Differential Network Enrichment Analysis Results - CRIC
(cric.csv)

Cluster Name# Nodes# Edges# Sig. Nodes# Diff. EdgesP-ValueQ-Value FDR
Cluster 11012874.843e-176.296e-16
Cluster 225324131.386e-129.012e-12
Cluster 339467215.506e-122.386e-11
Cluster 41416196.228e-072.024e-06
Cluster 554131.332e-063.463e-06
Cluster 61010154.031e-068.734e-06
Cluster 720260100.0057050.01059
Cluster 819210100.048740.07921
Cluster 924351120.22000.3178
Cluster 1022231120.27360.3557
Cluster 112330060.54850.6483
Cluster 1225341140.61120.6622
Cluster 1320240140.70060.7006


Comments :

1. To view cluster network, click the corresponding link in the table.

2. Node coloring reflects expression level.

3. For side-by-side networks, node coloring is proportional to group mean.

4. For aggregate networks, node coloring is proportional to mean change.

5. Differential edges are colored : an orange edge is more likely to be present in non-progressors; light blue edges are more likely to be present in progressors.

6. Network graphics can be zoomed. Individual nodes or the entire network can be repositioned by dragging.

7. Cluster layout is determined by the cose-bilkent 1 (< 180 nodes) or cose (> 180 nodes) algorithm.

8. To facilitate comparison, side-by-side networks are designed with parallel node layouts. If they differ, resizing your window and reloading can correct the problem.

9. Network graphics leverage the cytoscape.js 2 open-source graph theory library.


1Dogrusoz U, Giral E, Cetintas A, Civril A, Demir E. A Layout Algorithm For Undirected Compound Graphs, Information Sciences (2009) 179: 980-994

2Franz M, Lopes CT, Huck G, Dong Y, Sumer O, Bader GD. Cytoscape.js: a graph theory library for visualisation and analysis. Bioinformatics (2016) 32 (2): 309-311