10.1371/journal.pntd.0004302.g004 Renato Sathler-Avelar Renato Sathler-Avelar Danielle Marquete Vitelli-Avelar Danielle Marquete Vitelli-Avelar Armanda Moreira Mattoso-Barbosa Armanda Moreira Mattoso-Barbosa Marcelo Perdigão-de-Oliveira Marcelo Perdigão-de-Oliveira Ronaldo Peres Costa Ronaldo Peres Costa Silvana Maria Elói-Santos Silvana Maria Elói-Santos Matheus de Souza Gomes Matheus de Souza Gomes Laurence Rodrigues do Amaral Laurence Rodrigues do Amaral Andréa Teixeira-Carvalho Andréa Teixeira-Carvalho Olindo Assis Martins-Filho Olindo Assis Martins-Filho Edward J. Dick Jr Edward J. Dick Jr Gene B. Hubbard Gene B. Hubbard Jane F. VandeBerg Jane F. VandeBerg John L. VandeBerg John L. VandeBerg Systems biology strategy for analyzing innate immunity flow-cytometry data by heatmap and decision-tree analysis. Public Library of Science 2016 Trypanosoma cruzi Resemble Systems Biology Approaches systems biology analysis expression Human Chagas Disease BackgroundCynomolgus macaques ni nk 15 cynomolgus macaques Chagas disease ch Major Immunological Findings Observed granzyme imbricate biomarker networks 2016-01-28 12:41:28 Figure https://plos.figshare.com/articles/figure/_Systems_biology_strategy_for_analyzing_innate_immunity_flow_cytometry_data_by_heatmap_and_decision_tree_analysis_/1642034 <p>(A) Bioinformatics tool applied for single-cell data mining using heatmap computational method to preprocess flow cytometry data and to identify the innate immunity cell attributes. (B) Decision tree analysis identifies “root” (CD14<sup>+</sup>CD56<sup>+</sup>) and “secondary” (NK Granzyme A<sup>+</sup> and NK CD16<sup>+</sup>CD56<sup>-</sup>) cell attributes with higher accuracy to distinguish between non-human primates naturally infected with <i>T</i>. <i>cruzi</i> and non-infected controls. (C) Scatter distribution plots show the potential of selected biomarkers to discriminate infected from non-infected individuals. White rectangles indicate true positive (Chagas disease) and true negative (non-infected subjects) classifications. Gray rectangles indicate subjects that require the analysis of additional characteristics for accurate classification by the algorithm sequence proposed by the decision tree. (C) ROC curve analysis illustrating the cut-off points, the global accuracy (area under the curve–AUC) and performance indexes (sensitivity–Se, specificity–Sp and likelihood ratio–LR) for each selected biomarker.</p>