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Initial Steps Toward Computational Discovery of Genetic Regulatory Networks in Pancreatic Islet Development. Georg Gerber, PhD Gifford Laboratory, MIT CSAIL April 9, 2009. Outline. Goals Expression data overview TF-TF interaction networks p air-wise mutual information Bayesian networks

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slide1

Initial Steps Toward Computational Discovery of Genetic Regulatory Networks in Pancreatic Islet Development

Georg Gerber, PhD

Gifford Laboratory, MIT CSAIL

April 9, 2009

outline
Outline
  • Goals
  • Expression data overview
  • TF-TF interaction networks
    • pair-wise mutual information
    • Bayesian networks
  • Gene expression programs
  • ChIP-seq data
  • Directions for future work
biological goals of building a transcriptional regulatory network of pancreatic specification
Biological goals of building a transcriptional regulatory network of pancreatic specification
  • Knowledge of distinct signaling/transcriptional steps involved in pancreatic specification
    • Optimize ES differentiation by determining signaling event(s) directly inducing each sequential TF
  • What is the network structure? Linear or cross-regulatory, parallel or all interrelated
    • Direct reprogramming using TFs would benefit from knowing hierarchy of each network
    • Are TFs that play role in specification of pancreas necessary for later function of pancreas or are they merely required to properly induce other necessary TFs?
  • Can knowledge of the pancreatic specification network teach us about lineage diversification within the pancreas (endocrine, exocrine, duct)?
immediate computational goals
Immediate computational goals
  • Determine set of transcription factors active at different developmental stages
  • Discover network “wiring”
  • Determine how network changes/evolves throughout development
  • Compare in vivo and ESC networks
outline1
Outline
  • Goals
  • Expression data overview
  • TF-TF interaction networks
    • pair-wise mutual information
    • Bayesian networks
  • Gene expression programs
  • ChIP-seq data
  • Directions for future work
expression data overview
Expression data overview

E8.25

Embryonic ectoderm/notochord

Embryonic mesoderm

Definitive endoderm

(E7.75 and E8.75 as well)

E11.5

Stomach

endoderm

Intestinal

endoderm

Pancreatic

Endoderm

(E10.5 as well)

Lung

endoderm

Liver

endoderm

Esophageal

endoderm

slide7

Tcf2

Foxa2

DMSO

DMSO/

2 uM RA

6h/24h

50 ng/mLActA

6 days

ES

Sox17

GFP+

FACS sort Sox17GFP+Dpp4- definitive endoderm

and perform microarray

2 uM RA

  • Implant bead coated with DMSO/RA into foregut of E8.25 (4-6 somite) embryo
  • Explant embryo anterior to 1stsomite
  • Culture for 6/24 hours
  • Dissociate, sort for EpCAM+ endoderm
  • Amplify RNA and profile on Illumina Mouse Ref8 v2 chips
expression data overview cont
Expression data overview (cont.)
  • 120 Illumina arrays (18118 genes/array)
  • 72 distinct experiments (41 in mESC’s)
  • Standardized mESC/in vivo experiments separately
  • 2758 genes w/ ≥ 2-fold change in ≥ 5 experiments
  • 154 TFs w/ ≥ 2-fold change in ≥ 5 experiments (out of 946 “definite” or “candidate” TFs from TFCat, Fulton et al, Genome Biology 2009)
limitations of expression data for genetic network reconstruction
Limitations of expression data for genetic network reconstruction
  • Need 100’s of varied experiments for finding relevant/significant networks
  • Association ≠ causation
  • High false positive rates (high dimensional, noisy, dependent data)
  • High false negative rates (low TF transcript abundance, post-transcriptional regulation, etc.)
outline2
Outline
  • Goals
  • Expression data overview
  • TF-TF interaction networks
    • pair-wise mutual information
    • Bayesian networks
  • Gene expression programs
  • ChIP-seq data
  • Directions for future work
pair wise mutual information networks clr
Pair-wise mutual information networks (CLR)
  • Context Likelihood of Relatedness method: Faith et al., PLoS Biology 2007
  • Computes MI between all genes
  • Innovation: considers MI distribution for both target and source to compute p-values/estimate FDR
slide13

TF-TF network (MI)

E8.25 4-6s definitive endoderm

slide14

TF-TF network (MI)

E8.75 13-15s definitive endoderm

slide15

TF-TF network (MI)

E9.5 definitive endoderm

slide16

TF-TF network (MI)

E10.5 pancreatic endoderm

slide17

TF-TF network (MI)

E11.5 pancreatic endoderm

slide18

TF-TF network (MI)

E11.5 intestinal endoderm

slide19

TF-TF network (MI)

6h 83 uM RA bead

mES 2 uM RA 6h

slide20

TF-TF network (MI)

24h 83 uM RA bead

mES 2 uM RA 24h

outline3
Outline
  • Goals
  • Expression data overview
  • TF-TF interaction networks
    • pair-wise mutual information
    • Bayesian networks
  • Gene expression programs
  • ChIP-seq data
  • Directions for future work
bayesian networks
Bayesian networks
  • Directed networks, allow for multiple parents
  • Encode conditional independence
  • Penalize complexity automatically
  • Software: Banjo (Alexander Hartemink, Duke University)
slide23

E8.25 4-6s definitive endoderm

TF-TF network (Bayes Net)

slide24

E8.75 13-15s definitive endoderm

TF-TF network (Bayes Net)

slide25

E9.5 definitive endoderm

TF-TF network (Bayes Net)

slide26

E10.5 pancreatic endoderm

TF-TF network (Bayes Net)

slide27

E11.5 pancreatic endoderm

TF-TF network (Bayes Net)

slide28

mES 2 uM RA 6h

6h 83 uM RA bead

TF-TF network (Bayes Net)

slide29

mES 2 uM RA 24h

24h 83 uM RA bead

TF-TF network (Bayes Net)

outline4
Outline
  • Goals
  • Expression data overview
  • TF-TF interaction networks
    • pair-wise mutual information
    • Bayesian networks
  • Gene expression programs
  • ChIP-seq data
  • Directions for future work
advantages to methods that discover groups of genes
Advantages to methods that discover groups of genes
  • Infer more robust relationships because considering many genes
  • Allow for enrichment analysis
    • Functional categories
    • Signaling pathways
    • TF DNA binding sequence motifs
geneprogram
GeneProgram
  • Gerber et al, PLoS Comp Bio 2007
  • Discovers sets of genes co-expressed across subsets of conditions
  • Innovations:
    • Simultaneously models probabilistic structure of experiments (tissues) and genes
    • Uses Hierarchical Dirichlet Processes, a fully Bayesian method for automatically determining the number of expression programs and tissue groups
    • Outperforms state-of-the-art biclustering methods
slide33

Hierarchical clustering

Singular Value Decomposition (SVD)

Non-negative Matrix Factorization (NMF)

GeneProgram w/o tissue groups

Full GeneProgram model

slide34

tissue groups

GeneProgram produced a map of 12 tissue groups and 62 expression programs

slide35

tissue

GeneProgram produced a map of 12 tissue groups and 62 expression programs

slide36

GeneProgram produced a map of 12 tissue groups and 62 expression programs

expression programs (sorted by generality score)

slide37

GeneProgram produced a map of 12 tissue groups and 62 expression programs

expression program use by tissue

expression program enrichment analysis
Expression program enrichment analysis
  • GO categories
    • FDR controlled to 5%
  • TRANSFAC motifs
    • Software: SAMBA
    • Scans +3000 to -200 bp for each motif
    • Uses PWM to score region, background to calculate p-value (Bonferroni corrected)
slide39

Expression programs (GO and motif enrichment)

E8.25 4-6s definitive endoderm

slide40

Expression programs (GO and motif enrichment)

E8.75 13-15s definitive endoderm

slide43

Expression programs showing TFs in programs and motif enrichment

E8.25 4-6s definitive endoderm

slide44

Expression programs showing TFs in programs and motif enrichment

E8.75 13-15s definitive endoderm

slide45

Expression programs showing TFs in programs and motif enrichment

E9.5 definitive endoderm

slide46

Expression programs showing TFs in programs and motif enrichment

E10.5 pancreatic endoderm

slide47

Expression programs showing TFs in programs and motif enrichment

E11.5 pancreatic endoderm

outline5
Outline
  • Goals
  • Expression data overview
  • TF-TF interaction networks
    • pair-wise mutual information
    • Bayesian networks
  • Gene expression programs
  • ChIP-seq data
  • Directions for future work
retinoic acid receptor chip seq data
Retinoic acid receptor ChIP-seq data
  • Generated in the Wichterle lab at Columbia (unpublished data, Motor Neuron Development Project)
  • mESC’s grown to embryoid body stage, profiled after 8h of RA exposure
overlap of melton lab expression data and rar binding data
Overlap of Melton lab expression data and RAR binding data

Binding events determined with modified MACS method (Zhang et al, Genome Biology 2008); called if significant peak found w/in 50 kb of gene start site

future computational directions
Future computational directions
  • Add publically available ES expression data
  • Apply more sophisticated TF binding motif methods (phylogeny, spatial arrangements, co-regulation)
  • Extend GeneProgram framework for add’l data types (TF expression, binding motifs, ChIP-seq, knockdown/overexpression, ?protein-protein interactions, etc.) → causal/predictive models
  • Infer dynamic rewiring networks over inferred developmental tree
  • Develop novel probabilistic methods for ChIP-seq data
acknowledgements
Acknowledgements
  • Rich Sherwood (Melton lab) - all the expression data!
  • Arvind Jammalamadaka (Gifford lab) -initial data analysis/normalization methods
  • Shaun Mahony (Gifford lab) - RA ChIP-seq data analysis
  • Esteban Mazzoni (Wichterle lab) - RA ChIP-seq data
slide56

TF-TF network (MI)

E11.5 stomach endoderm

slide57

TF-TF network (MI)

E12.5 esophagus endoderm

slide58

TF-TF network (MI)

E11.5 liver endoderm

slide59

TF-TF network (MI)

E11.5 lung endoderm

slide68
GeneProgram outperformed popular biclustering algorithms in discovery of biologically meaningful gene sets from real microarray data

N = Novartis Tissue Atlas v2 (141 mouse and human tissues)

S = Shyamsundar et al. (115 human tissues)

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