flotilla.external.combat module¶

flotilla.external.combat.adjust_nums(numerical_covariates, drop_idxs)[source]¶

flotilla.external.combat.aprior(gamma_hat)[source]¶

flotilla.external.combat.bprior(gamma_hat)[source]¶

flotilla.external.combat.combat(data, batch, model=None, numerical_covariates=None)[source]¶

Correct for batch effects in a dataset

Parameters:

data : pandas.DataFrame A (n_features, n_samples) dataframe of the expression or methylation data to batch correct batch : List-like A column corresponding to the batches in the data, in the same order as the samples in data model : patsy.design_info.DesignMatrix, optional A model matrix describing metadata on the samples which could be causing batch effects. If not provided, then will attempt to coarsely correct just from the information provided in batch numerical_covariates : list-like List of covariates in the model which are numerical, rather than categorical

Returns:

corrected : pandas.DataFrame A (n_features, n_samples) dataframe of the batch-corrected data

flotilla.external.combat.design_mat(mod, numerical_covariates, batch_levels)[source]¶

flotilla.external.combat.it_sol(sdat, g_hat, d_hat, g_bar, t2, a, b, conv=0.0001)[source]¶

flotilla.external.combat.postmean(g_hat, g_bar, n, d_star, t2)[source]¶

flotilla.external.combat.postvar(sum2, n, a, b)[source]¶