A deep learning framework for the reconstruction of gene expression profiles derived from formalin-fixed paraffin-embedded samples

This dataset was applied to evaluate the performance of a deep learning framework, FFPERescuer, specifically designed to reconstruct gene expression profiles from RNA sequencing data derived from FFPE (formalin-fixed, paraffin-embedded) tumor tissues. The dataset includes a total of 12 RNA-Seq samples, comprising 10 FFPE tumor tissue samples from colorectal cancer (CRC) cases collected in Amsterdam, the Netherlands, with 2 duplicate samples included for reproducibility assessment. The corresponding gene expression profiles from fresh-frozen tumor tissues for these 10 cases are available in the dataset GSE33113, generated using microarray technology. Overall design: Expression profiling by high throughput sequencing