EXPression ANalyzer and DisplayER

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EXPression ANalyzer and DisplayER

• Adi Maron-Katz
• Igor Ulitsky
• Chaim Linhart
• Amos Tanay
• Rani Elkon

Seagull Shavit Roded Sharan Israel
Steinfeld Yossi Shiloh Ron Shamir
Ron Shamirs Computational Genomics Group
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Schedule

• 1015 1110 Expander
• 1110 1130 Amadeus
• 1130 1145 Spike
• 1145 1210 Matisse, FAME
• 1310 1500 Hands-on

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• EXPANDER an integrative package for analysis of
  gene expression data
• Built-in support for 11 organisms
• human, mouse, rat, chicken, zebra-fish, fly,
• worm, arabidopsis, yeast (sce, pombe), E.coli ()
  
• Demonstration - on a dataset collected in our
  labs

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What can it do?

• Low level analysis
• Missing data estimation (KNN or manual)
• Data adjustments (merge conditions, divide by
  base, take log)
• Normalization quantile, loess
• Filtering fold change, variation, t-test
• Standardization mean 0 std 1
• High level gene partition analysis
• Clustering
• Biclustering
• Network based clustering

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What Can it do? (II)

• Ascribing biological meaning to patterns
• Functional analysis (enriched Gene Ontology
  terms)
• Promoter analysis (over-represented
  transcription factor binding sites)
• Chromosomal location analysis
• miRNA targets enrichment analysis
• Custom annotations enrichment analysis
• Signaling pathway enrichment analysis and
  visualization

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Input data
Normalization/ Filtering
Visualization utilities
Links to public annotation databases
Grouping (Clustering/ Biclustering/ Network
based clustering)
Functional enrichment (TANGO)
Promoter signals (PRIMA)
Location enrichment
miRNA Targets enrichment (FAME)
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EXPANDER Data

• Input data
• Expression matrix (probe-row condition-column)
• One-channel data (e.g., Affymetrix)
• Dual-channel data, in which data is log R/G (e.g.
  cDNA microarrays)
• .cel files
• ID conversion file maps probes to genes
• Gene sets data defines gene groups

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EXPANDER Data (II)

• Data definitions
• Defining condition subsets
• Data type scale (log)
• Data Adjustments
• Missing value estimation (KNN or arbitrary)
• Merging conditions
• Divide by base
• Log data (base 2)

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EXPANDER Preprocessing

• Normalization removal of systematic biases from
  the analyzed chips
• Implemented methods quantile, lowess
• Visualization box plots, scatter plots (simple,
  M vs. A)
• Filtering Focus downstream analysis on the set
  of responding genes
• Fold-Change
• Variation
• Statistical tests (T-test)
• SAM (Significance Analysis of Microarrays)
• Standardization Mean0, STD1 (visualization)

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Input data
Normalization/ Filtering
Visualization utilities
Links to public annotation databases
Grouping (Clustering/ Biclustering/ Network
based clustering)
Functional enrichment (TANGO)
Promoter signals (PRIMA)
Location enrichment
miRNA Targets enrichment (FAME)
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Cluster Analysis

• partition the responding genes into distinct
  sets, each with a particular expression pattern
• Identify major patterns ? reduce dimensionality
  of the problem
• co-expression ? co-function
• co-expression ? co-regulation
• Partition the genes to achieve
• High Homogeneity within clusters
• High Separation between clusters

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Cluster Analysis (II)

• Implemented algorithms
• CLICK, K-means, SOM, Hierarchical
• Visualization
• Mean expression patterns
• Heat-maps
• Chromosomal positions
• Network sub-graph

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Example study responses to ionizing radiation
Ionizing Radiation
Double Strand Breaks
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Example study experimental design

• Genotypes Atm-/- and control w.t. mice
• Tissue Lymph node
• Treatment Ionizing radiation
• Time points 0, 30 min, 120 min
• Microarrays Affymetrix U74Av2 (12k probesets)

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Test case - Data Analysis

• Dataset six conditions (2 genotypes, 3 time
  points)
• Normalization
• Filtering step define the responding genes
  set
• genes whose expression level is changed by at
  least 1.75 fold
• 700 genes met this criterion
• The set contains genes with various response
  patterns we applied CLICK to this set of genes

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Major Gene Clusters Irradiated Lymph node
Atm-dependent early responding genes
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Major Gene Clusters Irradiated Lymph node
Atm-dependent 2nd wave of responding genes
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Input data
Normalization/ Filtering
Visualization utilities
Links to public annotation databases
Grouping (Clustering/ Biclustering/ Network
based clustering)
Functional enrichment (TANGO)
Promoter signals (PRIMA)
Location enrichment
miRNA Targets enrichment (FAME)
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Ascribe functional meaning to clusters

• Gene Ontology (GO) annotations for human, mouse,
  rat, chicken, fly, worm, arabidopsis, zebra-fish,
  yeast (sce and pombe) and e.coli.
• TANGO Apply statistical tests that seek
  over-represented GO functional categories in the
  clusters.

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Enriched GO Functional Categories

• Hierarchical structure ? highly dependent
  categories.
• Problems
• High redundancy
• Multiple testing corrections assume independent
  tests
• TANGO

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Functional Enrichment - Visualization
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Functional Categories
cell cycle control (plt1x10-6 )
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Functional Categories
Cell cycle control (plt5x10-6) Apoptosis (p0.001)
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Input data
Normalization/ Filtering
Visualization utilities
Links to public annotation databases
Grouping (Clustering/ Biclustering/ Network
based clustering)
Functional enrichment (TANGO)
Promoter signals (PRIMA)
Location enrichment
miRNA Targets enrichment (FAME)
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Clues are in the promoters
Identify Transcriptional Regulators
ATM
Hidden layer
?
?
?
?
?
p53
TF-C
TF-B
TF-A
NEW
Observed layer
g3
g13
g12
g10
g9
g1
g8
g7
g6
g5
g4
g11
g2
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Reverse engineering of transcriptional networks

• Infers regulatory mechanisms from gene expression
  data
• Assumption
• co-expression ? transcriptional co-regulation ?
  common cis-regulatory promoter elements
• Step 1 Identification of co-expressed genes
  using microarray technology (clustering algs)
• Step 2 Computational identification of
  cis-regulatory elements that are over-represented
  in promoters of the co-expressed gene

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PRIMA general description

• Input
• Target set (e.g., co-expressed genes)
• Background set (e.g., all genes on the chip)
• Analysis
• Identify transcription factors whose binding site
  signatures are enriched in the Target set with
  respect to the Background set.
• TF binding site models TRANSFAC DB
• Default From -1000 bp to 200 bp relative the TSS

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Promoter Analysis - Visualization
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PRIMA - Results
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PRIMA Results
NF-?B
5.1
3.8x10-8
p53
4.2
9.6x10-7
STAT-1
3.2
5.4x10-6
Sp-1
1.7
6.5x10-4
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Biclustering

• Clustering becomes too restrictive on large
  datasets
• Seeks global partition of genes according to
  similarity in their expression across ALL
  conditions
• Relevant knowledge can be revealed by identifying
  genes with common pattern across a subset of the
  conditions
• Novel algorithmic approach is needed
  Biclustering

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Biclustering SAMBAStatistical Algorithmic
Method for Bicluster Analysis
A. Tanay, R. Sharan, R. Shamir RECOMB 02

• Bicluster (module) subset of genes with
  similar behavior in a subset of conditions
• Computationally challenging has to consider
  many combinations of sub-conditions

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Biclustering Visualization
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Network based clustering

• Goal to identify modules using gene expression
  data and interaction networks.
• GE data Interactions file (.sif) .
• MATISSE (Module Analysis via Topology of
  Interactions and Similarity SEts).

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Network based clustering visualization

• Similar to clustering visualization (gene list,
  mean patterns, heat maps, etc.).
• Interactions map

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Input data
Normalization/ Filtering
Visualization utilities
Links to public annotation databases
Grouping (Clustering/ Biclustering/ Network
based clustering)
Functional enrichment (TANGO)
Promoter signals (PRIMA)
Location enrichment
miRNA Targets enrichment (FAME)
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Location analysis

• Goal Detect genes that are located in the same
  area and are co-expressed.
• Search for over represented chromosomal areas
  within gene groups.
• Statistical test.
• Redundancy filter
• Ignoring known gene clusters

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Location analysis visualization

• Enrichment analysis visualization
• Positions view with color assignments

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Input data
Normalization/ Filtering
Visualization utilities
Links to public annotation databases
Grouping (Clustering/ Biclustering/ Network
based clustering)
Functional enrichment (TANGO)
Promoter signals (PRIMA)
Location enrichment
miRNA Targets enrichment (FAME)
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miRNA Analysis

• Goal detect microRNAs whose binding sites are
  over/under represented in the 3' UTRs of a gene
  groups.
• FAME Algorithm
• Empirical tests using a sampling technique
  (random permutations). 
• Accounting for biases in the 3' UTR sequences

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Thank you
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Expression Data Input File
conditions
probes
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ID Conversion File
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Gene Sets File
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Normalization Box plots
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Standardization of Expression Levels
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Cluster Analysis Visualization (I)
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Cluster Analysis - Visualization (II)
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Positions visualization