Spatially Resolved Transcriptomics Data Clustering with Tailored Spatial-scale Modulation
Authors: Yuang Xiao, Yanran Zhu, Chang Tang, Xiao Zheng, Yuanyuan Liu, Kun Sun, Xinwang Liu
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate that our approach surpasses existing state-of-the-art methods in clustering tasks and related downstream applications. Section 4 is dedicated to "Experiments" and includes "Datasets," "Comparative Methods," "Training Details and Metrics," "Clustering Results and Analysis," "Ablation Studies," "Parameter Sensitivity Analysis," and "Downstream Applications." |
| Researcher Affiliation | Academia | 1School of Computer Science, China University of Geosciences, Wuhan, China 2School of Software Engineering, Huazhong University of Science and Technology, China 3 School of Computer Science, Hubei University of Technology, China 4 College of Computer Science and Technology, National University of Defense Technology, China |
| Pseudocode | No | The paper describes the methodology using text and mathematical equations in Section 3, but it does not include any clearly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluate our method on three widely used spatial transcriptomics datasets. The first dataset is the LIBD human dorsolateral prefrontal cortex DLPFC [Maynard et al., 2021]... The second dataset is the 10x Visium dataset of human breast cancer [Maynard et al., 2021]... The third dataset is mouse brain anterior tissue [Buache et al., 2011]... |
| Dataset Splits | No | The paper describes the datasets and preprocessing steps, such as filtering genes and normalization. However, it does not provide specific details on how the datasets were split into training, validation, or test sets (e.g., percentages, sample counts, or explicit standard splits). |
| Hardware Specification | Yes | We conduct experiments to evaluate the proposed TSstc on the PyTorch platform using a single NVIDIA GeForce RTX 3090. |
| Software Dependencies | No | The paper mentions using the PyTorch platform for experiments and the SCANPY [Wolf et al., 2018] toolkit for preprocessing. However, specific version numbers for these software dependencies are not provided. |
| Experiment Setup | Yes | First, we analyze the spatial distribution of spots and incrementally construct spatial graphs as the Euclidean distance-based radius R increases. Next, we input these spatial graphs into the GCN and train TSstc on all benchmark datasets for at least 150 iterations until convergence. We apply a weight decay of 0.0005 and use the Adam optimizer with a learning rate of 1e-3 to optimize our model. The parameter α is set to 10. |