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Protocol publication

[…] To test whether the SOX2+ cells per cross section showed a spatial pattern along the AP axis or not, we used three different methods (, ). First, it was tested if the cell count data linearly depends on spatial position along the AP axis using Bayesian inference (see ‘Constant density’ in ). The slope was always smaller than 0.13 cells/mm and only significantly different from 0 (p<0.05) for 4 of the 15 replicates. Second, a model of two spatially homogeneous zones was fitted to the data using Bayesian inference (see ‘Constant density’ in ). Here, only 4 of the 15 replicates showed a significant difference in density between the two zones (p<0.05). These first two methods indicated that, for an average animal, there is no significant change of the number of SOX2+ cells per cross section along the AP axis. Third, the data was collapsed ignoring the spatial position, and the resulting cell count histogram was tested for being a normal distribution using the SciPy function scipy.stats.normaltest (; ). Only for one of the replicates the null hypothesis could be rejected (p<0.05), hence the SOX2+ cell density in an average animal was considered spatially homogeneous with Gaussian noise in this study.For each replicate the mean number of SOX2+ cells per cross section averaged over all measurements along the AP axis was calculated. To access whether there was a significant change in this mean number, the replicates were grouped according to their time post amputation. A one-way ANOVA-test showed no significant differences among the groups (p=0.08, see ‘Constant density’ in ). [...] If not stated otherwise, measurements are reported as mean ± standard error of the mean. In the figures * denotes p<0.05 and ** denotes p<0.01 for the respective test as indicated in the figure caption.Image analysis was performed with Fiji () and AxioVision Microscopy software (Zeiss). Data analysis was performed using the python modules bokeh (http://bokeh.pydata.org), iminuit (http://github.com/iminuit/iminuit), ipycache (http://github.com/rossant/ipycache), Jupyter Notebook (http://jupyter.org/), matplotlib (), numba (http://numba.pydata.org/), pandas (), probfit (http://github.com/iminuit/probfit), pymc (), SciPy () and uncertainties (http://pythonhosted.org/uncertainties/). […]

Pipeline specifications

Software tools SciPy, Bokeh, Jupyter Notebook, matplotlib
Applications Miscellaneous, WGS analysis
Organisms Ambystoma mexicanum