The application discloses a
cell communication prediction method and application based on Boosting, deep forest and single-
cell sequencing data, and is characterized in that: on the basis of extracting ligand and
receptor biological characteristics, a limit
gradient boosting algorithm is designed to select the characteristics of ligand-
receptor pairs; then, based on a category characteristic
gradient boosting algorithm, a
natural gradient boosting
algorithm and a deep forest model, an integrated framework is designed to predict ligand-
receptor interaction; and combined with single-
cell sequencing data of tumor tissues, known and predicted ligand-
receptor interaction data are filtered; and then, according to the filtered ligand-
receptor interaction and single-cell
sequencing data, combined with an expression product method and an expression threshold method, cell communication in a
tumor microenvironment is predicted. The method can improve the prediction effect of cell communication, can be applied to cell communication prediction in
human tumor tissues, and solves the problem that the accuracy of predicting cell communication intensity based on ligand-
receptor interaction in the prior art is not high.