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.