Getting started with CONCORD 
Description
Revealing the underlying cell-state landscape from single-cell data requires overcoming the critical obstacles of batch integration, denoising, and dimensionality reduction. We present CONCORD, a unified framework that simultaneously addresses these challenges within a single self-supervised model. At its core, CONCORD implements a unified probabilistic sampling strategy that corrects batch effects via dataset-aware sampling and enhances biological resolution through hard-negative sampling. Remarkably, using only a minimalist neural network with a single hidden layer and contrastive learning, CONCORD surpasses state-of-the-art performance without relying on deep architectures, auxiliary losses, or external supervision. It seamlessly integrates data across batches, technologies, and even species to generate high-resolution cell atlases. The resulting latent representations are denoised and biologically meaningful—capturing gene co-expression programs, revealing detailed lineage trajectories, and preserving both local geometric relationships and global topological structures. We demonstrate CONCORD’s broad applicability across diverse datasets, establishing it as a general-purpose framework for learning unified, high-fidelity representations of cellular identity and dynamics.
Installation
It is recommended to use conda to create and set up a clean virtual environment for CONCORD.
1. Install PyTorch
You must install the correct version of PyTorch based on your system's CUDA setup. Follow the instructions on the official PyTorch website.
- For CPU:
- For CUDA (adjust based on your GPU version):
2. Install CONCORD (Stable or Development)
Stable Version (PyPI)
Development Version (GitHub)
Optional Installations
(Recommended) Enable Additional Functionalities
For GO enrichment, benchmarking, and R integration, install:
(Optional) Install FAISS for Accelerated kNN mode
Note: If using Mac, you may need to disable FAISS when running Concord:
- FAISS with GPU:
- FAISS with CPU:
(Optional) Integration with VisCello
CONCORD integrates with the R package VisCello, a tool for interactive visualization.
To explore results interactively, visit VisCello GitHub for more details.
Getting Started
Concord integrates seamlessly with anndata objects.
Single-cell datasets, such as 10x Genomics outputs, can easily be loaded into an annData object using the Scanpy package. If you're using R and have data in a Seurat object, you can convert it to anndata format by following this tutorial.
In this quick-start example, we'll demonstrate CONCORD using the pbmc3k dataset provided by the scanpy package.
Load package and data
# Load required packages
import concord as ccd
import scanpy as sc
import torch
# Load and prepare example data
adata = sc.datasets.pbmc3k_processed()
adata = adata.raw.to_adata() # Assume starting from raw counts
# (Optional) Select top variably expressed/accessible features for analysis (other methods besides seurat_v3 available)
feature_list = ccd.ul.select_features(adata, n_top_features=2000, flavor='seurat_v3')
sc.pp.normalize_total(adata) # Normalize counts per cell
sc.pp.log1p(adata) # Log-transform data
Run CONCORD:
# Set device to cpu or to gpu (if your torch has been set up correctly to use GPU), for mac you can use either torch.device('mps') or torch.device('cpu')
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Initialize Concord with an AnnData object, skip input_feature to use all features, set preload_dense=False if your data is very large
cur_ccd = ccd.Concord(adata=adata, input_feature=feature_list, device=device, preload_dense=True)
# If integrate across batches, provide domain_key (a column in adata.obs that contains batch label):
# cur_ccd = ccd.Concord(adata=adata, input_feature=feature_list, domain_key='batch', device=device, preload_dense=True)
# Encode data, saving the latent embedding in adata.obsm['Concord']
cur_ccd.fit_transform(output_key='Concord')
Visualization:
CONCORD latent embeddings can be directly used for downstream analyses such as visualization with UMAP and t-SNE or constructing k-nearest neighbor (kNN) graphs. Unlike PCA, it is important to utilize the full CONCORD latent embedding in downstream analyses, as each dimension is designed to capture meaningful and complementary aspects of the underlying data structure.
ccd.ul.run_umap(adata, source_key='Concord', result_key='Concord_UMAP', n_components=2, n_neighbors=30, min_dist=0.1, metric='euclidean')
# Plot the UMAP embeddings
color_by = ['n_genes', 'louvain'] # Choose which variables you want to visualize
ccd.pl.plot_embedding(
adata, basis='Concord_UMAP', color_by=color_by, figsize=(10, 5), dpi=600, ncols=2, font_size=6, point_size=10, legend_loc='on data',
save_path='Concord_UMAP.png'
)
The latent space produced by CONCORD often capture complex biological structures that may not be fully visualized in 2D projections. We recommend exploring the latent space using a 3D UMAP to more effectively capture and examine the intricacies of the data. For example:
ccd.ul.run_umap(adata, source_key='Concord', result_key='Concord_UMAP_3D', n_components=3, n_neighbors=30, min_dist=0.1, metric='euclidean')
# Plot the 3D UMAP embeddings
import plotly.io as pio
pio.renderers.default = 'notebook'
col = 'louvain'
fig = ccd.pl.plot_embedding_3d(
adata, basis='Concord_UMAP_3D', color_by=col,
save_path='Concord_UMAP_3D.html',
point_size=3, opacity=0.8, width=1500, height=1000
)
License
This project is licensed under the MIT License.
See the LICENSE file for details.
Citation
If you use CONCORD in your research, please cite the following preprint:
"Revealing a coherent cell state landscape across single-cell datasets with CONCORD"
bioRxiv, 2025