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>