Abstract Summary Epigenetic modifications reflect key aspects of transcriptional regulation, and many epigenomic data sets have been generated under many biological contexts to provide insights into regulatory processes. However, the technical noise in epigenomic data sets and the many dimensions (features) examined make it challenging to effectively extract biologically meaningful inferences from these data sets. We developed a package that reduces noise while normalizing the epigenomic data by a novel normalization method, followed by integrative dimensional reduction by learning and assigning epigenetic states. This package, called S3V2-IDEAS, can be used to identify epigenetic states for multiple features, or identify signal intensity states and a master peak list across different cell types for a single feature. We illustrate the outputs and performance of S3V2-IDEAS using 137 epigenomics data sets from the VISION project that provides V al I dated S ystematic I ntegrati ON of epigenomic data in hematopoiesis. Availability and implementation S3V2-IDEAS pipeline is freely available as open source software released under an MIT license at: https://github.com/guanjue/S3V2_IDEAS_ESMP Contact rch8@psu.edu , gzx103@psu.edu Supplementary information S3V2-IDEAS-bioinfo-supplementary-materials.pdf

S3V2-IDEAS: a package for normalizing, denoising and integrating epigenomic datasets across different cell types