symphonypy 0.2.1

Porting of Symphony R package to Python

Symphonypy

Porting of Symphony R package to Python

• Installation
• Examples
• Instructions
  • Step 1: Reference building
  • Step 2: Query preprocessing and Symphony
  • Step 3: Label transfer
  • Step 4 (optional): Dimensionality reduction
• Benchmarking

Installation

Symphonypy package might be installed via pip:

pip install symphonypy

Examples

Here are Jupyter-notebooks with simple examples of how to use symphonypy

• with pre-built references from original Symphony (nbviewer),
• with reference building from scratch (nbviewer),
• for mapping to reference without harmony step (nbviewer).

Instructions

Step 1: Reference building

import scanpy as sc
import symphonypy as sp

sc.pp.normalize_total(adata_ref, target_sum=1e5)
sc.pp.log1p(adata_ref)
sc.pp.highly_variable_genes(
    adata_ref,
    batch_key=batch_key_ref,
    n_top_genes=n_top_genes,
)
adata_ref.raw = adata_ref
adata_ref = adata_ref[:, adata_ref.var.highly_variable]
sc.pp.scale(adata_ref, max_value=10)
sc.pp.pca(adata_ref, n_comps=30, zero_center=False)

# You can skip Harmony if you have only one batch in reference
sp.pp.harmony_integrate(adata_ref, key=batch_key_ref)  
# -> adata_ref.obsm["X_pca_harmony"] <- Harmony adjusted "X_pca"
# -> adata_ref.uns["harmony"] <- Harmony object for Symphony

Step 2: Query preprocessing and Symphony

# target_sum for normalize_total() should be the same as in reference
sc.pp.normalize_total(adata_query, target_sum=1e5)
sc.pp.log1p(adata_query)

# Symphony
sp.tl.map_embedding(adata_query, adata_ref, key=batch_key_query)
# -> adata_query.obsm["X_pca_harmony"] <- Symphony adjusted query's PCA
sp.tl.per_cell_confidence(adata_query, adata_ref)
# -> adata_query.obs["symphony_per_cell_dist"] <- Symphony mapping score per cell
sp.tl.per_cluster_confidence(adata_query, adata_ref, query_clusters)
# -> adata_query.uns["symphony_per_cluster_dist"] <- Symphony mapping score per cluster

Step 3: Label transfer

sp.tl.transfer_labels_kNN(adata_query, adata_ref, labels)
# -> adata_query.obs[labels] <- transferred labels (via sklearn kNN)

Step 4 (optional): Dimensionality reduction

With UMAP:

sc.pp.neighbors(adata_ref, use_rep="X_pca_harmony")
sc.tl.umap(adata_ref)
sp.tl.ingest(adata_query, adata_ref)
# -> adata_query.obsm["X_umap"] <- mapped to the reference's UMAP coords

With t-SNE (openTSNE should be installed, pip install openTSNE):

tSNE_model = sp.tl.tsne(adata_ref, use_rep="X_pca_harmony", return_model=True)
sp.tl.tsne(adata_query, use_rep="X_pca_harmony", use_model=tSNE_model)
# -> adata_query.obsm["X_tsne"] <- mapped to the reference's tSNE coords

Benchmarking

• Harmony (R) vs harmonypy benchmarking: benchmarking/Benchmarking_harmony_PBMC_Satija.ipynb
• Symphony (R) vs symphonypy benchmarking: benchmarking/Benchmarking_symphony_PBMC.ipynb