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Fig. 1 | BMC Genomics

Fig. 1

From: Sort-seq under the hood: implications of design choices on large-scale characterization of sequence-function relations

Fig. 1

Sort-seq scheme. a Input distributions of single-cell fluorescence measurements for individual isolated variants, plotted as histograms. Distributions for these variants may have different mean and variance from the wild-type or reference variant. b The distribution is much broader for the mixed library of variants. Cells are sorted by flow cytometry and sequenced in parallel. Sort gates define each sorted population and span the fluorescence range between and u. In this configuration, gates are evenly spaced with width w on a log scale. c The input distribution of single-cell fluorescence measurements for a single variant is characterized by input parameters. The output distribution represents the proportions of sort-seq reads within each gate. Statistical estimators are used to infer the input parameters from the output distribution. d The performance of an estimator is characterized by examining the probability that it yields a certain value in an experiment (colored curve) compared with the true input value (indicated in black). For a biased estimator (red) this probability distribution is not centered around the true input parameter, and one defines the bias as the distance between its mean and the true value. Unbiased estimators (blue, green) are centered around the input. The efficiency of such estimators depends on the width of the distribution, such that with a given sample, a more efficient estimator (green) leads to a more precise estimate

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