SCRaPL: A Bayesian hierarchical framework for detecting technical associates in single cell multiomics data
Fig 4
SCRaPL’s behavior compared to Pearson/Spearman correlation in micro and macro scale.
In all figures apart from 4D the scatter plot depicts raw data for chosen features color-coded by CpG coverage, and normalized expression plotted in the log(1 + x) scale. The violin plots depict the posterior correlation densities estimated by SCRaPL for the raw data in their left hand side. (4A) Example where both SCRaPL and Pearson/Spearman identify the feature’s association as significant. (4B) Example were only Pearson/Spearman identifies the feature’s association significant. (4C) Example were only SCRaPL identifies the feature’s association significant. (4D) Scatter plots to demonstrate the negative/positive relationship between alternative correlation estimates and CpG coverage/% zeros in expression respectively. ( and ρprs in Fig 4D are posterior mean and Pearson correlation for feature j.).