shobhitagrawal1 / Portal

Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets

Portal

Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets

An efficient, accurate and flexible method for single-cell data integration.

Check out our manuscript in Nature Computational Science:

• Nature Computational Science website
• Read fulltext link
• Preprint in bioRxiv

Reproducibility

We provide source codes for reproducing the experiments of the paper "Adversarial domain translation networks for fast and accurate integration of large-scale atlas-level single-cell datasets".

• Integration of mouse spleen datasets (we reproduce the result of performance metrics in this notebook as an example). Benchmarking.
• Integration of mouse marrow datasets.
• Integration of mouse bladder datasets.
• Integration of mouse brain cerebellum datasets.
• Integration of mouse brain hippocampus datasets.
• Integration of mouse brain thalamus datasets.
• Integration of human PBMC datasets (sensitivity analysis).
• Integration of entire mouse cell atlases from the Tablula Muris project.
• Integration of mouse brain scRNA-seq and snRNA-seq datasets.
• Integration of human PBMC scRNA-seq and human brain snRNA-seq datasets.
• Integration of scRNA-seq and scATAC-seq datasets.
• Integration of developmental trajectories.
• Integration of spermatogenesis differentiation process across multiple species. Gene lists from Ensembl Biomart (we only use genes that are assigned with the type "ortholog_one2one" in the lists): orthologues (human vs mouse), orthologues (human vs macaque).

Installation

To run Portal, please follow the installation instruction:

git clone https://github.com/YangLabHKUST/Portal.git
cd Portal
conda env update --f environment.yml
conda activate portal

Normally the installation time is less than 5 minutes.

Quick Start

Basic Usage

Starting with raw count matrices formatted as AnnData objects, Portal uses a standard pipline adopted by Seurat and Scanpy to preprocess data, followed by PCA for dimensionality reduction. After preprocessing, Portal can be trained via model.train().

import portal
import scanpy as sc

# read AnnData
adata_1 = sc.read_h5ad("adata_1.h5ad")
adata_2 = sc.read_h5ad("adata_2.h5ad")

model = portal.model.Model()
model.preprocess(adata_1, adata_2) # perform preprocess and PCA
model.train() # train the model
model.eval() # get integrated latent representation of cells

The evaluating procedure model.eval() saves the integrated latent representation of cells in model.latent, which can be used for downstream integrative analysis.

Parameters in portal.model.Model():

• lambdacos: Coefficient of the regularizer for preserving cosine similarity across domains. Default: 20.0.
• training_steps: Number of steps for training. Default: 2000. Use training_steps=1000 for datasets with sample size < 20,000.
• npcs: Dimensionality of the embeddings in each domain (number of PCs). Default: 30.
• n_latent: Dimensionality of the shared latent space. Default: 20.
• batch_size: Batch size for training. Default: 500.
• seed: Random seed. Default: 1234.

The default setting of the parameter lambdacos works in general. We also enable tuning of this parameter to achieve a better performance, see Tuning lambdacos (optional). For the integration task where the cosine similarity is not a reliable cross-domain correspondance (such as cross-species integration), we recommend to use a lower value such as lambdacos=10.0.

Memory-efficient Version

To deal with large single-cell datasets, we also developed a memory-efficient version by reading mini-batches from the disk:

model = portal.model.Model()
model.preprocess_memory_efficient(adata_A_path="adata_1.h5ad", adata_B_path="adata_2.h5ad")
model.train_memory_efficient()
model.eval_memory_efficient()

Integrating Multiple Datasets

Portal integrates multiple datasets incrementally. Given adata_list = [adata_1, ..., adata_n] is a list of AnnData objects, they can be integrated by running the following commands:

lowdim_list = portal.utils.preprocess_datasets(adata_list)
integrated_data = portal.utils.integrate_datasets(lowdim_list)

Tuning lambdacos (optional)

An optional choice is to tune the parameter lambdacos in the range [15.0, 50.0]. Users can run the following command to search for an optimal parameter that yields the best integration result in terms of the mixing metric:

lowdim_list = portal.utils.preprocess_datasets(adata_list)
integrated_data = portal.utils.integrate_datasets(lowdim_list, search_cos=True)

Recovering expression matrices

Portal can provide harmonized expression matrices (in scaled level or log-normalized level):

lowdim_list, hvg, mean, std, pca = portal.utils.preprocess_recover_expression(adata_list)
expression_scaled, expression_log_normalized = portal.utils.integrate_recover_expression(lowdim_list, mean, std, pca)

Demos

We provide demos for users to get a quick start: Demo 1, Demo 2.

Development

This package is developed by Jia Zhao ([e-mail]) and Gefei Wang ([e-mail]).

Citation

Jia Zhao, Gefei Wang, Jingsi Ming, Zhixiang Lin, Yang Wang, The Tabula Microcebus Consortium, Angela Ruohao Wu, Can Yang. Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets. Nature Computational Science 2, 317–330 (2022).