This repository contains the data and code accompanying the paper
Variability in reported midpoints of (in)activation of cardiac INa. Michael Clerx, Paul G. A. Volders, Gary R. Mirams. 2024
The data is all derived from an sqlite database (see base/mutations.sqlite
) that was created in 2016 for the study
Predicting changes to INa from missense mutations in human SCN5A. Michael Clerx, Jordi Heijman, Pieter Collins, Paul G.A. Volders. Scientific Reports. https://doi.org/10.1038/s41598-018-30577-5 and https://github.com/MichaelClerx/mutations-scn5a
The database can be opened with any SQLite compatible software.
The included scripts require Python 3.6 or newer, with the libraries listed in requirements.txt
.
- Figure 1 is generated by
f1-all.py
- Figure 3 is generated by
f2-correlation.py
- Figure 2 is generated by
f3-subgroups.py
- Supplementary table 1 is generated by
t1-multi-exp.py
- Supplementary table 2 is generated by
t1-cell-counts.py
- Supplementary table 3 is generated by
t3-all-midpoints.py
Numbers that appear in the text can be obtained using the scripts listed below.
d0-90th-percentile.py
- Calculates and outputs the 5th-95th percentile range of a normal distribution.
d1-counts.py
- Number of studies surveyed.
- Number of reports (1 or more per publication) of mean
Va
and/orVi
. - Number of reports of mean
Va
ANDVi
. - Total cell counts
d2-sigmas.py
- Min, max, median of standard deviation in mean
Va
inVi
.
- Min, max, median of standard deviation in mean
d3-means.py
- Min, max, median, and range of mean
Va
andVi
.
- Min, max, median, and range of mean
d4-within-exp
- Range of between-experiment variability in two studies with more than 1 experiment.
d5-temperatures
- Minimum and maximum temperatures.
f2-correlation.py
- Best fit slope and offset.
- Pearson correlation coefficient.
- Fixed-slope fit offset