DBNorm: normalizing high-density oligonucleotide microarray data based on distributions.

ABSTRACT: BACKGROUND:Data from patients with rare diseases is often produced using different platforms and probe sets because patients are widely distributed in space and time. Aggregating such data requires a method of normalization that makes patient records comparable. RESULTS:This paper proposed DBNorm, implemented as an R package, is an algorithm that normalizes arbitrarily distributed data to a common, comparable form. Specifically, DBNorm merges data distributions by fitting functions to each of them, and using the probability of each element drawn from the fitted distribution to merge it into a global distribution. DBNorm contains state-of-the-art fitting functions including Polynomial, Fourier and Gaussian distributions, and also allows users to define their own fitting functions if require

SUBMITTER: Meng Q 

PROVIDER: S-EPMC5706403 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature