Skip to main content
. 2020 Mar 18;16(3):e9195. doi: 10.15252/msb.20199195

Figure EV2. Sampling size affects statistical properties and accuracy of DRB calling.

Figure EV2

  1. Mean–variance plots for the benchmark OVCAR5 null subsamples (replica#2) and perturbed subsamples (35% perturbation degree; replicas #1 and #2). Local variance was calculated by averaging a tagwise variance over the mean counts using a 20 read‐count window. Mean counts were estimated using all the null or perturbed samples, respectively.
  2. Mean–variance plots for Mia‐PaCa‐2 null subsamples. Barcode read counts were median‐normalized. Local variance was calculated by averaging a tagwise variance over the mean counts using a 20 read‐count window.
  3. Scatter plots of median‐normalized read counts of Mia‐PaCa‐2 null subsamples.
  4. Local negative binomial goodness of fit was estimated using chi‐squared test or Cramer–von Mises test. Dispersion parameter of the negative binomial model was estimated locally over the window of 3 read counts using maximum‐likelihood estimator. P‐value of the chi‐squared test statistics was estimated using fitdistrplus::gofstat() function. P‐values of the Cramer–von Mises test were calculated by Monte Carlo bootstrap method as implemented in RVAideMemoire::cramer.test.