spaVAE

SpaVAE, spaPeakVAE, spaMultiVAE, and spaLDVAE are dependency-aware deep generative models for multitasking analysis of spatial genomics data. Different models are designed for different analytical tasks of spatial genomics data.
spaVAE is a Gaussian process (GP) variational autoencoder (VAE) with a negative binomial (NB) model-based decoder. The model is for multitasking analysis of spatially resolved transcriptomics (SRT) data, including dimensionality reduction, visualization, clustering, batch integration, denoising, differential expression, spatial imputation, and resolution enhancement.
spaPeakVAE is a variant model of spaVAE, which uses a Bernoulli decoder to characterize spatial ATAC-seq binary data. The analytical tasks in spaVAE can also be fulfilled by spaPeakVAE for spatial ATAC-seq data.
spaMultiVAE characterizes spatial multi-omics data, which profiles gene expression and surface protein intensity simultaneously. Besides the analyses aforementioned, spaMultiVAE uses a NB mixture decoder to denoise backgrounds in proteins.
spaLDVAE is spaVAE with a linear decoder, which contains two latent embedding components, one follows GP prior and the other follows standard normal prior. The model can be used for detecting spatial variable genes and peaks.

Table of contents

• Network diagram
• Requirements
• Usage
• Parameters
• Datasets
• Reference
• Contact

Network diagram

Diagram of spaVAE (a), spaPeakVAE (a), spaMultiVAE (b), and spaLDVAE (c) networks:

Requirements

Python: 3.9.7
PyTorch: 1.11.0 (https://pytorch.org)
Scanpy: 1.9.1 (https://scanpy.readthedocs.io/en/stable)
Numpy: 1.21.5 (https://numpy.org)
Scipy: 1.8.0 (https://scipy.org)
Pandas: 1.4.2 (https://pandas.pydata.org)
h5py: 3.6.0 (https://pypi.org/project/h5py)

Usage

For human DLPFC dataset:

python run_spaVAE.py --data_file HumanDLPFC_151673.h5 --noise 1 --inducing_point_steps 6

For integrating 4 human DLPFC samples:

python run_spaVAE_Batch.py --data_file 151673_151674_151675151676_samples_union.h5 --noise 1 --inducing_point_steps 6

For mouse hippocampus Slide-seq V2 dataset:

python run_spaVAE.py --data_file Mouse_hippocampus.h5 --grid_inducing_points False --inducing_point_nums 300

For spatial ATAC-seq dataset of mouse embryonic (E15.5) brain tissues in the MISAR-seq dataset:

python run_spaPeakVAE.py --data_file MISAR_seq_mouse_E15_brain_ATAC_data.h5 --inducing_point_steps 19

For spatial multi-omics DBiT-seq data:

python run_spaMultiVAE.py --data_file Multiomics_DBiT_seq_0713_data.sh --inducing_point_steps 15

--data_file specifies the data file name, in the h5 file. For SRT data, spot-by-gene count matrix is stored in "X" and 2D location is stored in "pos". For spatial ATAC-seq data, "X" represents spot-by-peak count matrix. For spatial multi-omics data, "X_gene" represents spot-by-gene count matrix, and "X_protein" represents spot-by-protein count matrix.

Parameters

--data_file: data file name.
--select_genes: number of selected genes for embedding analysis, default = 0 means no filtering.
--batch_size: mini-batch size, default = 512.
--maxiter: number of max training iterations, default = 2000.
--lr: learning rate, default = 1e-3.
--weight_decay: weight decay coefficient, default = 1e-2.
--noise: coefficient of random Gaussian noise for the encoder, default = 0.
--dropoutE: dropout probability for encoder, default = 0.
--dropoutD: dropout probability for decoder, default = 0.
--encoder_layers: hidden layer sizes of encoder, default = [128, 64, 32].
--z_dim: size of bottleneck layer, default = 2.
--decoder_layers: hidden layer sizes of decoder, default = [32].
--beta: coefficient of the reconstruction loss, default = 20.
--num_samples: number of samplings of the posterior distribution of latent embedding, default = 1.
--fix_inducing_points: fixed or trainable inducing points, default = True.
--grid_inducing_points: whether to use 2D grid inducing points or k-means centroids on positions, default = True.
--inducing_point_steps: if using 2D grid inducing points, set the number of 2D grid steps, default = None.
--inducing_point_nums: if using k-means centroids on positions, set the number of inducing points, default = None.
--fixed_gp_params: kernel scale is trainable or not, default = False.
--loc_range: positional locations will be scaled to the specified range. For example, loc_range = 20 means x and y locations will be scaled to the range 0 to 20, default = 20.
--kernel_scale: initial kernel scale, default = 20.
--model_file: file name to save weights of the model, default = model.pt
--final_latent_file: file name to output final latent representations, default = final_latent.txt.
--denoised_counts_file: file name to output denoised counts, default = denoised_mean.txt.
--device: pytorch device, default = cuda.

Datasets

Datasets used in the study can be found

https://figshare.com/articles/dataset/Spatial_genomics_datasets/21623148

Reference

Contact

Tian Tian [email protected]