Fig. 2 ∣. Food genome quantification on simulated ground-truth data.

a, Illustration of the mapping and filtering strategy used by MEDI. Individual k-mer assignments (LCA classifications) were used to assign consistency scores to reads and to filter reads with discordant mappings. b, Sampling strategy for the ground-truth data. All samples contain at least 90% background of an average bacteria, archaea and host background. Positive samples contain simulated reads from ten random food assemblies with exponentially increasing abundances. c, Quantification performance across simulated negative and positive controls. Points denoting a detected food item in a single sample are slightly jittered on the x axis to resolve overlaps. The black line denotes a linear regression fit (mean relationship between ground truth and observed) and the grey area is the 95% confidence interval around that mean. Fill colour denotes negative (red) or positive samples (blue). False-positive organisms are generally connected to organisms within the same taxonomic family. d, Probability of detecting a true-positive food item in a sample as a function of relative food item abundance (that is, detection power).