1. Large Scale Data Integration
Curtis Huttenhower
Sequence and Expression

2. Functional Relationships
Two genes that work together to achieve similar cellular goals are functionally related
Proteins that co-complex: ribosomal, polymerase, ORC, etc. etc.
A TF and its target
Two enzymes catalyzing different steps in the same metabolic pathway
A membrane-bound receptor and a downstream phos. target
etc. etc. etc.
Genes that do really different stuff are considered to be functionally unrelated
Anything else is neither
Genes with unknown function
Genes in similar but non-identical pathways

3. Functional Relationships
How can we tell?
These databases classify every gene pair into one of three groups:
Functionally related
Unrelated
Neither

4. Data
Well, as long as we’re talking about gene pairs, how do pairs of genes act in data?
MANY MANY microarrays
Correlation
Colocalization
Yes/No
Shared miRNA sites
Yes/No
High
Two-hybrid
Yes/No
Same chromosome band
Yes/No
Low
Conserved TF sites
Yes/No
Affinity
Yes/No

5. Data These fall into two general categories: Yes/no (binary or 0/1)
Continuous (numerical scores)
Each dataset turns into a set of gene pairs labeled with small integers (or nothing).
G1 G2 0
G1 G3 1
G1 G4 -
G2 G3 -
G2 G4 0
G3 G4 1
Two-hybrid
Yes/No
G1 G2 0.9
G1 G3 0.75
G1 G4 0.1
G2 G3 -0.1
G2 G4 0.2
G3 G4 -0.5
G1 G2 4
G1 G3 3
G1 G4 1
G2 G3 0
G2 G4 2
G3 G4 0
Binning
Microarrays
Correlation

6. Integration Now, for each gene pair… DS1 DS2 DS3 DSN
We have an “answer” indicating whether it’s a related pair
G1 G2 1
G1 G2 0
G1 G2 1
G1 G2 -
G1 G2 3
DS1
DS2
DS3
DSN
And we have a bunch of datasets contributing their opinions
(i.e. experimental results)

7. Integration Let’s look at each dataset individually: DS1
Our answers know about a bunch of unrelated genes.
G1 G3 0
G3 G4 0
G9 G14 0
G10 G11 0 …
G1 G2 1
G1 G3 1
G4 G7 1
G10 G12 1 …
And a bunch of related genes.
Within each dataset, some subset of these pairs have values:
DS1
G1 G3 0
G3 G4 -
G9 G14 1
G10 G11 0 …
G1 G2 1
G1 G3 0
G4 G7 -
G10 G12 1 …

8. Integration Let’s look at each dataset individually: DS2
Our answers know about a bunch of unrelated genes.
G1 G3 0
G3 G4 0
G9 G14 0
G10 G11 0 …
G1 G2 1
G1 G3 1
G4 G7 1
G10 G12 1 …
And a bunch of related genes.
Within each dataset, some subset of these pairs have values:
DS2
G1 G3 -
G3 G4 0
G9 G14 0
G10 G11 - …
G1 G2 -
G1 G3 1
G4 G7 1
G10 G12 1 …

9. Integration
Within each dataset, let’s count up the number of times each value occurs for each type of gene pair (related or unrelated):
G1 G3 0
G3 G4 0
G9 G14 0
G10 G11 0 …
G1 G2 1
G1 G3 1
G4 G7 1
G10 G12 1 …
Functionally related? No
Yes 96 9
Dataset value
DS1 13 36 1
G1 G3 0
G3 G4 -
G9 G14 1
G10 G11 0 …
G1 G2 1
G1 G3 0
G4 G7 -
G10 G12 1 …

10. Integration
Within each dataset, let’s count up the number of times each value occurs for each type of gene pair (related or unrelated):
G1 G3 0
G3 G4 0
G9 G14 0
G10 G11 0 …
G1 G2 1
G1 G3 1
G4 G7 1
G10 G12 1 …
Functionally related? No
Yes
0.9
0.2
Dataset value
DS1
0.1
0.8 1
G1 G3 0
G3 G4 -
G9 G14 1
G10 G11 0 …
G1 G2 1
G1 G3 0
G4 G7 -
G10 G12 1 …

11. Integration
Each dataset is now represented by two probability distributions:
One for related gene pairs, one for unrelated
Related genes are more likely to bind
Related
Related genes are more likely to be highly correlated
Unrelated
Prob.
Prob.
This is particularly noticeable for continuous datasets, where these represent correlations. 1 1 2 3 4 5
DS1
value
DS5
value

12. Integration In the best case, datasets look like these:
In the worst case, they look like these:
Related
Prob.
Prob.
Unrelated 1 1
Prob.
Prob.
Prob. 1 1 1

13. Integration
The variation in a dataset’s probability distribution indicates how informative it is.
Some microarrays might look like these:
Even if genes are highly correlated, it doesn’t mean anything, because unrelated genes are also correlated.
Prob. 1 2 3 4 5
Everything’s really correlated! We can actually correct microarrays like this during preprocessing.
Prob. 1 2 3 4 5

14. For each dataset Di, we know P(Di = d | FR)
Prediction
Ok, so what?
Given what we know about some genes, we’ve learned something about datasets:
For each dataset Di, we know P(Di = d | FR)
What we want to know is, given some data, what can we predict about unknown genes?

15. Prediction We know: And we want to know: P(FR) P(D = d) P(D = d | FR)
The probability of a gene pair being functionally related
P(FR)
The probability of each dataset containing some value
P(D = d)
The probability of each value given a relationship (or not)
P(D = d | FR)
The probability that new genes are related given some data
P(FR | D = d)

16. Prediction
Enter Thomas Bayes:
Who established Bayes’ theorem:

17. Datasets with no data for a particular gene pair are ignored
Prediction
For each new gene pair, we can find its probability of being functionally related
Each dataset is weighted according to how informative we’ve calculated it to be
<insert math here>
Datasets with no data for a particular gene pair are ignored
And importantly for us, this all happens very quickly, regardless of the number of genes or datasets

18. Only the most confident edges are typically shown
Prediction
The result is that we produce a probability of functional relationship for each gene pair:
G1 G2 0.9
G1 G3 0.75
G1 G4 0.1
G2 G3 0.3
G2 G4 0.2 …
Which in turns translates into a fully connected interaction network:
Only the most confident edges are typically shown

19. Context Specificity We can do even better!
This process lets us figure out how much to “trust” each dataset.
But datasets can give better (or worse) results in particular biological areas:
Microarrays for ribosomal gene pairs
Microarrays for all gene pairs
Prob.
Prob. 1 2 3 4 5 1 2 3 4 5

20. Context Specificity 1
Prob.
So we don’t just learn one probability distribution per dataset.

21. Context Specificity 1
Prob.
So we don’t just learn one probability distribution per dataset.
We learn one probability distribution per dataset per biological process of interest!
Carbon
metabolism
Translation
Autophagy 1 1 1

22. Context Specificity 1
Prob.
So we don’t just learn one probability distribution per dataset.
We learn one probability distribution per dataset per biological process of interest!
This means that for each gene pair, we can predict a different probability of relationship per process of interest.
Carbon
metabolism
Translation
Autophagy 1 1 1
G1 G2 0.9
G1 G3 0.75
G1 G4 0.1
G2 G3 0.3
G2 G4 0.2 …
G1 G2 0.1
G1 G3 0.2
G1 G4 0.75
G2 G3 0.15
G2 G4 0.9 …
G1 G2 0.15
G1 G3 0.1
G1 G4 0.9
G2 G3 0.2
G2 G4 0.25 …

23. Context Specificity 1
Prob.
So we don’t just learn one probability distribution per dataset.
We learn one probability distribution per dataset per biological process of interest!
This means that for each gene pair, we can predict a different probability of relationship per process of interest.
Which in turn produces different interaction networks for each process.
Carbon
metabolism
Translation
Autophagy 1 1 1
G1 G2 0.9
G1 G3 0.75
G1 G4 0.1
G2 G3 0.3
G2 G4 0.2 …
G1 G2 0.1
G1 G3 0.2
G1 G4 0.75
G2 G3 0.15
G2 G4 0.9 …
G1 G2 0.15
G1 G3 0.1
G1 G4 0.9
G2 G3 0.2
G2 G4 0.25 …

24. Predicting Gene Function
Ok, so what?
We can now dig through these networks for interesting things:
YFG, its interaction partners, and their processes
Each edge represents specific datasets/publications
Dense clusters (new functional modules)
Areas that change a lot from process to process
What known disease genes are doing
Find relationships between TFs and their targets
Predicting function for uncharacterized genes

25. Predicting Gene Function
Suppose we have a whole interaction network for autophagy:
How do we predict new genes involved in the process?
Look at stuff “around” the known autophagy genes!

26. Predicting Gene Function
The bioPIXIE algorithm:
Given a network and some query genes, find the other genes most strongly connected to the whole query

27. Predicting Gene Function
The bioPIXIE algorithm:
Given a network and some query genes, find the other genes most strongly connected to the whole query
G1: = 1.1

28. Predicting Gene Function
The bioPIXIE algorithm:
Given a network and some query genes, find the other genes most strongly connected to the whole query
G1: = 1.1
G2: = 2.3

29. Predicting Gene Function
The bioPIXIE algorithm:
Given a network and some query genes, find the other genes most strongly connected to the whole query
G1: = 1.1
G2: = 2.3 …

30. Predicting Gene Function
The bioPIXIE algorithm:
Given a network and some query genes, find the other genes most strongly connected to the whole query
G1: = 1.1
G2: = 2.3 …
Then display the genes with the best scores and the strongest edges connecting them

31. Predicting Gene Function
The ratio algorithm:
Given a network and some query genes, find the other genes most specifically connected to the whole query

32. Predicting Gene Function
The ratio algorithm:
Given a network and some query genes, find the other genes most specifically connected to the whole query
G1: = 0.8

33. Predicting Gene Function
The ratio algorithm:
Given a network and some query genes, find the other genes most specifically connected to the whole query
G1: = 0.8
G2: = 1.5

34. Predicting Gene Function
The ratio algorithm:
Given a network and some query genes, find the other genes most specifically connected to the whole query
G1: = 0.8
G2: = 1.5 …
Then display the genes with the best scores and the strongest edges connecting them

35. Predicting Gene Function
These can differ a lot, particularly for “hubby” genes!

36. Predicting Gene Function
These can differ a lot, particularly for “hubby” genes!
bioPIXIE
G1: = 1.4
G2: = 1.0
Ratio
G1: = 1.2
This difference is exacerbated when the query isn’t itself strongly connected, since it makes it easy for hubby genes to dominate bioPIXIE’s results.
G2: = 1.7

37. Predicting Gene Function
How is this relevant to us?
Suppose we ask about just a few genes
If they’re not internally consistent in the data, bioPIXIE’s results are mostly hubs
This usually means that each predicted gene is only related to one or two of the query genes

38. Predicting Gene Function
This is a problem in the human genome, where our prior knowledge is relatively limited
The ratio algorithm generates predictions that are targeted towards the commonalities of the query: