Fig. 2

Benchmarking workflow. Starting from aligned read files in .bam format, feature matrices were constructed using each method. The feature matrix construction techniques used by each method were grouped into four broad categories: define regions, count features, transformation, and dimensionality reduction. A colored dot under a technique indicates that the method (signified by the respective color in the legend on the right) uses that technique. For each method, feature matrix files (defined as columns as cells and rows as features) are calculated and used to perform hierarchical, Louvain, and k-means clustering analysis. For datasets with a ground truth such as FACS-sorting labels or known tissues, clustering evaluation was performed according to the adjusted Rand index (ARI), adjusted mutual information (AMI), and homogeneity (H) scores. For datasets without ground truth, the clustering solutions were evaluated according to a Residual Average Gini Index (RAGI), a metric that compares cluster separation based on known marker genes against housekeeping genes. Lastly, a final score is assigned to each method