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Fig. 3 | BMC Bioinformatics

Fig. 3

From: Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

Fig. 3

We estimate from simulations the rate of detection of true positives (sensitivity) and negatives (specificity), visualised as box-plots from 100 independent points each. We compare TRaIT’s algorithms Edmonds and Chow-Liu with SCITE, the state-of-the-art for mutational trees inference in a setting of mild noise in the data, and canonical sample size. In SCS data noise is ε+=5×10−3;ε=5×10−2, in multi-region ε=5×10−2. Extensive results for different models, data type, noise and sample size are in Additional file 1: Figures S3–S16. a Here we use a generative model from [6] (Additional file 1: Figure S7-B). (left) SCS datasets with m=50 single cells, for a tumour with n=11 mutations. (right) Multi-region datasets with m=10 spatially separated regions, for a tumour with n=11 mutations. b We augment the setting in A-right with 2 random variables (with random marginal probabilty) to model confounding factors, and generated SCS data. c We generated multi-region data from a tumour with n=21 mutations, and a random number of 2 or 3 distinct cells of origin to model polyclonal tumour origination. d Spectrum of average sensitivity and specificity for Gabow algorithm included in TRaIT (see SM) estimated from 100 independent SCS datasets sampled from the generative model in Additional file 1: Figure S7-B (m=75, n=11). The true noise rates are ε+=5×10−3;ε=5×10−2; we scan input ε+ and ε in the ranges: ε+=(3,4,5,6,7)×10−3 and 3×10−2ε=≤7×10−2

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