The effect of clustering on differential gene expression receiver operating characteristic (ROC) curves. The unique true positive differentially expressed clusters against unique false positive clusters in the de novo analysis is shown. A unique positive refers to only counting the first instance of a gene that appears multiple times in the ranked list. Corset performed similarly to or better than CD-HIT-EST and the assembler’s own clustering, in all cases: (A) chicken data assembled with Trinity; (B) chicken data assembled with Oases; (C) human data assembled with Trinity; (D) human data assembled with Oases; (E) yeast data assembled with Trinity; and (F) yeast data assembled with Oases. For comparison, we also show the results of no clustering, where the analysis was performed at the level of contigs rather than clusters.