RIPPLE: A Self-Dimensioning Factor Model for Single-Cell Transcriptomics

Single-cell atlases are becoming biology's reference maps, and almost every downstream analysis runs on a low-dimensional embedding of the counts rather than on the counts themselves. Such an analysis must discover the cell types and states of interest, so the representation has to be learned without labels. Two choices govern it and are usually left unprincipled: how many factors the representation carries, and whether its coordinates mean anything. We present RIPPLE, an unsupervised negative binomial factor model that settles both from one design. A sparsity-inducing half-Cauchy prior on the per-factor loading scales collapses the factors the data do not support, and the active dimension is read off the fitted posterior by a signal-to-noise rule with no threshold to tune. The decoder is linear, so every factor that survives is a gene program that can be read directly. Under mild conditions, we prove that this rule loses no true factor, and that the fitted signal carried beyond the true factors is asymptotically weightless. On a six-dataset, eight-method benchmark, RIPPLE outperforms every unsupervised baseline on every biology-preserving metric averaged over the datasets. On the 585k-cell Human Lung Cell Atlas, a single selected factor separates each of the 61 annotated cell types at a median AUROC of 0.96, independently of how rare the cell type is.