The present application provides a base model
processing method and device for single-
cell proteome detection data, which comprises: obtaining single-
cell proteome detection data, uniformly
processing protein identification information, and standardizing
protein abundance data according to detection technology types to obtain uniformly distributed
protein expression data; mapping
relevant information into identification and abundance embedding vectors respectively and fusing them, adding a special marker vector to construct
cell sequence data; inputting the cell sequence data into a base model for pre-training, optimizing parameters through a
mask reconstruction and global prediction task to obtain a pre-training model; and fine-tuning the model for a
target analysis task, analyzing and
processing to-be-processed data to obtain downstream results. The present application can integrate single-cell
proteome data of different detection technologies and batches, effectively remove batch effects, extract stable cell low-dimensional representations, support multi-downstream task fine-tuning, realize undetected protein
inference and cell
trajectory analysis, and improve data utilization efficiency and task adaptability.