scNoah

Benchmarking cell type annotation and modality prediction.

Balance dataset

Balancing dataset using your own annotation for future model training. Oversmaple or undersample some cell types.

scnoah.balance	Balance cell types in AnnData object.
scnoah.oversample	Oversample some cell types in AnnData object.
scnoah.undersample	Undersample some cell types in AnnData object.

Annotation metrics

Test annotation method quality using confusion matrix, accuracy, balanced accuracy and calculating cell type specific precision, recall (also called sensitivity), specificity, f1-score, geometric mean, and index balanced accuracy of the geometric mean.

scnoah.report_classif_full	Returns metrics (precision, recall (also called sensitivity), specificity, f1-score, geometric mean, and index balanced accuracy of the geometric mean) of predicted cell types.
scnoah.report_classif_sens_spec	Returns specificity and recall (also called sensitivity) metrics of predicted cell types.
scnoah.conf_matrix	Compute confusion matrix to evaluate the accuracy of a classification.
scnoah.pred_status	Find correct and incorrect predictions.

Regression metrics

Test modality prediction method quality using error metrics (RMSE, MedianAE, MeanAE), EVS, R² score and PC. Also, visualise metrics on cell embeddings.

RMSE - Root mean squared error

MeanAE - Mean absolute error

MedianAE - Median absolute error

EVS - Explained variance score

R² score - Coefficient of determination

PC - Pearson coefficient

For error metrics (RMSE, MedianAE, MeanAE): lower value - better prediction

scnoah.report_reg	Returns multiple metrics of cell surface proteins prediction.
scnoah.regres_status	Compute regression status of cells to visualize on UMAP.
scnoah.pearson_coef_prot	Compute Pearson correlation coefficient of predicted protein.

Count cells

Count number of cell types per sample or condition.

scnoah.cell_counter

Count cell types in samples.

Explanations

Get explanations of gene importances in scAdam model prediction.

scnoah.explain	Identify the genes that are most important for determining cell type using a model.
scnoah.feature_importance	Get dataframe with gene importances for specific cell type.

Managing large datasets

Get fraction of large dataset.

scnoah.get_frac	Get fraction of anndata object.
scnoah.get_samples	Get samples from AnnData object.

Difference between clusters

Calculate Integral of absolute density difference and Mutual Information between two clusterings.

scnoah.clust_diff

Calculates metrics to Integral of absolute density difference and Mutual Information between two clusterings.