The MORPH Algorithm
MORPH = MOdule guided Ranking of candidate PatHway genes
high throughput data
Slides: Rachel E. Bell, June 2013
Motivation
Challenges in studying biological pathways
• Identify missing pathway members
• Information gaps on participating genes:
a) e.g. nature of interactions between metabolites and gene
expression
b) understanding control mechanisms, feedback, cross-talk
• Many genes in genome(s) have unknown function
Biological Pathways: Overview
What is a pathway?
A series of interactions between genes (proteins)
involved in performing a certain biological function
Cell input = extracellular/ endogenous:
e.g.: stress, changes in PH, UV exposure,
nutrients
Cell output = response:
e.g.: transcription of genes, sucrose degradation
MORPH Algorithm: Overview
OUTPUT
INPUT
High throughput
data of gene
expression,
networks and
biological pathways
Machine learning
and validation
methods
Predict genes
involved in biological
pathways
Other methods for functional prediction
Coexpression-based methods (& possibly pathways)
e.g.: ACT, GeneCat, ATED-II, MapMan
Assumptions:
1) Similar expression patterns -> similar function or regulation
2) Pathway genes -> coordinated expression
Network-based methods (& gene expression)
e.g: Markov random field (MRF) models , k-nearest neighbours (k-NN),
ADOMETA: coexpression, phylogeny, clustering on chrom., metabolic networks
Assumption:
Closer nodes -> common functions
Introduction: MORPH Algorithm
MORPH uses pathway information, gene expression
data and network information
Compared to other methods, MORPH:
• offers robustness (performs well on many pathways)
• increases networks coverage
• applied to different organisms
Talk outline
1. MORPH input types: (a) gene expression data, (b) pathways
and (c) networks
2. Types of clustering (modules) methods
3. The MORPH algorithm and validation
4. Results
5. Comparison to other methods
6. Summary
MORPH Introduction
Arabidopsis Thaliana
Solanum
Lycopersicum
(Tomato)
MORPH was developed on 2 model organisms
MORPH Input: Arabidopsis Thaliana
Pathways: 66 AraCyc, 164 MapMan
Preprocessing: filter pathways with <10 genes with expression data
Total 230 pathways, 2 sets
Gene Expression datasets: seedlings, tissues (leaves, roots,
flowers, seeds), seed developmental stages, DS1
Preprocessing: filter low variance and detection call, average replicates,
normalize to controls, standardize experiments
Total 216 GE profiles, 4 datasets, ~12500 genes
MORPH Input: Arabidopsis Thaliana
Metabolic (MD) Network (AraCyc)
Node = metabolic genes (enzymes)
Edges = nodes share a metabolite (reactant or product)
Preprocessing: remove most common metabolites (they connect enzymes with
weak functional associations)
Total: 1987 genes, 56244 interactions
PPI Network (PAIR & Interactome Map databases)
Node = genes (proteins)
Edges = interactions between proteins
Preprocessing: Unite (predicted & expt.) interactions from both databases
Total: 4642 genes, 149229 interactions
Talk outline
1. MORPH input types: (a) gene expression data, (b) pathways and
(c) networks
2. Types of clustering (modules) methods
3. The MORPH algorithm and validation
4. Results
5. Comparison to other methods
6. Summary
MORPH Goal
MORPH goal:
MORPH receives 3 types of input:
Given a specific biological pathway
1. Pathways
2. Gene expression data
3. Partitioning into modules
MORPH seeks candidate genes that
participate in (or regulate) the
pathway.
A key step in MORPH is the
partitioning of genes into
modules (clusters).
Assumptions of clustering data into modules
Q: Why use modules?
• Modules reflect broad functions
• Some functions are related to target pathway
• Pathway genes -> more coordinated expression
than random genes
Input: Partitioning Gene Modules and Networks
Different strategies for partitioning genes
Expression based
clustering
Annotation based
clustering
Enzyme/not enzyme
SOM = self-organizing map
(partitions all genes)
CLICK = CLuster Identification
via Connectivity Kernels
(partitions most genes)
Orthologs in rice &
maize/no orthologs
Network based
clustering
Matisse*
Markov cluster
algorithm (MCL)
Input: Partitioning Networks
Reminder: MATISSE seeks connected sub-networks with high
expression similarity
Interaction
High expression similarity
(Ulitsky & Shamir, 2007)
Goal: construct modules using gene expression data and networks
Problem: low coverage of MD network
Input: Partitioning Networks - MATISSE*
Motivation - overcome low coverage of networks
• Add genes with high correlation
• Repeat until module correlation <0.4
• Connectivity ignored
MATISSE* (modified MATISSE)
Results: Matisse* increased MD
network coverage to ~4500 genes
Matisse* performed similarly to
Matisse
Summary: Methods of Partitioning Gene Modules and Networks
Gene expression-based clustering
Clustering algorithm
Method
SOM
CLICK
Co-expression
Co-expression
Annotation-based clustering
Bipartition
Enzymes
Orthologs
Categories
Y/N
Y/N
No clustering - single module
Modules using network data
Clustering algorithm
Network
Markov cluster process (MCL)
PPI
MATISSE*
MATISSE*
PPI
MD network
Total of 8
clustering solutions
Talk outline
1. MORPH input types: (a) gene expression data, (b) pathways and
(c) networks
2. Types of clustering (modules) methods
3. The MORPH algorithm and validation
4. Results
5. Comparison to other methods
6. Summary
MORPH = MOdule guided Ranking of
candidate PatHway genes
MORPH is an algorithm for prioritizing novel candidate genes in a
given specific pathway.
Input:
1. Pathway genes S = {s1,s2,…sl}
2. Gene expression profiles
3. Partition solution for genes with gene
expression data: k modules = M1……Mk
4. Similarity function (D) Pearson/Spearman
Module-Guided Ranking Algorithm
Step #1: Partition genes into k modules
M1,M2,…,Mk
Step #2:
• Identify pathway genes s1,s2,…,sl and
candidate genes g
• ignore modules with no pathway genes
• add module for non partitioned
pathway genes
Step #3: Analyze each module separately
#2
#1
#3
Module-Guided Ranking
Algorithm
Step #4: For each g (candidate gene) in
module Mi calculate mean similarity with sj
(pathway genes) using gene expression data
candidate
genes
pre-defined
module
Similarity
function
(Pearson’s
Corr.)
pathway
genes in
module
provides ranking within module
#3
#4
Module-Guided Ranking
Algorithm
Step #5: Standardize mean similarity
scores within each module
candidate
genes
stdev / mean of
mean
similarity
scores of all
candidate
genes in
module Mi
#5
Step #6: Rank all candidate genes (using
standardized z-scores)
#6
How do we assess predictions of
many pathways?
Given a clustering solution
AND
gene dataset
Arabidopsis Thaliana
230 pathways
run algorithm for each pathway
Assessment of pathways using Leave-One-Out
Cross-Validation (LOOCV) procedure
Leave-One-Out Cross-Validation
(LOOCV) procedure
LOOCV generates for each pathway gene ->
SELF-RANK
Definition
SELF RANK of a gene is its position in ranking,
when left out of algorithm calculation
Meaning
Self rank of pathway gene = its overall strength
of association with remaining pathway genes
Kharchenko et al., 2006
Self-Rank Curve: AUSR score
LOOCV procedure
For each pathway S:
1. Remove one gene (v) -> S\{v}
2. Consider S\{v} = test set
3. Generate ranking of v using S\{v}
4. Repeat for every v
•
•
•
•
Calculate self-rank for all v in S
Create self-rank plot
Self-rank threshold of k=1..1000
Calculate area under self-rank
curve (AUSR)
(Random gene set of size 13 genes)
AUSR score assesses pathway
solutions (given input
combinations – discussed next)
(k)
Figure 2 Self-Rank plot of the Carotenoid Biosynthetic Pathway
contains 13 genes; SOM - clustering solution
Talk outline
1. MORPH input types: (a) gene expression data, (b) pathways and
(c) networks
2. Types of clustering (modules) methods
3. The MORPH algorithm and validation
4. Results
5. Comparison to other methods
6. Summary
Different input produces different AUSR scores
AUSR(seedlings) - AUSR(DS1)
Different: gene expression dataset
Same: MD network, Matisse*, 66
AraCyc Pathways
Inspired adoption of selection
(learning configuration)
FIGURE 3: Comparison of 2 gene expr. datasets
Learning Configuration
Every pathway tested with gene expression
dataset and partitioning solution (modules)
Definition
Learning configuration = combination of:
gene expression dataset (4)
AND
Clustering solution (8)
Total of 4x8 = 32 combinations
Machine Learning
LOOCV used to select optimal learning configuration (i.e. data
set and clustering) for each examined pathway.
LOOCV avoids overfitting, since test gene is left out.
MORPH applies a
selection procedure
Comparison of selection process to
other ‘fixed’ configurations
Results
• Better: enzymes or MD
network
• Poorer: PPI network, no
clustering, SOM, CLICK
& Orthologs
66 AraCyc metabolic pathways
(metabolic genes had higher corr.)
Selection improved on all
configurations
Figure 4: The average AUSR for each learning combination (gene
expr. dataset + clustering solution)
Results
29/66 AUSR > maximal
random score
AUSR > 0.75
15/66 - real pathways
0
- random
1.0
AUSR
times for each size)
0.5
randomly selected sets
with same size (repeated 100
0.0
Real vs. Random Pathways
66 AraCyc pathways
1.5
Robustness of selection method
Sizes
Figure 5: AUSR Scores of Real and Random Pathways
Talk outline
1. MORPH input types: (a) gene expression data, (b) pathways and
(c) networks
2. Types of clustering (modules) methods
3. The MORPH algorithm and validation
4. Results
5. Comparison to other methods
6. Summary
Comparison of MORPH to other methods: Arabidopsis
Thaliana pathways
66 AraCyc Pathways
*
Coexpression (no network data) methods using
reference datasets: ACT, DS1
Markov Ranking Field (MRF) methods (network data)
CMRF = total # of pathway gene in neighbourhood
WMRF= total similarity with path. genes in
neighbourhood
164 MapMan Pathways
*
k-Nearest Neighbour (k-NN) (network data)
Input:
Gene expression: seeds, tissues, seedlings, DS1
Networks: PPI and MD networks
Pathways: AraCyc, MapMan
Figures 4B & 4C
Figure 4D & 4E: Comparison to other methods
AraCyc pathways with AUSR>0.8
MapMan pathways with AUSR>0.7
k-NN predictor complements MORPH
My analysis: AUSR scores of MORPH and k-NN
Data retrieved from
Supplemental Data Set 3
k-NN is twice as good as MORPH for high AUSRs >0.9
(6 compared to 3)
Carotenoid Pathway and the
MORPH Candidate genes
Carotenoids are antioxidants, perform
stress response functions
Candidate Genes (Numbered Octagons)
SPS2 – Plastoquinone
pathway essential for
carotenoid pathway
• 8/25 top candidates have predicted
functions, with little details of roles
in plants
• Other predictions inc. genes with
similar functions – response to
oxidative stress
SQE3 –catalyzes the
precursor of a pathway
which is coordinated
expression with the
carotenoid pathway
Comparison of MORPH to other methods
93 Tomato pathways
Figure 7
Predictors include MORPH, k-NN, MRF-based, and coexpression based classifiers.
(A) Average and median AUSR scores.
(B) The number of pathways that had AUSR score above 0.7
Talk outline
1. MORPH input types: (a) gene expression data, (b) pathways and
(c) networks
2. Types of clustering (modules) methods
3. The MORPH algorithm and validation
4. Results
5. Comparison to other methods
6. Summary
Summary: Advantages of MORPH
1. Robust – different pathways
2. k-NN consider only genes in the network, MORPH increases network
coverage
3. k-NN more dependent on sub-networks diameter (higher diameter
lower AUSR), MORPH more robust
4. Self-rank k=1000 threshold for AUSR, ignores poor pathway gene
correlations
5. Potential useful predictions
Summary: Drawbacks of MORPH
1. If pathway genes not coherent, better select best/top
module(s) than average
2. Dependent on input quality (e.g. AraCyc > MapMan)
3. Predicts close pathways (drawback/advantage)
4. Requires known pathway info for predictions
Questions?
Top AUC scores for tested pathways
Pathway
Spearman AUC
Pearson AUC
Size
0.995115
0.994654
26
0.952
0.950643
14
Carotenoids Core pathway
0.859312
0.868158
13
tRNA charging pathway
0.832438
0.831844
32
gluconeogenesis
0.831634
0.833135
30
0.78642
0.770003
12
cysteine biosynthesis I
0.785097
0.787916
11
fatty acid &beta;-oxidation II (core pathway)
0.746601
0.752534
15
glycolysis I
0.742482
0.747914
44
glycolysis IV (plant cytosol)
0.730273
0.74716
44
Calvin-Benson-Bassham cycle
0.723338
0.729027
29
glucosinolate biosynthesis from homomethionine
0.721732
0.721641
11
homogalacturonan biosynthesis
0.720999
0.729749
12
glucosinolate biosynthesis from hexahomomethionine
0.719277
0.719277
11
glucosinolate biosynthesis from pentahomomethionine
0.719277
0.719277
11
ethylene biosynthesis from methionine
0.709665
0.766496
12
photosynthesis light reactions
Chlorophyllide biosynthesis I
triacylglycerol degradation
MORPH Classifications
3 types of input data:
Pathways genes (s1,s2,…sl)
Gene expression
Partition gene expression data
into k modules = M1,…,Mk
66 Arabidopsis Thaliana
4 datasets
8 Partitioning methods