KAFSTExp: Kernel Adaptive Filtering With Nyström Approximation for Predicting Spatial Gene Expression From Histology Images

Spatial transcriptomics (ST), known as an expensive medical examination, plays an important role in analyzing the spatial heterogeneity of tumors. When considering the correlation between tissue morphological patterns and gene profiles, predicting corresponding gene expression from pathology images obtained from affordable biopsies is regarded as an instantaneous and cost-effective alternative. However, accurately modeling the complex and nonlinear relationship between histological features and gene expression remains challenging. Existing deep learning models often struggle to generalize on limited ST datasets due to their large and overparameterized architectures. The primary advantage of kernel adaptive filtering (KAF) lies in its ability to transform a challenging nonlinear problem arising in the original space into a linear regression problem in the higher-dimensional feature space via kernel methods. Therefore, this paper proposes a framework called KAFSTExp, which utilizes the state-of-the-art pathology foundation model UNI to extract image feature vectors, and then introduces the kernel least mean square algorithm with Nyström approximation to predict the normalized transcript counts of specific genes. Extensive experiments show that KAFSTExp significantly improves prediction accuracy while reducing computational cost and training time. KAFSTExp demonstrates consistent performance gains across multiple ST datasets, achieving relative improvements in Pearson correlation coefficient ranging from 1.24% to 94.23%, with an average increase of 19.80% over the best-performing non-KAF methods. External validation and further clinical analysis confirm the generalization performance and clinical application value of the proposed KAFSTExp.