Systematic analysis of genome-wide
fitness data in yeast reveals novel gene
function and drug action.
M. Hillenmeyer (Stanford), E. Ericson (Toronto), R. Davis
(Stanford), C. Nislow (Toronto), D. Koller (Stanford) and G. Giaever
(Toronto)
Published in Genome Biology 2010
Presented By: Yaron Margalit
1
• Deeply investigating and analysis chemical
genome wide fitness data.
– Predict gene-functional
– Predict protein-drug interactions
– Have new observations or/and extend previous
ones with the new data.
2
Outline
• Brief introduction
• Large-scale genome-wide Dataset
• Co-fitness
– Motivation and Definition
– Implementation
– Results
• Co-inhibition
– Motivation and Definition
– Implementation
– Results
• Predict drug-target interactions
– Motivation
– Model
– Results
• Summary
3
Outline
• Brief introduction
• Large-scale genome-wide Dataset
• Co-fitness
– Motivation and Definition
– Implementation
– Results
• Co-inhibition
– Motivation and Definition
– Implementation
– Results
• Predict drug-target interactions
– Motivation
– Model
– Results
• Summary
4
Brief Introduction - Reminder
• Deletion Mutants Sensitive to a Particular
Drug Should be Synthetically Lethal with the
Drug Target
Synthetic Lethal Interactions
Synthetic Chemical Interactions
Alive
Alive
Drug
Alive
Alive
Drug
Dead
Dead
5
CGI (C for chemical) vs. GI
GI
genes
Library genes
CGI
chemicals
6
CGI notes
• Some notes we need to take into account
when we get into CGI:
– Inactivation of the target protein function caused
by the compound is not complete
– Multi-drug resistant genes: Some mutant are
hypersensitive to many drugs of different types
(many promiscuous)
– Side effects: compound cause inactivation of
other proteins and not only the specific gene
required
7
Outline
• Brief introduction
• Large-scale genome-wide Dataset
• Co-fitness
– Motivation and Definition
– Implementation
– Results
• Co-inhibition
– Motivation and Definition
– Implementation
– Results
• Predict drug-target interactions
– Motivation
– Model
– Results
• Summary
8
Hillenmeyer et al. Science 2008
Chemical genomic
• Study relationship between small molecules
and genes.
• Small molecules:
– Drugs – FDA approved
– Chemical probes – well characterized
– New compounds – unknown biological activity
10
Saccharomyces cerevisiae
(the “beer yeast”)
• “Beer yeast” consist of ~ 6000 genes.
• ~ 1000 genes are essential
• Dataset include large diploid deletion collections
– ~ 6000 heterozygous gene deletion strains (+/-)
– ~ 5000 homozygous deletion strains (-/-)
– Only 5000 because about 1000 are essential (genes that a
cell cannot live without regardless of conditions it grows
in)
11
Data source
• Used deletion sets to study cell growth rate
(fitness) response to conditions (small
compounds and environmental stressors):
– 726 conditions per heterozygous deletion strain
– 418 conditions per homozygous deletion strain
• Homozygous or heterozygous gene mutation in
combination with a drug (or other treatment)
causes growth fitness defect (reduction)
– Compared to no-drug control
12
Outline
• Brief introduction
• Large-scale genome-wide Dataset
• Co-fitness
– Motivation and Definition
– Implementation
– Results
• Co-inhibition
– Motivation and Definition
– Implementation
– Results
• Predict drug-target interactions
– Motivation
– Model
– Results
• Summary
14
co-fitness
• Definition: co-fitness value - the similarity of two
genes fitness score across experiments
• Intuitive:
– Gene-drug interaction: retrieve fitness defect score:
compare gene’s intensity in a specific treatment to the
same gene’s intensity in the control (no-drug)
– Result to gene-gene relationship: Calculate
correlation (similarity) between two genes
(i.e. “how much genes are sensitive to similar drugs”)
• co-fitness was calculated separately for the
heterozygous and homozygous datasets
15
co-fitness – the similarity of two genes
• How to calculate fitness defect (reduction)
gene-drug interaction:
–
–
–
–
Z-score
P-value
Log ratio
Log P-value
• Example of such a score, log rate:
Where:
- mean intensity of i replicate across multiple control conditions
(controls)
- intensity of i replicate under treatment t (cases)
16
co-fitness – the similarity of two genes
• Calculate correlation gene-gene relationship.
• Example of co-fitness, distance metric:
Euclidean distance:
Where:
- i replicate, defect score of gene x under
treatment t
- i replicate, defect score of gene y under
treatment t
17
co-fitness – the similarity of two genes
• Goal: Quantify the degree to which co-fitness
can predict gene function and compare its
performance to other similarities types
(datasets)
• Several similarities – correlation based were
tested:
– Pearson
correlation
– Spearman rank
correlation
– Euclidean distance
– Bicluster cooccurrence count
– Bicluster Pearson
correlation
18
co-fitness – picking best distance metric
19
co-fitness – the similarity of two genes
• So far: We tested and found that Pearson
correlation exhibit the best performance for
co-fitness
• Use co-fitness and evaluate its prediction of
gene functional
20
co-fitness predicts reference network
• Evaluate co-fitness prediction on expertcurated reference interaction (“reference
network”) – gold standard compared dataset.
• Each dataset compared to the reference
network:
– Reference network divided into 32 GO slim
biological sub-net works
– Each gene pair was assigned to the sub-network if
both genes were annotated to that process
21
co-fitness predicts reference network
22
23
24
co-fitness more results
• Essential genes were co-fit with other
essential genes more frequently:
– 40% essential genes co-fit with essential genes
compared to 23% for non essential genes.
• Pairs of co-complexed genes (genes encoded
within same protien complex) increased cofitness with other members of the complex.
25
co-fitness more results
26
co-fitness application example
• Find nonessential proteins that might be essential
for optimal growth in conditions.
– Idea comes from previous study saying proteins that
are essential in rich medium (type of condition) tend
to cluster into complexes (i.e. essential complex).
• Application:
– Define complex to be essential if 80% of its members
are essential.
– Run over all co-fitness values and search for a
significant essential complexes.
27
co-fitness application example
• Create a synthetic data for each condition:
– Generate 10,000 a random distribution – reassign
genes to complexes (but maintain complexes size)
– Protein complex is essential if at least 80% of its
genes had a significant (P < 0.01 cutoff) fitness
defect.
• Identify condition with significantly more
essential complexes if this essential complex
was not observed essential in any of the
10,000 permutations.
28
Outline
• Brief introduction
• Large-scale genome-wide Dataset
• Co-fitness
– Motivation and Definition
– Implementation
– Results
• Co-inhibition
– Motivation and Definition
– Implementation
– Results
• Predict drug-target interactions
– Motivation
– Model
– Results
• Summary
29
Co-inhibition
• Definition: co-inhibition value: correlation
between drug1 and drug2 s.t. inhibit similar
genes.
• Intuitive (similar to co-fitness):
– Gene-drug interaction: retrieve fitness defect score:
compare gene’s intensity in a specific treatment to the
same gene’s intensity in the control (no-drug)
– Result to drug-drug relationship: Calculate correlation
(similarity) between two drugs
(i.e. “how much drugs inhibit similar genes”)
• co-inhibition was calculated separately for the
heterozygous and homozygous datasets
30
Co-inhibition
• Claim that indicated from small scale databases:
High co-inhibition value tend to share chemical
structure and mechanism of action in the cell
• Goal: use co-inhibition to predict mechanism of
action and therefore identify drug targets or
toxicities
• Next steps:
–
–
–
–
Calculate co-inhibition (1)
Define chemical structural similarity (2)
Define chemical therapeutic (action) use (3)
Verify claim (1,2,3 share high percent similarity)
31
Co-inhibition
• Claim that indicated from small scale databases:
High co-inhibition value tend to share chemical
structure and mechanism of action in the cell
• Goal: use co-inhibition to predict mechanism of
action and therefore identify drug targets or
toxicities
• Next steps:
–
–
–
–
Calculate co-inhibition (1)
Define chemical structural similarity (2)
Define chemical therapeutic (action) use (3)
Verify claim (1,2,3 share high percent similarity)
32
Calculate co-inhibition (1)
• How to calculate fitness defect (reduction)
gene-drug interaction – Similar to co-fitness
–
–
–
–
Z-score
P-value
Log ratio
Log P-value
• Example of such a score, log rate:
Where:
- mean intensity of i replicate across multiple control conditions
(controls)
- intensity of i replicate under treatment t (cases)
33
Calculate co-inhibition (1)
• Calculate correlation drug-drug relationship.
• co-inhibition, distance metric that was used
Pearson correlation:
Where:
- i replicate, defect score of drug x with
gene g
- i replicate, defect score of drug y with
gene g
34
Co-inhibition
• Claim that indicated from small scale databases:
High co-inhibition value tend to share chemical
structure and mechanism of action in the cell
• Goal: use co-inhibition to predict mechanism of
action and therefore identify drug targets or
toxicities
• Next steps:
–
–
–
–
Calculate co-inhibition (1)
Define chemical structural similarity (2)
Define chemical therapeutic (action) use (3)
Verify claim (1,2,3 share high percent similarity)
35
Define chemical structural similarity (2)
• Model each chemical to substructure motifs
• Construct substructure vectors (containing all
possible substructures in our case 554 types)
and set a value between 0-1 for each
substructure is it similar to chemical structure
or not.
• Calculate structural similarity between 2 drugs
by a distance metric.
36
Define chemical structural similarity (2)
• Model each chemical to substructure motifs
• Construct substructure vectors (containing all
possible substructures in our case 554 types)
and set a value between 0-1 for each
substructure is it similar to chemical structure
or not.
• Calculate structural similarity between 2 drugs
by a distance metric.
37
Define chemical structural similarity (2)
• Construct substructure vectors (containing all
possible substructures in our case 554 types)
and set a value between 0-1 for each
substructure is it similar to chemical structure
or not.
– We will show 3 different ways to do that
38
chemical structural similarity –
substructure vectors
• First way Binary identifier
• Simple binary vector where the value is 1 if
the compound contains the substructure and
0 otherwise.
39
chemical structural similarity –
substructure vectors
• Second way IDF
• Convert binary indicator to an inverse document
frequency (IDF). IDF score for substructure mofit
i (regardless of the chemical):
C – number of compounds
Cj – number of compounts that contain motif i
• Set 0 if compound does not contain substructure
and IDF > 0 otherwise.
40
chemical structural similarity –
substructure vectors
• Third way Binary-IDF
• Convert binary indicator to an inverse
document frequency (IDF).
• Convert back to binary using a threshold on
IDF value (for IDF > X threshold set 1
otherwise 0)
41
Define chemical structural similarity (2)
• Model each chemical to substructure motifs
• Construct substructure vectors (containing all
possible substructures in our case 554 types)
and set a value between 0-1 for each
substructure is it similar to chemical structure
or not.
• Calculate structural similarity between 2 drugs
by a distance metric.
42
Calculate chemical structural similarity (2)
• For the binary data (first and third ways) they
tested as a distance metric:
– Tanimoto (Jaccard) coefficient
– Hamming distance
– Dice coefficient
• For the IDF data (second way) they tested:
–
–
–
–
–
Cosine distance Pearson correlation
Spearman correlation
Euclidean distance
Kendall’s Tau
City-block distance
43
Calculate chemical structural similarity (2)
• Greatest relationship done by using Binary-IDF
with (threshold > 2.5)
• Distance metric was Tanimoto (Jaccard)
coefficient
• Suggests that structure similarity should be
defined by a less common substructures.
44
Co-inhibition
• Claim that indicated from small scale databases:
High co-inhibition value tend to share chemical
structure and mechanism of action in the cell
• Goal: use co-inhibition to predict mechanism of
action and therefore identify drug targets or
toxicities
• Next steps:
–
–
–
–
Calculate co-inhibition (1)
Define chemical structural similarity (2)
Define chemical therapeutic (action) use (3)
Verify claim (1,2,3 share high percent similarity)
45
Define chemical therapeutic (action) use (3)
• Use known data:
– Define pair of compounds to be co-therapeutic if
they share annotation at level 3 of the WHO
(classification of drug uses) ATC hierarchy.
46
Co-inhibition
• Claim that indicated from small scale databases:
High co-inhibition value tend to share chemical
structure and mechanism of action in the cell
• Goal: use co-inhibition to predict mechanism of
action and therefore identify drug targets or
toxicities
• Next steps:
–
–
–
–
Calculate co-inhibition (1)
Define chemical structural similarity (2)
Define chemical therapeutic (action) use (3)
Verify claim (1,2,3 share high percent similarity)
47
co-inhibition - is it really true?
• Counted pairs of compounds that have:
– Positive co-inhibition (correlation > 0)
– Shared therapeutic class
– Measurable structural similarity
• From this counting:
– 70% did not share structural similarity (Tanimoto
similarity < 0.2)

48
co-inhibition – results
• Limited correlation between co-inhibition and
similar chemical structure.
49
co-inhibition – results
• Significant relationship between shared ATC
therapeutic class and co-fitness
50
co-inhibition – results
• Some observation of
differences between shared
structure and common
therapeutic
51
co-inhibition – results
• Co-inhibition can reveal both
shared structure and common
therapeutic
• specially useful for the non
target drug use
52
Outline
• Brief introduction
• Large-scale genome-wide Dataset
• Co-fitness
– Motivation and Definition
– Implementation
– Results
• Co-inhibition
– Motivation and Definition
– Implementation
– Results
• Predict drug-target interactions
– Motivation
– Model
– Results
• Summary
53
Predict drug-target interactions
• Method to address the difficult task of
predicting drug targets.
• Goal:
– Use genomic data to better predict the protein
target of a compound
– Distinguish which of the sensitive genes is most
likely drug target
• Let’s use a Machine-learning algorithm!
54
What is Machine learning
• Automated learning.
• There are many types of machine learning, we
will focus on Supervised, Batch learning (our
case).
– “Supervised” : Based Training set so that learner
should figure out a rule for new arrival data.
– “Batch” : Retrieve first training set then run on
test set.
55
Machine learning example
• Papayas example
56
Predict drug-target interactions
• Method to address the difficult task of
predicting drug targets.
• Learn to estimate an “interaction score”
between compound c and gene g:
– Have a training set
– Set several key features
– Produce an estimation for compound c and gene g
– Test algorithms using “cross-validation”
57
Predict drug-target interactions
• Method to address the difficult task of
predicting drug targets (protein-compund
interaction).
• Learn to estimate an “interaction score”
between compound c and gene g:
– Have a training set
– Set several key features
– Produce an estimation for compound c and gene g
– Test algorithms using “cross-validation”
58
Training set (1)
• Experts identify known protein interactions in
yeast (with literature evidence) – 83 training
data
• In order to test our learning algorithm, have a
negative test set of 83 random combinations
of compound-protein interactions.
59
Training set (2)
• Use known dataset DrugBank for Humans and
map it to yeast by application BLASTp. – 46
training data
• Again another negative test set of 46 random
combinations
60
Predict drug-target interactions
• Method to address the difficult task of
predicting drug targets.
• Learn to estimate an “interaction score”
between compound c and gene g:
– Have a training set
– Set several key features
– Produce an estimation for compound c and gene g
– Test algorithms using “cross-validation”
61
Key features
• Features used in learning drug targets over all
20 features:
– Fitness defect score of the heterozygous data (two
features)
• Log ratio
• P-value
– Gene sensitivity frequency (one feature)
• Number of compounds causing sensitivity in protein.
– Drug inhibition frequency (one feature)
• Number of inhibit genes.
62
Key features (2)
• Features used in learning drug targets over all
20 features:
– Phenotype in rich medium (one feature)
– Chemical structure similarity enrichment of
putative compounds (three features)
• Sensitive gene for similar compounds might increase
confidence
• Number of other compounds that share a common
motif with the requested compound
• Average structural similarity score
63
Key features (3)
• Features used in learning drug targets over all
20 features:
– Co-inhibition “secondary compound” fitness
defect scores (ten features)
• Top 10 co-inhibiting compounds with the requested
compound
– Co-inhibition “secondary compound” summary
statistics fitness defect scores (two features):
• Mean
• Median
64
Machine learning algorithm
• Several machine learning algorithms were
used:
– Random forest
– Naïve Bayes
– Decision Stump
– Logistic regression
– SVM
– Decision tree
– Bayesian Network
65
Machine learning validation
• 10-fold Cross-validation method:
– Partition the training set into 10 subsets
– For each subset, a predictor is trained on the
other 9 subsets and then its error is estimated
using the subset.
– Pick algorithm with minimal errors.
66
Random forest is the best algorithm
67
Random Forest is really useful?
Why not just use fitness defect score?
68
69
Intro to decision tree
70
From decision tree to Random Forest
• Forest = Multiple decision trees
– The output of every decision tree in the “forest” is
averaged
• What’s random in a Random Forest?
– Random a subset of the explanatory variables
– Random a subset of the training data
• Why random?
– Avoids modeling noise
– Decision trees are greedy: Using the best split at every
point might overlook better solutions in the long-term
(stuck at local optimum)
71
Why random forests are great
• Non parametric and non-linear:
– No specific relationship between our explanatory
variables and our predictions.
– Logistic regression (other algorithm) would impose for
example a specific relationship between the
explanatory variables and the predicated value.
– Random forest is flexible. No need for special
assumptions or specific decisions. All decision are
random.
– Another advantage: incorporate interactions
between all the explanatory variables.
72
Random forest algorithm
• Each tree:
– Take number of training cases and number of
variables (key features)
– Calculate the best split cases on these variables.
– Each tree is grown until the end (full tree)
• Prediction:
– Each label assigned to a value according to each
tree.
– Take the average vote.
73
Prediction results
• Authors run algorithm over the genome-wide
dataset
• 4 of top 10 predicated interactions were
validated in lab
74
Summary
• We have shown a systematic analysis for
genome-wide large scale fitness data.
– Introduced co-fitness value for gene-gene
relationship . Helpful to predict gene functionality
– Defined similar drug relationship by co-inhibition
value. Helpful to show chemical similar structure
and therapeutic use.
– Showed a learning algorithm to predict drugtargets
75
Questions
76
77
Calculate co-fitness
• Pearson correlation:
Where:
- i replicate, defect score of gene x under
condition g
- i replicate, defect score of gene y under
condition g
78