Protocol for non-invasive tumor monitoring and diagnosis based on interpretable deep learning

Tumor-specific DNA methylation profiling in plasma cell-free DNA (cfDNA) offers a promising approach for non-invasive tumor detection. Here, we present a protocol that uses Oncoder, an interpretable deep-learning-based framework, to monitor treatment response by tracking dynamic changes in tumor-specific DNA methylation signals in patient plasma cfDNA. We describe steps for data preparation, performing differential methylation analysis, training Oncoder, and interpreting the model's outputs. This protocol is versatile and adaptable to various data types and application scenarios. For complete details on the use and execution of this protocol, please refer to Yang et al.¹.