The recognition of subtle variation among cells of the same class presents a formidable analytical challenge for unsupervised analysis (Mayer et al., 2015; Satija et al., 2015; Shekhar et al., 2016; Tanay and Regev, 2017; Tasic et al., 2016). The size, diversity, and replicates for each atlas region highlighted limitations in current methods, exemplified by clusters specific to experimental replicates or driven by tissue digestion artifacts (see below). We sought a strategy that would (i) dissect biological from technical contributions to expression data and (ii) generate intermediate outputs (upstream of clustering) that could be critically evaluated and analyzed.
