1. Time-Course Network Enrichment
TiCoNE
Time-Course Network Enrichment
November 2016
by Jan Baumbach

2. vs.
Jan Baumbach
Computational Biology group
University of Southern Denmark
Odense, DK

3. Time series network enrichment

4. Time series gene expression: A network perspective
Application:
Time series data enrichment
Network: PPI + GRN
Cell type: Lung cell samples
Study object: After infection with Influenza or Rhino virus over ten time points
Read-out: Gene expression
Human bronchial epithelial cells (BEAS-2B) Rhinovirus, Influenza virus or both, and RNAs profiled after 2, 4, 6, 8, 12, 24, 36, 48, 60 and 72hrs.
Kim TK et al. A systems approach to understanding human rhinovirus and influenza virus infection. Virology 2015 Dec;486:

5. Time series gene expression: A network perspective
Application:
Time series data enrichment
Network: PPI + GRN
Cell type: Lung cell samples
Study object: After infection with Influenza or Rhino virus over ten time points
Read-out: Gene expression

6. Time series gene expression: A network perspective
Application:
Time series data enrichment
Network: PPI + GRN
Cell type: Lung cell samples
Study object: After infection with Influenza or Rhino virus over ten time points
Read-out: Gene expression
Result: Temporally (in)active
pathways during Influenza vs. Rhino virus infection

7. Time series gene expression: A network perspective
Time-Series Data
Clustering
Network

8. Time series gene expression: A network perspective
...
Initial Clustering
Cluster prototypes

9. Time series gene expression: A network perspective
...
Human
Augmented
Clustering
Initial Clustering
Cluster prototypes
Remove non-fitting objects
Split a cluster
Merge clusters
...
...
...
...

10. Time series gene expression: A network perspective
How many network interactions can we expect between two clusters?
How many do we actually see?
Discrepancy may indicate functional relationships.

11. Time series gene expression: A network perspective
How many network interactions can we expect between two clusters?
How many do we actually see?
Discrepancy may indicate functional relationships.
Here: Are four edges between the four blue and the four green cluster more/less than expected by chance?

12. Time series gene expression: A network perspective
How many network interactions can we expect between two clusters?
How many do we actually see?
Discrepancy may indicate functional relationships.
Here: Are four edges between the four blue and the four green cluster more/less than expected by chance?
Calculate expected number of edges between any four nodes to any four nodes (with the same node degrees as the green/blue ones, using the joint node degree distribution).
Calculate log-odds score S (#observed edges / #expected edges).
Create 1,000 random networks (crossover 4*|E| edges). For each, compute log-odds score Si.  Score distribution.
Empirical p-value = rel. frequency of Si >= S

13. Time series gene expression: A network perspective
How many network interactions can we expect between two clusters?
How many do we actually see?
Discrepancy may indicate functional relationships.
Here: Are they similar between two conditions (e.g. Influenza vs. Rhino)?
Influenza
Rhino

14. Time series gene expression: A network perspective
How many network interactions can we expect between two clusters?
How many do we actually see?
Discrepancy may indicate functional relationships.
Here: Are they similar between two conditions (e.g. Influenza vs. Rhino)?
Infl.
Rhino # p 2
Influenza
Rhino

15. Time series gene expression: A network perspective
How many network interactions can we expect between two clusters?
How many do we actually see?
Discrepancy may indicate functional relationships.
Here: Are they similar between two conditions (e.g. Influenza vs. Rhino)?
p = empirical p-value for 1,000 networks with random color assignment
Infl.
Rhino # p 2
0.07
Influenza
Rhino

16. Time series gene expression: A network perspective
How many network interactions can we expect between two clusters?
How many do we actually see?
Discrepancy may indicate functional relationships.
Here: Are they similar between two conditions (e.g. Influenza vs. Rhino)?
p = empirical p-value for 1,000 networks with random color assignment
Infl.
Rhino # p 2
0.07 3
0.01
0.1 1
0.3
Influenza
Rhino

17. Time series gene expression: A network perspective
More/less edges between pairs of clusters of genes (of temporally similar expression) than expected by chance after Rhino virus infection.
[more]
[less]

18. Time series gene expression: A network perspective
More/less edges between pairs of clusters of genes (of temporally similar expression) than expected by chance after Rhino virus infection.
[more]
[less]
Graph representation (edge weights: log-scaled p-value).

19. Time series gene expression: A network perspective
Subnetworks enriched with genes having
network-associated temporal response patterns
after Influenza infection but not after Rhino virus infection.

20. Time series gene expression: A network perspective
Subnetworks enriched with genes having
network-associated temporal response patterns
after Influenza infection but not after Rhino virus infection.
Known Influenza gene complex (literature).

21. Time series gene expression: A network perspective

22. Thanks! OR
 Jan Baumbach