Original networks, NVC networks and COPD data sets used in: Enhancement of COPD biological networks using a web-based collaboration interface

<p>Original networks, NVC networks and their descriptions.<br>The file contains the names of the original networks (as they were published), agglomerated NVC networks (as presented on the Bionet website), and network descriptions. The 15 networks that were discussed during jamboree are indicated by “X” in the column Discussed in Jamboree.</p> <p>COPD data sets, their descriptions, and the comparisons used to build the COPD models during Phase 1.<br>Reverse causal reasoning was performed using COPD and emphysema data sets from lung, small airway, and alveolar macrophages of early COPD patients and healthy smokers. Data Sets, the Gene Expression Omnibus (GEO) used to build the COPD networks. SCs, state changes defined using differentially expressed genes that meet the following criteria: FDR adjusted p<0.05, fold change ≥1.3, and minimum expression of 100 (for Affy platforms). HYPs, mechanisms or hypotheses predicted from the SCs and the Selventa Knowledgebase [1] with the following cutoffs: richness p<0.1, concordance p<0.1.</p> <p>Early COPD was defined as Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages 1 and 2.<br>The three small airway data sets were merged using ComBat [2] because of the small sample size of early COPD patients within each data set.<br>Lone emphysema is defined in the GSE10006 data set as patients who have normal spirometry but decreased transfer factor and evidence of emphysema on chest computed tomography scans. The lone emphysema data were selected because they might be useful in understanding COPD onset.</p> <p>References<br>1. Catlett NL, Bargnesi AJ, Ungerer S, Seagaran T, Ladd W, Elliston KO, Pratt D: Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data. BMC bioinformatics 2013, 14:340.<br>2. Chen C, Grennan K, Badner J, Zhang D, Gershon E, Jin L, Liu C: Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods. PloS one 2011, 6:e17238.</p> <p> </p>