API

Import symphonypy as:

import symphonypy as sp

Preprocessing

pp.harmony_integrate(adata, key[, flavor, ...])

Run Harmony batch correction on adata, save corrected output to adata.obsm, save all the necessary to Symphony mapping algorithm parameters to adata.uns

Tools

tl.map_embedding(adata_query, adata_ref[, ...])	Actually runs Symphony algorithm for mapping adata_query to adata_ref.
tl.transfer_labels_kNN(adata_query, ...[, ...])	Run sklearn kNN classifier for label transferring.
tl.per_cell_confidence(adata_query, adata_ref)	Calculates the weighted Mahalanobis distance for query cells to reference clusters.
tl.per_cluster_confidence(adata_query, ...)	Calculates the Mahalanobis distance from user-defined query clusters to their nearest reference centroid after initial projection into reference PCA space.
tl.ingest(adata_query, adata_ref[, obs, ...])	Copied from https://github.com/scverse/scanpy/blob/master/scanpy/tools/_ingest.py with little change that var_names equality between adata and adata_new wouldn't be check if needless, and additional parameter use_rep is added.
tl.tsne(adata[, use_rep, t_sne_slot, ...])	Run openTSNE dimension reduction on adata if use_model is None, or ingest adata.obsm[use_rep] to existing embedding, saved in use_model.