Table 1.
Survey of module networks software tools, in chronological order by their first release date.
Fig 1.
Flow chart for integrative module network inference with Lemon-Tree.
This figure shows the general workflow for a typical integrative module network inference with Lemon-Tree. Blue boxes indicate the pre-processing steps that are done using third-party software such as R or user-defined scripts. Green boxes indicate the core module network inference steps done with the Lemon-Tree software package. Typical post-processing tasks (orange boxes), such as GO enrichment calculations, can be performed with Lemon-Tree or other tools. The Lemon-Tree task names are indicated in red (see main text for more details).
Fig 2.
Comparison between Lemon-Tree and CONEXIC.
Gene Ontology (GO) enrichment of the co-expressed gene clusters, indicated by counting the number of GO categories having a lower p-value (A) and by comparing the sum of the quantity -log10(p-value) (B) for different global p-value cutoff levels (x-axis). (C) Relative enrichment of inferred interactions by Lemon-Tree and CONEXIC to known molecular protein-protein interactions (PPI), for increasing interaction distances.
Table 2.
GO enrichment for glioblastoma modules.
Fig 3.
Glioblastoma signaling pathway alterations for top hub regulators.
Copy number alterations for a selection of predicted hub regulators are indicated for canonical glioblastoma signaling pathways p53, RB and RTK/PI3K. Genes selected by the algorithm are indicated in black boxes, while light grey boxes depict genes that were not selected by the algorithm but are key factors for the pathway. Purple hexagons indicate phenotypes. Percentage of copy gain or loss is indicated by value and by color shades of red for gene gains and green for gene losses. The values are taken from GISTIC putative calls for low-levels gains or single-copy losses on 563 glioblastoma samples (data from the Broad institute).
Table 3.
High-scoring amplified gene hubs detected by Lemon-Tree.
Table 4.
High-scoring deleted genes detected by Lemon-Tree.
Fig 4.
Kaplan-Meier survival curves for a selection of top hub glioblastoma genes predicted by the Lemon-Tree algorithm.
The top three panels are genes having low-levels gains or high-level amplifications (magenta) compared to normal (blue), the bottom three panels are genes having single-copy loss or homozygous deletions (green) compared to normal (blue). All genes display significant differences between the groups (p < 0.05, see S6 Table for a full list of p-values). Patient with putative gene gains or losses have significantly worse prognosis (lower values on the y-axis). The x-axis on all figures represent the time in number of days.