APO-SYS workshop on data APO-SYS workshop on data analysis and pathway analysis and pathway

charting charting

Igor UlitskyRon Shamir’s Computational Genomics Group

Part I: PresentationsPart I: PresentationsEXPANDER AMADEUSSPIKEMATISSE

Part II: Hands-on Part II: Hands-on SessionSession

EXPANDERMATISSESPIKE

EXPEXPression ression ANANalyzer and alyzer and DDisplayisplayERER

Adi Maron-KatzAdi Maron-KatzChaim LinhartChaim LinhartAmos TanayAmos TanayRani ElkonRani ElkonIsrael SteinfeldIsrael Steinfeld

Seagull ShavitSeagull ShavitIgor UlitskyIgor UlitskyRoded SharanRoded SharanYossi ShilohYossi ShilohRon ShamirRon Shamir

http://acgt.cs.tau.ac.il/http://acgt.cs.tau.ac.il/expanderexpander

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

Clustering(CLICK, SOM,

K-means, Hierarchical)

Input data

Biclustering(SAMBA)

Functional enrichment(TANGO)

Normalization/Filtering

Promoter signals (PRIMA)

Lin

ks to

pu

blic

ann

ota

tion

da

tab

ase

s V

isua

lizatio

n utilitie

s

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 genesGene sets data

Data definitions: Defining condition subsets Data type & scale (log)

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)

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 Mean=0, STD=1 Log data (base 2) Fixed Norm (divide by norm of probe vector)

Clustering(CLICK, SOM,

K-means, Hierarchical)

Input data

Biclustering(SAMBA)

Functional enrichment(TANGO)

Normalization/Filtering

Promoter signals (PRIMA)

Lin

ks to

pu

blic

ann

ota

tion

da

tab

ase

sV

isua

lizatio

n utilitie

s

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.

Cluster Analysis (II)• Implemented algorithms:

– CLICK, K-means, SOM, Hierarchical

• Visualization: – Mean expression patterns

– Heat-maps

Ionizing Radiation

Effectors (p53, BRCA1, CHK2)

DNArepair

Cell cyclearrest

Stressresponses

Survival

pathways

Apoptosis

Cell death pathways

Sensors

ATM

Double Strand Breaks

Example study: responses to ionizing radiation

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)

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

Major Gene Clusters – Irradiated Lymph nodeAtm-dependent early responding genes

Major Gene Clusters – Irradiated Lymph nodeAtm-dependent 2nd wave of responding genes

Clustering(CLICK, SOM,

K-means, Hierarchical)

Input data

Biclustering(SAMBA)

Functional enrichment(TANGOTANGO)

Normalization/Filtering

Promoter signals (PRIMA)

Lin

ks to

pu

blic

ann

ota

tion

da

tab

ase

sV

isua

lizatio

n utilitie

s

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.

Functional Enrichment - Visualization

Functional Categories

cell cycle control (p<1x10-6 )

Cell cycle control (p<5x10-6)Apoptosis (p=0.001)

Functional Categories

Clustering(CLICK, SOM,

K-means, Hierarchical)

Input data

Biclustering(SAMBA)

Functional enrichment(TANGO)

Normalization/Filtering

Promoter signals (PRIMA)

Lin

ks to

pu

blic

ann

ota

tion

da

tab

ase

sV

isua

lizatio

n utilitie

s

? ? ? ? ?p53TF-C TF-B TF-ANEW

ATM

g3g13 g12 g10 g9 g1g8 g7 g6 g5 g4g11 g2

Hidden layer

Observed layer

Clues are in the

promoters

Identify Transcriptional Regulators

‘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

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

Promoter Analysis - Visualization

PRIMA - Results

Transcription factor

Enrichment factor

P-value

Transcription factor

Enrichment factor

P-value

CREB2.66.0x10-5

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

Clustering(CLICK, SOM,

K-means, Hierarchical)

Input data

Biclustering(SAMBA)

Functional enrichment(TANGO)

Normalization/Filtering

Promoter signals (PRIMA)

Lin

ks to

pu

blic

ann

ota

tion

da

tab

ase

sV

isua

lizatio

n utilitie

s

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

* Bicluster (=module) : subset of genes with similar behavior in a subset of conditions

* Computationally challenging: has to consider many combinations of sub-conditions

Biclustering: SAMBAStatistical Algorithmic Method for Bicluster Analysis

A. Tanay, R. Sharan, R. Shamir RECOMB 02

Biclustering Visualization