Fig. 3

Filtering data using PRECOG’s quality indices. a Data is filtered using four quality indices, QI1 - “overall noisiness”, QI2 - “local noisiness”, QI3 - “number of spikes”, and QI4 - “curve collapses”. Upper panels: performance of each quality filter on the “aggregated 90 k set”, including almost 90,000 growth curves. x-axis shows QI score, y-axis shows number of growth curves flagged at each QI score setting. Blue bars = non-cumulative flagging, red line = cumulative flagging, dashed black line = selected QI score setting that flags a cumulative 5 % of curves. Lower panels: performance of the QI filter (QI score, y-axis) on the two selected benchmarking sets of 100 high- and 100 low-quality curves (x-axis). Dashed horizontal lines: performance at the 5 % rejection threshold selected based on the “aggregated 90 k set” growth curves. b Summary performance of all quality indices. Number of curves that obtain 0, 1, 2, 3 and 4 flags in the “aggregated 90 k set” at the selected threshold, where each quality index flags the worst 5 % of growth curves, i.e. 90 % of all curves were not scored by any of the quality indices while 2 % were scored by all four. c Summary performance of all quality indices. Number of QI flags in the high- and low-quality benchmarking sets. Colours indicate quality index responsible for the flagging, with blue = QI1, red = QI2, green = QI3 and purple = QI4. d Number of false positives and negatives in the two benchmarking sets, as a function of using various thresholds from the “aggregated 90 k set”