Wenwen Min (闵文文)

Wenwen Min (闵文文)

医学人工智能

软件包列表

You can find all the software packages developed by our research group in My Github

scstGCN

Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location and histology using GCN

Ideal ST data should have single-cell resolution and cover the entire tissue surface,but generating such ST data with existing platforms remains challenging scstGCN is a GCN-based method that leverages a weakly supervised learning framework to integrate multimodal information and then infer super-resolution gene expression at single-cell level. It first extract high-resolution multimodal feature map, including histological feature map, positional feature map, and RGB feature map. and then use the GCN module to predict super-resolution gene expression from multimodal feature map by a weakly supervised framework. scstGCN can predict super-resolution gene expression accurately, aid researchers in discovering biologically meaningful differentially expressed genes and pathways. Additionally, it can predict expression both outside the spots and in external tissue sections.

Image description
Overview of scstGCN. First, the histological image is divided into sub-images of size 224 to fit the input of the ViT module, which can extract histological feature maps by converting image data into sequence data. Second, the RGB feature map can be obtained from the histological image through downsampling and then the location feature map is calculated based on the two-dimensional spatial coordinates of the superpixels. These features are stacked to obtain the multimodal feature map. Next, The GCN module is utilized to further capture the complex relationships between adjacent cells. Finally, based on a weakly supervised GCN framework, the original gene expression data is employed as pseudo-labels to predict super-resolution gene expression.

Contact details

If you have any questions, please contact wenwen.min@foxmail.com

Citing

The corresponding BiBTeX citation are given below:

@article{xue2024inferring,
  title={Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location and histology using GCN},
  author={Xue, Shuailin and Zhu, Fangfang and Chen, Jinyu and Min, Wenwen},
  journal={Briefings in Bioinformatics},
  volume={DOI:10.1093/bib/bbae630},
  year={2024},
  publisher={Oxford University Press}
}

mclSTExp

Multimodal contrastive learning for spatial gene expression prediction using histology images

In this study, we propose mclSTExp: a multimodal deep learning approach utilizing Transformer and contrastive learning architecture. Inspired by the field of natural language processing, we regard the spots detected by ST technology as ‘‘words’’ and the sequences of these spots as ‘‘sentences’’ containing multiple ‘‘words’’. We employ a self-attention mechanism to extract features from these ‘‘words’’ and combine them with learnable position encoding to seamlessly integrate the positional information of these ‘‘words’’. Subsequently, we employ a contrastive learning framework to fuse the combined features with image features. we employed two human breast cancer datasets and one human cutaneous squamous cell carcinoma (cSCC) dataset.

Image description
Overview of mclSTExp. Step 1: mclSTExp seamlessly integrates spot features with their positional information using the self-attention mechanism of Transformer. Subsequently, it fuses H\&E image information through contrastive learning, thus learning a multi-modal embedding space enriched with diverse features. Step 2: Projected image patches into the learned multimodal embedding space to query the expressions of the nearest k spotsl; inferred the gene expression of the test image by weighted aggregation of these queried spot expressions.

Contact details

If you have any questions, please contact wenwen.min@foxmail.com

Citing

The corresponding BiBTeX citation are given below:

@article{min2024multimodal,
  title={Multimodal contrastive learning for spatial gene expression prediction using histology images},
  author={Min, Wenwen and Shi, Zhiceng and Zhang, Jun and Wan, Jun and Wang, Changmiao},
  journal={Briefings in Bioinformatics},
  volume = {25},
  number = {6},
  pages = {bbae551},
  year={2024}
}

SpaDiT

SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq

A novel deep learning method that uses a diffusion generative model to integrate scRNA-seq data and ST data for the prediction of undetected genes.

Image description
Overview of SpaDiT. There are three parts in total: latent embedding, conditional embedding and network backbone. (A) is the training process where each gene is considered as a sample, and (B) is the inference process.

Contact details

If you have any questions, please contact wenwen.min@foxmail.com

Citing

The corresponding BiBTeX citation are given below:

@article{spadit,
  title={SpaDiT: diffusion transformer for spatial gene expression prediction using scRNA-seq},
  author={Li, Xiaoyu and Zhu, Fangfang and Min, Wenwen},
  journal={Briefings in Bioinformatics},
  volume={25},
  number={6},
  pages={bbae571},
  year={2024},
  publisher={Oxford University Press}
}

SpaMask

SpaMask: Dual Masking Graph Autoencoder with Contrastive Learning for Spatial Transcriptomics

Understanding the spatial locations of cell within tissues is crucial for unraveling the organization of cellular diversity. Recent advancements in spatial resolved transcriptomics (SRT) have enabled the analysis of gene expression while preserving the spatial context within tissues. Spatial domain characterization is a critical first step in SRT data analysis, providing the foundation for subsequent analyses and insights into biological implications. Graph neural networks (GNNs) have emerged as a common tool for addressing this challenge due to the structural nature of SRT data. However, current graph-based deep learning approaches often overlook the instability caused by the high sparsity of SRT data. Masking mechanisms, as an effective self-supervised learning strategy, can enhance the robustness of these models. To this end, we propose SpaMask, dual masking graph autoencoder with contrastive learning for SRT analysis. Unlike previous GNNs, SpaMask masks a portion of spot nodes and spot-to-spot edges to enhance its performance and robustness. SpaMask combines Masked Graph Autoencoders (MGAE) and Masked Graph Contrastive Learning (MGCL) modules, with MGAE using node masking to leverage spatial neighbors for improved clustering accuracy, while MGCL applies edge masking to create a contrastive loss framework that tightens embeddings of adjacent nodes based on spatial proximity and feature similarity. We conducted a comprehensive evaluation of SpaMask on eight datasets from five different platforms. Compared to existing methods, SpaMask achieves superior clustering accuracy and effective batch correction.

Image description
Overview of SpaMask. (A) SpaMask employs two distinct masking techniques to handle the gene expression matrix and spatial topology structure separately. (B) SpaMask integrates Masked Graph Autoencoders (MGAE) and Masked Graph Contrastive Learning (MGCL) modules. MGAE employs node masking to infer missing features based on spatial neighbor information. MGCL applies edge masking to create a contrastive loss framework that tightens embeddings of adjacent nodes based on spatial proximity and feature similarity. (C) The learned latent representations are applied to spatial clustering, trajectory inference, gene expression imputation, and other downstream analytical tasks.

Contact details

If you have any questions, please contact wenwen.min@foxmail.com

Citing

SpaDAMA

SpaDAMA: Spatial Transcriptomics Deconvolution Using Domain-Adversarial Masked Autoencoder

In this study, we introduce a Domain-Adversarial Masked Autoencoder (SpaDAMA) for cell type deconvolution in spatial transcriptomics data. SpaDAMA leverages Domain-Adversarial learning to align real ST data with simulated ST data generated from scRNA-seq data. By projecting both datasets into a shared latent space, SpaDAMA effectively minimizes the data modality gap. Furthermore, SpaDAMA incorporates masking techniques to enhance the model’s ability to learn robust features from real ST data, while mitigating noise and spatial confounding factors.

Image description
Overview of SpaDAMA.

Contact details

If you have any questions, please contact wenwen.min@foxmail.com

Citing

SpaDAMA: Spatial Transcriptomics Deconvolution Using Domain-Adversarial Masked Autoencoder

stMCDI

Masked Conditional Diffusion Model with GNN for Spatial Transcriptomics Data Imputation

Spatially resolved transcriptomics represents a significant advancement in single-cell analysis by offering both gene expression data and their corresponding physical locations. However, this high degree of spatial resolution entails a drawback, as the resulting spatial transcriptomic data at the cellular level is notably plagued by a high incidence of missing values. Furthermore, most existing imputation methods either overlook the spatial information between spots or compromise the overall gene expression data distribution. To address these challenges, our primary focus is on effectively utilizing the spatial location information within spatial transcriptomic data to impute missing values, while preserving the overall data distribution. We introduce stMCDI, a masked conditional diffusion model for spatial transcriptomics data imputation, which employs a denoising network trained using randomly masked data portions as guidance, with the unmasked data serving as conditions. Additionally, it utilizes a GNN encoder to integrate the spatial position information, thereby enhancing model performance. Compared with baseline methods, our model achieves state-of-the-art performance in all evaluation metrics on six real-world datasets. The results obtained from spatial transcriptomics datasets elucidate the performance of our methods relative to existing approaches.

Image description
Overview of stMCDI.

Citing

MAVAE

Multimodal attention-based variational autoencoder for clinical risk prediction

Prediction of survival risk in cancer patients is crucial for understanding the underlying mechanisms of canceration in different stages. Previous studies mainly relied on single-modal omics data due to technological constraints. However, with the increasing availability of cancer omics data, researchers have focused on the use of multi-omics and multimodal data for survival analysis. The application of deep learning methods has become an option for the prediction of clinical risk. Recent advances in the attention mechanism and the variational autoencoder (VAE) have made them promising for analyzing cancer omics data. However, VAE has limitations in disregarding the importance of different features between modalities, and the introduction of an attention mechanism could address this limitation. In this study, we propose a Multimodal Attention-based VAE (MAVAE) deep learning framework using cross-modal multihead attention to integrate cancer multi-omics data for clinical risk prediction. We evaluated our approach on eight TCGA datasets. We find that (1) MAVAE outperforms traditional machine learning and recent deep learning methods; (2) Multi-modal data yields better classification performance than single-modal data; (3) The multi-head attention mechanism improves the decision-making process; (4) Clinical and genetic data are the most important modal data.

Image description
Overview of MAVAE.

Citing

The corresponding BiBTeX citation are given below:

@inproceedings{li2023multimodal,
  title={Multimodal attention-based variational autoencoder for clinical risk prediction},
  author={Li, Xiaoyu and Xu, Taosheng and Chen, Jinyu and Wan, Jun and Min, Wenwen},
  booktitle={2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
  pages={1260--1265},
  year={2023},
  organization={IEEE}
}

TransVCOX

TransVCOX: Bridging Transformer Encoder and Pre-trained VAE for Robust Cancer Multi-Omics Survival Analysis

Traditional survival analysis models, such as the COX proportional hazards model, face challenges in processing multimodal data, identifying nonlinear relationships, and recognizing complex data patterns. The rise of deep learning, particularly Transformers and variational autoencoders (VAEs), has showcased its potential in analyzing cancer multi-omics data comprehensively. However, many individual cancer datasets suffer from limited sample sizes, preventing some deep learning models from extracting in-depth data representations and resulting in subpar performance. To address this issue, we advocate the adoption of pre-training and fine-tuning techniques, which effectively mitigate performance deficits due to sparse cancer data samples. We introduce TransVCOX, a deep survival analysis model integrating a Transformer encoder with VAE. This model leverages pre-training and fine-tuning approaches to predict patients’ survival risk using cancer multi-omics data. Rigorous tests on eight unique cancer datasets from TCGA revealed: (1) TransVCOX outperforms other deep learning and conventional COX models. (2) Pre-training significantly reduces model overfitting and enhances performance. (3) VAE encoding, compared to positional encoding, offers a richer decision-making foundation. (4) The performance boost doesn’t linearly correlate with the addition of Transformer blocks. These findings underline TransVCOX’s promising capability for predicting cancer patients’ survival risks using multi-omics data.

Image description
Overview of MAVAE.

Citing

The corresponding BiBTeX citation are given below:

@inproceedings{li2023transvcox,
  title={TransVCOX: Bridging Transformer Encoder and Pre-trained VAE for Robust Cancer Multi-Omics Survival Analysis},
  author={Li, Xiaoyu and Min, Wenwen and Chen, Jinyu and Wu, Jiaxin and Wang, Shunfang},
  booktitle={2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
  pages={1254--1259},
  year={2023},
  organization={IEEE}
}