KDDC ： a new framework that integrates Kmers, Dataset filtering, Dimension reduction and Classification algorithms to achieve immune cell heterogeneity classification

Nan Zhang^1,2Shishun Zhao²Runze Wu²Xizi Luo³Zecheng Chang^3*Jianting Xu^1*

• ¹Cancer Center, The First Hospital of Jilin University, Changchun, Jilin Province, China
• ²School of Mathematics, Jilin University, Changchun, Jilin Province, China
• ³College of Basic Medical Sciences, Jilin University, Changchun, Jilin Province, China

Integrating immune repertoire sequencing data with single-cell sequencing data offers profound insights into the diversity of immune cells and their dynamic changes across various disease states. Here, we propose a novel KDDC framework that integrates kmers, dataset selection, dimensionality reduction and classification algorithms to facilitate the heterogeneous classification of immune cells. By comparing various kmer length combinations across seven different classification algorithms, we found that B cell receptor-based cell subset classification outperforms T cell receptor-based classification, achieving an average AUC of over 96%. This finding offers a new perspective on the classification of immune cells. We also observed that 11 distinct cell subpopulations exhibited differences in cell proportions, inflammatory factor expression, cell communication, and metabolic pathways, with notable activity in metabolic pathways. These variations may reflect the adaptive changes of cell subpopulations in response to different disease states. This study aims to uncover the potential biological significance of immune prediction, target antigens, and effective evaluation by analyzing the immune characteristics of specific cell subsets at the cellular level. These findings will not only enhance our understanding of immune system functions but also offer new directions for the development and optimization of immunotherapy.