Hello,
I appreciate the approach taken by this framework a lot, and I would like to implement it in my publications. However, I would prefer to use a sina plot instead of a beeswarm, as it has 2 advantages:
1- apart from kernel density function estimation, it does not produce an artificial structuring on the data (ie, the "branch-like" lines in the beeswarm),
2- each class's sina plot's width is normalized across all classes, so that we can get an impression of the difference in sample size at a glance.
I think the last point in particular can very well complement the ideas put forward by the DABEST framework. There is a Python implementation of Sina plots in the plotnine package (geom_sina).
Also maybe it would be interesting, if possible at all, to generalize the possibility of using other kinds of plots, as I guess different users might have different preferences?
Hello,
I appreciate the approach taken by this framework a lot, and I would like to implement it in my publications. However, I would prefer to use a sina plot instead of a beeswarm, as it has 2 advantages:
1- apart from kernel density function estimation, it does not produce an artificial structuring on the data (ie, the "branch-like" lines in the beeswarm),
2- each class's sina plot's width is normalized across all classes, so that we can get an impression of the difference in sample size at a glance.
I think the last point in particular can very well complement the ideas put forward by the DABEST framework. There is a Python implementation of Sina plots in the plotnine package (geom_sina).
Also maybe it would be interesting, if possible at all, to generalize the possibility of using other kinds of plots, as I guess different users might have different preferences?