xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
Authors: Jing Gong, Minsheng Hao, Xingyi Cheng, Xin Zeng, Chiming Liu, Jianzhu Ma, Xuegong Zhang, Taifeng Wang, Le Song
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments also show that the performance of x Trimo Gene improves as we scale up the model sizes, and it also leads to SOTA performance over various downstream tasks, such as cell type annotation, perturb-seq effect prediction, and drug combination prediction. |
| Researcher Affiliation | Collaboration | Bio Map Research 2 Tsinghua University 3 Mohamed bin Zayed University of Artificial Intelligence |
| Pseudocode | No | The paper includes Figure 1 which illustrates the x Trimo Gene Framework, but this is a conceptual diagram and not structured pseudocode or an algorithm block. |
| Open Source Code | No | x Trimo Gene model is now available for use as a service via the following link: https://api.biomap.com/xTrimoGene/apply. This indicates the model is available as an API service, but does not provide access to its source code. |
| Open Datasets | Yes | We evaluated x Trimo Gene s performance on cell type annotation task with Zheng68K [39] and Segerstolpe [31] dataset, which has been widely benchmarked. |
| Dataset Splits | No | The paper mentions using benchmarked datasets and referring to Appendix 1 for data description and Appendix Table 2 for hyperparameter settings, but it does not explicitly provide specific training, validation, or test split percentages or sample counts in the main text. |
| Hardware Specification | Yes | The memory consumption for inference with the x Trimo Gene-100M model is approximately 50GB, whose hardware requirement (Nvidia A100 80G GPU) is beyond some academic labs... |
| Software Dependencies | No | The paper does not explicitly list any software components with their specific version numbers (e.g., Python 3.x, PyTorch 1.x, CUDA 11.x) that would be needed to replicate the experiments. |
| Experiment Setup | Yes | To test the scale-up ability of x Trimo Gene, we pre-trained three models across multiple compute regions and scales (e.g., from 3M to 100M parameters). The detailed hyperparameter setting is displayed in the App. Table 2. |