ABSTRACT
Omic data are the product of high throughput technologies that are affected by laboratory conditions, reagent lots and personnel. Biological systems are highly reactive on small changes in the surrounding physical an chemical conditions. This chapter analyzes collected data and readers interested in collecting new data should refer to authors discussing the study design of specific omic studies. It illustrates how to detect and correct the batch effect, taking as an example transcriptomic data. The chapter discusses how to detect unwanted variation in omic data from high throughput experiments using surrogate variable analysis. ComBat function implements an algorithm that allows the creation of a new expression dataset in which the batch effect has been removed. Batch effects are an important source of omic variation and constitute an important source of confounding in association tests. The chapter shows that aims to detect transcriptomic differences, using microarray data, among Alzheimer’s disease cases, mild cognitive impairment patients and controls.
