A stemness score for single-cell RNA sequencing data using a protein-protein network as a scaffold

SENRA, DANIELA; GUISONI, NARA; DIAMBRA, LUIS

Congreso; 3rd Women in Bioinformatics and Data Science LA Conference; 2022

Single-cell RNA sequencing (scRNA-seq) is a powerful, high-resolution technique used to study cellular heterogeneity at the transcriptomic level. It has been successful in discovering novel cell types in cancer and analyzing lineages in the context of embryonic development.A common application of scRNA-seq data is trajectory inference. However, it is often necessary to previously identify the origin of the trajectories, namely the stem or progenitor cells. Typically, previously known stemness markers are used to determine the root cell, but it is not always feasible due to the high drop-out rate of the scRNA-seq technique. Moreover, stemness markers depend on the tissue and the developmental stage. Other alternatives have been proposed, such as entropy-based algorithms or pluripotency scores based on different hypotheses, for example that gene counts generally correlate with differentiation status.Here, we developed a novel tool to quantify pluripotency from single cell transcriptomics data that does not need prior knowledge of stemness markers. The algorithm is implemented as a free and open-source R package. The approach uses the protein-protein interaction (PPI) network associated with the differentiation process as a scaffold and the gene expression matrix to calculate a score that we call differentiation activity. This score reflects how active the differentiation network is in each cell. We benchmark the performance of our algorithm with two previously publicated methodologies, LandSCENT and CytoTRACE, for four healthy human data sets: breast, colon, hematopoietic and lung. We show that our algorithm is more efficient than LandSCENT and requires less RAM memory than the other programs. We also perform a complete workflow from the count matrix to trajectory inference using the breast data set.