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Integrative omics analysis
Qi Liu
Center for Quantitative Sciences
Vanderbilt University School of Medicine
[email protected]
Content
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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
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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
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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
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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.