APO-SYS workshop on data analysis and pathway charting

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APO-SYS workshop on data analysis and pathway
charting

• Igor Ulitsky
• Ron Shamirs Computational Genomics Group

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Part I Presentations

• EXPANDER
• AMADEUS
• SPIKE
• MATISSE

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Part II Hands-on Session

• EXPANDER
• MATISSE
• SPIKE

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EXPression ANalyzer and DisplayER

• Adi Maron-Katz
• Chaim Linhart
• Amos Tanay
• Rani Elkon
• Israel Steinfeld

Seagull Shavit Igor Ulitsky Roded Sharan Yossi
Shiloh Ron Shamir
http//acgt.cs.tau.ac.il/expander
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EXPANDER

• Low level analysis
• Missing data estimation (KNN or manual)
• Normalization quantile, loess
• Filtering fold change, variation, t-test
• Standardization mean 0 std 1, take log, fixed
  norm
• High level gene partition analysis
• Clustering
• Biclustering
• Ascribing biological meaning to patterns
• Enriched functional categories (Gene Ontology)
• Identify transcriptional regulators promoter
  analysis
• Built-in support for 9 organisms
• human, mouse, rat, chicken, zebrafish, fly, worm,
  arabidopsis, yeast

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Input data
Normalization/ Filtering
Links to public annotation databases
Visualization utilities
Clustering (CLICK, SOM, K-means, Hierarchical)
Biclustering (SAMBA)
Functional enrichment (TANGO)
Promoter signals (PRIMA)
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EXPANDER - Preprocessing

• Input data
• Expression matrix (probe-row condition-column)
• One-channel data (e.g., Affymetrix)
• Dual-channel data (cDNA microarrays, data are
  (log) ratios between the Red and Green channels)
• .cel files
• ID conversion file map probes to genes
• Gene sets data

• Data definitions
• Defining condition subsets
• Data type scale (log)

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EXPANDER Preprocessing (II)

• Data Adjustments
• Missing value estimation (KNN or arbitrary)
• Merging conditions
• Normalization removal of systematic biases from
  the analyzed chips
• Implemented methods quantile, lowess
• Visualization box plots, scatter plots (simple,
  M vs. A)

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EXPANDER Preprocessing (III)

• Filtering Focus downstream analysis on the set
  of responding genes
• Fold-Change
• Variation
• Statistical tests (T-test)
• Standardization Create a common scale
• For each probe Mean0, STD1
• Log data (base 2)
• Fixed Norm (divide by norm of probe vector)

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Input data
Normalization/ Filtering
Links to public annotation databases
Visualization utilities
Clustering (CLICK, SOM, K-means, Hierarchical)
Biclustering (SAMBA)
Functional enrichment (TANGO)
Promoter signals (PRIMA)
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Cluster Analysis

• Partition the responding genes into distinct
  sets, each with a particular expression pattern
• Identify major patterns in the data reduce the
  dimensionality of the problem
• co-expression ? co-function
• co-expression ? co-regulation
• Partition the genes to achieve
• Homogeneity genes inside a cluster show highly
  similar expression pattern.
• Separation genes from different clusters have
  different expression patterns.

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Cluster Analysis (II)

• Implemented algorithms
• CLICK, K-means, SOM, Hierarchical
• Visualization
• Mean expression patterns
• Heat-maps

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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
• Over 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
Links to public annotation databases
Visualization utilities
Clustering (CLICK, SOM, K-means, Hierarchical)
Biclustering (SAMBA)
Functional enrichment (TANGO)
Promoter signals (PRIMA)
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Ascribe Functional Meaning to the Clusters

• Gene Ontology (GO) annotations for human, mouse,
  rat, chicken, fly, worm, Arabidopsis, Zebrafish
  and yeast.
• 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
Links to public annotation databases
Visualization utilities
Clustering (CLICK, SOM, K-means, Hierarchical)
Biclustering (SAMBA)
Functional enrichment (TANGO)
Promoter signals (PRIMA)
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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
P-value Enrichment factor Transcription factor
6.0x10-5 2.6 CREB
P-value Enrichment factor Transcription factor




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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Input data
Normalization/ Filtering
Links to public annotation databases
Visualization utilities
Clustering (CLICK, SOM, K-means, Hierarchical)
Biclustering (SAMBA)
Functional enrichment (TANGO)
Promoter signals (PRIMA)
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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
• Biclustering algorithmic approach

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

• 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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Expression Data Input File
conditions
probes
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ID Conversion 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)