Computational Analysis of Single-Cell RNA Sequencing Data

Abstract

Single-cell RNA sequencing has revolutionised transcriptomics by resolving gene expression at individual-cell resolution, yet the computational analysis of the resulting high-dimensional, sparse, and noisy count matrices presents challenges that standard bulk-RNA methods cannot address. We evaluated 216 scRNA-seq computational analysis programmes across bioinformatics centres in Spain and Germany between 2017 and 2021, spanning five analytical task categories: quality control and normalisation pipelines, dimensionality reduction and clustering methods, trajectory inference and pseudotime ordering, cell-type annotation frameworks, and differential expression and gene regulatory network inference. A Single-Cell Analysis Pipeline Index (SCAPI) was constructed from five sub-scores -- biological accuracy of cell-type recovery, scalability to atlas-scale datasets, robustness to technical noise, user accessibility, and benchmark reproducibility -- with weights from regression against sustained adoption into active single-cell research pipelines. SCAPI correlated with adoption at r = +0.84 and discriminated adopted from non-adopted methods with an AUC of 0.882. Quality control and normalisation pipelines scored highest (mean SCAPI 0.824), while gene regulatory network inference trailed at 0.598. Only 35.2 percent of programmes exceeded the 0.75 threshold. Biological accuracy carried the largest regression weight (beta = +0.280), followed by scalability (beta = +0.228).