Differential Network Enrichment Analysis Results - CPROBE
(cprobe.csv)

Cluster Name# Nodes# Edges# Sig. Nodes# Diff. EdgesP-ValueQ-Value FDR
Cluster 124334102.827e-083.392e-07
Cluster 218235118.990e-065.394e-05
Cluster 399240.00029660.001187
Cluster 420201120.0039770.01193
Cluster 548621370.016780.04028
Cluster 61721180.044360.07604
Cluster 71014020.041390.07604
Cluster 81822080.051730.07760
Cluster 91317190.15790.2105
Cluster 10109160.21100.2532
Cluster 1120230130.46140.5034
Cluster 1242590200.60110.6011


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 early stage subjects; light blue edges are more likely to be present in late stage subjects.

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