GGE: General-purpose deep meta-learning for classification of human transcriptomes with limited data

Abstract

Transcriptomic classification is often hindered by the small number of samples relative to the high dimensionality of gene expression data. We introduce General Gene Expression (GGE), a deep meta-learning framework designed to support robust classification in this limited-sample setting. By training across 5,220 distinct biomedical prediction objectives drawn from 1,779 different human datasets, GGE learns a model initialization that captures biological patterns shared across heterogeneous classification tasks. This learned initialization has two key advantages. First, it enables improved performance to new datasets using only a small number of labeled samples. Second, because it is learned across diverse prediction objectives, it can be applied to a broad range of biomedical problems. We show that GGE outperforms established classifiers in data-limited settings across a wide range of applications, including datasets generated using different RNA-seq platforms and preprocessing pipelines. In addition, attention-based analysis identifies recurrent genes that contribute to performance across multiple biological objectives, providing insight into shared determinants of human biological states. Together, these results establish GGE is a general-purpose framework for human transcriptome-based classification in biomedical settings where labeled data are scarce.