2%) was discovered by the integration of proteomics data

miRNA-mRNA miRNA-ratio

miRNA-protein

212 147

31 295

156

0

miRNA-mRNA

TargetScan

miRanda

MirTarget2

miRNA-ratio

miRNA-protein

Function

Sequence

miR-138 prefers translational repression SW620 and SW480 (derived from the same patient)

SW620 SW480source lymph node primary

metastasis high poor

miR-138 (log2)

3.06 6.39

• 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

• Linear regression

• Ridge—L2 penalized• Lasso—L1 penalized• Elastic net—L1+L2 penalized

ClusteringUnsupervised 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 plotCircoshttp://circos.ca/intro/genomic_data/Rcircoshttp://cran.r-project.org/web/packages/RCircos/index.html OmicCircoshttp://www.bioconductor.org/packages/release/bioc/html/OmicCircos.html

IGVhttp://www.broadinstitute.org/software/igv/home

NetGestalthttp://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.