Whole-genome analysis of cancer specimens is commonplace and investigators frequently share or re-use specimens in later studies. Duplicate expression profiles in public databases will impact re-analysis if left undetected, a so-called “doppelgänger” effect. The doppelgangR package uses batch correction and outlier detection among pairwise expression profile correlations to accurately identify duplicate profiles for cancer types where profiles are sufficiently distinct. It is intended for use when nucleotide-level sequence data are unavailable, and is is effective even for specimens where duplicated samples are profiled by different microarray technologies, or by a combination of microarray and log-transformed RNA-seq data.