Figure 4. Reducing the number of classifications, $n$, used to build the consensus dilution decreases the reproducibility and accuracy of the consensus measurement.

(A) The consensus dilution becomes less reproducible as the number of classifications is reduced, as measured by both the exact and essential agreements. (B) Likewise, the consensus dilution becomes less accurate as the number of classifications is decreased, however the highest level of exact agreement using the mean is obtained when $n=3$ and the mode, and to a lesser extent the median, are relatively insensitive to the number of classifications. These data are all with respect to the Expert +AMyGDA dataset.

Figure 4—figure supplement 1. Reducing the number of classifications, $n$, used to build the consensus dilution decreases the reproducibility and accuracy of the consensus measurement.

(A) The consensus dilution becomes less reproducible as the number of classifications is reduced, as measured by both the exact and essential agreements. (B) Likewise, the consensus dilution becomes less accurate as the number of classifications is decreased, however the highest level of exact agreement using the mean is obtained when $n=3$ and the mode, and to a lesser extent the median, are relatively insensitive to the number of classifications. These data are all with respect to the Expert dataset.

Figure 4—figure supplement 2. Altering the number of days incubation does not markedly affect the observed trends in reproducibility.

Shown are results for the Expert +AMyGDA dataset after (A) 7, (B) 10, (C) 14 and (D) 21 days of incubation. A previous study (Rancoita et al., 2018) showed that optimal results were achieved after 14 days incubation.

Figure 4—figure supplement 3. Altering the number of days incubation does not markedly affect the observed trends in accuracy.

Shown are results for the Expert +AMyGDA dataset after (A) 7, (B) 10, (C) 14 and (D) 21 days of incubation. A previous study (Rancoita et al., 2018) showed that optimal results were achieved after 14 days incubation.

Figure 4—figure supplement 4. Segmenting the drug images by the mean amount of growth in the positive control wells (Figure 6—figure supplement 3) does not markedly affect the reproducibility of the three consensus methods.

The plates are split into those with (A) low (≤ 10 %) growth, (B) medium (10 < growth ≤) growth and (C) high (> 50 %) growth. The drug images from the Expert +AMyGDA dataset were used and the proportion with MIC is the proportion of consensus readings that are a definite numerical minimum inhibitory concentration.

Figure 4—figure supplement 5. Segmenting the drug images by the mean amount of growth in the positive control wells (Figure 6—figure supplement 3) does not markedly affect the accuracy of the three consensus methods.

The plates are split into those with (A) low (≤ 10% %) growth, (B) medium (10 < growth ≤ 50 %) growth and (C) high (> 50 %) growth. The drug images from the Expert +AMyGDA dataset were used and the proportion with MIC is the proportion of consensus readings that are a definite numerical minimum inhibitory concentration.