This module, formerly called Gene Expression profiling, has been restructured to emphasize how the theory and application of transcriptomics can be extended to include other types of omic analysis, and then integrated using statistical and machine learning tools. We start with the statistical basis of hypothesis testing, covering the central role of normalization strategies and the specifics of differential expression analysis. Students will be given the opportunity to work examples using open source R code that is in standard use for RNASeq data. We then discuss options for downstream processing by clustering and module detection/comparison; extensions to methylation profiling, proteomics, and metabolomics; eQTL analysis including fine mapping of regulatory variation; and finally integrative methodologies addressing the relationship between genomic, meta-genomic, and phenotypic variation. This module deals primarily with upstream data processing methods that lead to the delineation of networks and pathways that are then considered in Module 16.