Medical big data with artificial intelligence are vital in advancing digital medicine. However, the opaque and non-standardised nature embedded in most medical data extraction is prone to batch effects and has become a significant obstacle to reproducing previous works. This paper aims to develop an easy-to-use time-series multimodal data extraction pipeline, Quick-MIMIC, for standardised data extraction from MIMIC datasets. Our method can fully integrate different data structures into a time-series table, including structured, semi-structured, and unstructured data. We also introduce two additional modules to Quick-MIMIC, a pipeline parallelization method and data analysis methods, for reducing the data extraction time and presenting the characteristics of the extracted data intuitively. The extensive experimental results show that our pipeline can efficiently extract the needed data from the MIMIC dataset and convert it into the correct format for further analytic tasks.

Drawing parallels between linguistic constructs and cellular biology, Large Language Models (LLMs) have achieved success in diverse downstream applications for single-cell data analysis. However, to date, it still lacks methods to take advantage of LLMs to infer Ligand-Receptor (LR)-mediated cell-cell communications for spatially resolved transcriptomic data. Here, we propose SpaCCC to facilitate the inference of spatially resolved cell-cell communications, which relies on our fine-tuned single-cell LLM and functional gene interaction network to embed ligand and receptor genes into a unified latent space. The LR pairs with a significant closer distance in latent space are taken to be more likely to interact with each other. After that, the molecular diffusion and permutation test strategies are respectively employed to calculate the communication strength and filter out communications with low specificities. The benchmarked performance of SpaCCC is evaluated on real single-cell spatial transcriptomic datasets with superiority over other methods. SpaCCC also infers known LR pairs concealed by existing aggregative methods and then identifies communication patterns for specific cell types and their signaling pathways. Furthermore, SpaCCC provides various cell-cell communication visualization results at both single-cell and cell type resolution. In summary, SpaCCC provides a sophisticated and practical tool allowing researchers to decipher spatially resolved cell-cell communications and related communication patterns and signaling pathways based on spatial transcriptome data. SpaCCC is free and publicly available at https://github.com/jiboyalab/SpaCCC.