Figure to the comment on paper: DOI 10.1371/journal.pmed.1002786

2019-06-05T06:43:24Z (GMT) by Thomas Scriba

Performance of the three transcriptomic signatures for discriminating between progressors and non-progressors on samples collected within 24 months of TB diagnosis in the test subsets or the full South African and Gambian sets of the GC6-74 cohort. Bar graphs representing area under the ROC curve (AUC) and 95% CI values (error bars) for the Zak16-gene CoR[3], Sweeney3[4] or Maertzdorf4[5] signatures, measured by qRT-PCR, to discriminate between progressors and non-progressors in the 3 test sets of the GC6-74 cohort on the left, as shown in Suliman et al., 2018[2], or on the full South African and Gambian sets, as shown for the Zak16-gene CoR in Zak et al., 2016[3]. The numbers in brackets below each cohort represent the number of progressors included in the cohort (non-progressors were matched ~4:1 to the progressors). The GC6-74 cohort test sets were comprised of 14 progressors and 83 controls from South Africa, 8 progressors and 56 controls from The Gambia and 12 progressors and 48 controls from Ethiopia. The GC6-74 full cohorts were comprised of 40 progressors and 159 controls from South Africa and 26 progressors and 104 controls from The Gambia. Scores for the Maertzdorf4 and Sweeney3 signatures for the full GC6-74 cohorts were based on cycle threshold data generated by qRT-PCR (Fluidigm, USA) using the classification models published for each signature [5,6]. In cases where the 95% CI crosses 0.5, the error bar is in red, indicating non-significant validation. Areas under the ROC curves show that Sweeney3 now validates in the complete South African cohort, while Maertzdorf4 validates in the complete Gambian cohort.


References

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