sccoda.util.result_classes.CAResult.summary_prepare¶
-
CAResult.
summary_prepare
(est_fdr=0.05, *args, **kwargs)¶ Generates summary dataframes for intercepts and slopes. This function builds on and supports all functionalities from
az.summary
.- Parameters
- Return type
- Returns
Intercept and effect DataFrames
intercept_df – pandas df – Summary of intercept parameters. Contains one row per cell type.
Columns: - Final Parameter: Final intercept model parameter - HDI X%: Upper and lower boundaries of confidence interval (width specified via hdi_prob=) - SD: Standard deviation of MCMC samples - Expected sample: Expected cell counts for a sample with no present covariates. See the tutorial for more explanation
effect_df – pandas df – Summary of effect (slope) parameters. Contains one row per covariate/cell type combination.
Columns: - Final Parameter: Final effect model parameter. If this parameter is 0, the effect is not significant, else it is. - HDI X%: Upper and lower boundaries of confidence interval (width specified via hdi_prob=) - SD: Standard deviation of MCMC samples - Expected sample: Expected cell counts for a sample with only the current covariate set to 1. See the tutorial for more explanation - log2-fold change: Log2-fold change between expected cell counts with no covariates and with only the current covariate - Inclusion probability: Share of MCMC samples, for which this effect was not set to 0 by the spike-and-slab prior.