tasccoda.tree_results.CAResult_tree.summary_prepare

CAResult_tree.summary_prepare(*args, **kwargs)

Generates summary dataframes for intercepts and slopes. This function builds on and supports all functionalities from az.summary.

Parameters
args

Passed to az.summary

kwargs

Passed to az.summary

Return type

Tuple[DataFrame, DataFrame, DataFrame]Tuple[DataFrame, DataFrame, DataFrame]

Returns

  • Intercept, effect and node 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 on the data features (cell types or OTUs). Contains one row per covariate/cell type combination.

    Columns: - Effect: Final effect model parameter. If this parameter is 0, the effect is not significant, else it is. - Median: Median of parameter over MCMC chain - 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.

  • node_df – pandas df – Summary of effect (slope) parameters on the tree nodes (features or groups of features). 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. - Median: Median of parameter over MCMC chain - HDI X%: Upper and lower boundaries of confidence interval (width specified via hdi_prob=) - SD: Standard deviation of MCMC samples - Delta: Decision boundary value - threshold of practical significance - Is credible: Boolean indicator whether effect is credible