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
est_fdr : floatfloat (default: 0.05)

Desired FDR value

args

Passed to az.summary

kwargs

Passed to az.summary

Return type

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

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.