Garfield 1.0.0

Garfield: Graph-based Contrastive Learning enable Fast Single-Cell Embedding

Garfield: Graph-based Contrastive Learning enable Fast Single-Cell Embedding

News

February 7, 2025: We officially released v1.0 version of Garfield.

Installation

Please install Garfield from pypi with:

pip install Garfield

install from Github:

pip install git+https://github.com/zhou-1314/Garfield.git

or git clone and install:

git clone https://github.com/zhou-1314/Garfield.git
cd Garfield
python setup.py install

Garfield is implemented in Pytorch framework.

Documentation

Please refer to the documentation for more details.

Main Parameters of Garfield Model

Data Preprocessing Parameters

• adata_list: List of AnnData objects containing data from multiple batches or samples.
• profile: Specifies the data profile type (e.g., 'RNA', 'ATAC', 'ADT', 'multi-modal', 'spatial').
• data_type: Type of the multi-omics dataset (e.g., Paired, UnPaired) for preprocessing.
• sub_data_type: List of data types for multi-modal datasets (e.g., ['rna', 'atac'] or ['rna', 'adt']).
• sample_col: Column in the dataset that indicates batch or sample identifiers.
• weight: Weighting factor that determines the contribution of different modalities or types of graphs in multi-omics or spatial data.
  • For non-spatial single-cell multi-omics data (e.g., RNA + ATAC), weight specifies the contribution of the graph constructed from scRNA data. The remaining (1 - weight) represents the contribution from the other modality.
  • For spatial single-modality data, weight refers to the contribution of the graph constructed from the physical spatial information, while (1 - weight) reflects the contribution from the molecular graph (RNA graph).
• graph_const_method: Method for constructing the graph (e.g., 'mu_std', 'Radius', 'KNN', 'Squidpy' for spatial data; and 'Scanpy' for single-cell data).
• genome: Reference genome to use during preprocessing.
• use_gene_weight: Whether to apply gene weights in the preprocessing step.
• use_top_pcs: Whether to use the top principal components during dimensionality reduction.
• used_hvg: Whether to use highly variable genes (HVGs) for analysis.
• min_features: Minimum number of features required for a cell to be included in the dataset.
• min_cells: Minimum number of cells required for a feature to be retained in the dataset.
• keep_mt: Whether to retain mitochondrial genes in the analysis.
• target_sum: Target sum used for normalization (e.g., 1e4 for counts per cell).
• rna_n_top_features: Number of top features to retain for RNA datasets.
• atac_n_top_features: Number of top features to retain for ATAC datasets.
• n_components: Number of components to use for dimensionality reduction (e.g., PCA).
• n_neighbors: Number of neighbors to use in graph-based algorithms.
• metric: Distance metric used during graph construction.
• svd_solver: Solver for singular value decomposition (SVD).

Datasets

• used_pca_feat: Whether to use PCA or LSI features for the encoder.

• adj_key: Key in the AnnData object that holds the adjacency matrix.

Data Split Parameters

• edge_val_ratio: Ratio of edges to use for validation in edge-level tasks.
• edge_test_ratio: Ratio of edges to use for testing in edge-level tasks.
• node_val_ratio: Ratio of nodes to use for validation in node-level tasks.
• node_test_ratio: Ratio of nodes to use for testing in node-level tasks.

Model Architecture Parameters

• augment_type: Type of augmentation to use (e.g., 'dropout', 'svd').
• svd_q: Rank for the low-rank SVD approximation.
• use_FCencoder: Whether to use a fully connected encoder before the graph layers.
• hidden_dims: List of hidden layer dimensions for the encoder.
• bottle_neck_neurons: Number of neurons in the bottleneck (latent) layer.
• num_heads: Number of attention heads for each graph attention layer.
• dropout: Dropout rate applied during training.
• concat: Whether to concatenate attention heads or not.
• drop_feature_rate: Dropout rate applied to node features.
• drop_edge_rate: Dropout rate applied to edges during augmentation.
• used_edge_weight: Whether to use edge weights in the graph layers.
• used_DSBN: Whether to use domain-specific batch normalization.
• conv_type: Type of graph convolution to use ('GATv2Conv', 'GAT', 'GCN').
• gnn_layer: Number of times the graph neural network (GNN) encoder is repeated in the forward pass.
• cluster_num: Number of clusters for latent feature clustering.

Data Loader Parameters

• num_neighbors: Number of neighbors to sample for graph-based data loaders.
• loaders_n_hops: Number of hops for neighbors during graph construction.
• edge_batch_size: Batch size for edge-level tasks.
• node_batch_size: Batch size for node-level tasks.

Loss Function Parameters

• include_edge_recon_loss: Whether to include edge reconstruction loss in the training objective.
• include_gene_expr_recon_loss: Whether to include gene expression reconstruction loss in the training objective.
• used_mmd: Whether to use maximum mean discrepancy (MMD) for domain adaptation.
• lambda_latent_contrastive_instanceloss: Weight for the instance-level contrastive loss.
• lambda_latent_contrastive_clusterloss: Weight for the cluster-level contrastive loss.
• lambda_gene_expr_recon: Weight for the gene expression reconstruction loss.
• lambda_latent_adj_recon_loss: Weight for the adjacency reconstruction loss.
• lambda_edge_recon: Weight for the edge reconstruction loss.
• lambda_omics_recon_mmd_loss: Weight for the MMD loss in omics reconstruction tasks.

Training Parameters

• n_epochs: Number of training epochs.
• n_epochs_no_edge_recon: Number of epochs without edge reconstruction loss.
• learning_rate: Learning rate for the optimizer.
• weight_decay: Weight decay (L2 regularization) for the optimizer.
• gradient_clipping: Maximum norm for gradient clipping.

Other Parameters

• latent_key: Key for storing latent features in the AnnData object.

• reload_best_model: Whether to reload the best model after training.

• use_early_stopping: Whether to use early stopping during training.

• early_stopping_kwargs: Arguments for configuring early stopping (e.g., patience, delta).

• monitor: Whether to print training progress.

• device_id: Device ID for GPU training.

• seed: Random seed for reproducibility.

• verbose: Whether to display detailed logs during training.

Support

Please submit issues or reach out to [e-mail].

Acknowledgment

Garfield uses and/or references the following libraries and packages:

• NicheCompass

• scArches

• SIMBA

• MaxFuse

• Scanpy

Thanks for all their contributors and maintainers!

Citation

If you have used Garfiled for your work, please consider citing:

@misc{2024Garfield,
    title={Garfield: Graph-based Contrastive Learning enable Fast Single-Cell Embedding},
    author={Weige Zhou},
    howpublished = {\url{https://github.com/zhou-1314/Garfield}},
    year={2024}
}