SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics
Authors: Qingtian Zhu, Yumin Zheng, Yuling Sang, Yifan Zhan, Ziyan Zhu, Jun Ding, Yinqiang Zheng
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By extensive experiments of a wide range of common ST platforms under varying degradations, SUICA outperforms both conventional INR variants and SOTA methods regarding numerical fidelity, statistical correlation, and bio-conservation. |
| Researcher Affiliation | Academia | 1The University of Tokyo 2Mc Gill University 3MUHC Research Institute 4Duke-NUS Medical School 5Carnegie Mellon University 6Mila Quebec AI Institute. Correspondence to: Jun Ding <EMAIL>, Yinqiang Zheng <EMAIL>. |
| Pseudocode | No | The paper describes the overall pipeline in Figure 2 and in the '3.2. Method' section, but does not provide any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Szym29/SUICA. |
| Open Datasets | Yes | For a quantitative benchmarking, we involve a nanoscale resolution Stereo-seq (Spa Tial Enhanced REsolution Omicssequencing) dataset, MOSTA (Chen et al., 2022a). MOSTA consists of a total of 53 sagittal sections from C57BL/6 mouse embryos at 8 progressive stages using Stereo-seq, from which we take 1 slice for each stage (from E9.5 to E16.5) for benchmarking. In addition to Stereo-seq, we also leverage ST data by other common platforms, i.e., Slide-seq V2, 10x Genomics Visium (see Appendix) and MERFISH (see Appendix), to further demonstrate the generalization of SUICA. |
| Dataset Splits | Yes | for spatial imputation, we randomly sample 80% of the spots for training, leaving the rest 20% for evaluation; for gene imputation, we randomly mute 70% of the elements in the data matrices; for denoising, a standard Gaussian noise is injected to the raw data. |
| Hardware Specification | Yes | All experiments can be conducted on 1 NVIDIA RTX 4090. |
| Software Dependencies | No | We implement SUICA with Py Torch. For the implementation, we re-compile the library of tiny-cuda-nn to support float32, to align with other methods. However, no specific version numbers are provided for PyTorch or tiny-cuda-nn. |
| Experiment Setup | Yes | During the pre-training phase of GAE, we use Adam with a learning rate of 1e 5 for 200 epochs. After obtaining low-dimensional cell embeddings, we train the INR with Adam at a learning rate of 1e 4 for 1k epochs to learn the embedding mapping. Subsequently, the INR is frozen, with the pre-trained decoder trained for an additional 1k epochs using Adam with the same learning rate. To construct the k NN graph for GCN, we set k = 5, including the given cell itself. |