  While measurement advances now allow extensive surveys of gene activity
(large numbers of genes across many samples), interpretation of these data is
often confounded by noise -- expression counts can differ strongly across
samples due to variation of both biological and experimental origin.
Complimentary to perturbation approaches, we extract functionally related
groups of genes by analyzing the standing variation within a sampled
population. To distinguish biologically meaningful patterns from
uninterpretable noise, we focus on correlated variation and develop a novel
density-based clustering approach that takes advantage of a percolation
transition generically arising in random, uncorrelated data. We apply our
approach to two contrasting RNA sequencing data sets that sample individual
variation -- across single cells of fission yeast and whole animals of C.
elegans worms -- and demonstrate robust applicability and versatility in
revealing correlated gene clusters of diverse biological origin, including cell
cycle phase, development/reproduction, tissue-specific functions, and feeding
history. Our technique exploits generic features of noisy high-dimensional data
and is applicable, beyond gene expression, to feature-rich data that sample
population-level variability in the presence of noise.
