From Cell‐Free Transcriptomes to Single‐Cell Landscapes: Biomarker Discovery and Originating Cell Alteration Analysis via Graph Matrix Factorization

Characterizing the cellular origin and disease‐driven dynamics of cfRNA is essential for integrating cfRNA profiling into clinical workflows and precision‐medicine strategies. Most cfRNA studies are restricted to bulk‐level analyses, which preclude detailed analysis of alterations in the cellular origins of cfRNA. Single‐cell RNA sequencing reveals cellular heterogeneity and communication, but its application to cfRNA is limited by diverse cellular origins, leaving a critical gap in understanding functional alterations in cfRNA biomarker‐originating cells. In this work, we propose CellFreeGMF, a tool designed to enable diagnosis classification of clinical samples, identify cfRNA biomarkers, and analyze the alterations in their originating cells based on graph matrix factorization. Furthermore, by utilizing cell–cell communication analysis, CellFreeGMF investigates the functional alterations occurring in the cfRNA originating cells under disease conditions. We validate CellFreeGMF on diverse cell‐free RNA transcriptome clinical datasets. In the case of pancreatic ductal adenocarcinoma (PDAC), CellFreeGMF not only identified cfRNA biomarkers but also traced their cellular origins to myeloid and T‐cell populations. Further analysis revealed significant transcriptomic differences in these cell populations between disease and normal groups. Our user‐friendly CellFreeGMF toolkit ( https://cellfreegmf.readthedocs.io/ ) enables identifying cfRNA biomarkers and elucidating pathophysiological changes in their originating cells.