hemaClass.org: Online One-By-One Microarray Normalization and Classification of Hematological Cancers for Precision Medicine.

Journal Article (Journal Article)

BACKGROUND: Dozens of omics based cancer classification systems have been introduced with prognostic, diagnostic, and predictive capabilities. However, they often employ complex algorithms and are only applicable on whole cohorts of patients, making them difficult to apply in a personalized clinical setting. RESULTS: This prompted us to create hemaClass.org, an online web application providing an easy interface to one-by-one RMA normalization of microarrays and subsequent risk classifications of diffuse large B-cell lymphoma (DLBCL) into cell-of-origin and chemotherapeutic sensitivity classes. Classification results for one-by-one array pre-processing with and without a laboratory specific RMA reference dataset were compared to cohort based classifiers in 4 publicly available datasets. Classifications showed high agreement between one-by-one and whole cohort pre-processsed data when a laboratory specific reference set was supplied. The website is essentially the R-package hemaClass accompanied by a Shiny web application. The well-documented package can be used to run the website locally or to use the developed methods programmatically. CONCLUSIONS: The website and R-package is relevant for biological and clinical lymphoma researchers using affymetrix U-133 Plus 2 arrays, as it provides reliable and swift methods for calculation of disease subclasses. The proposed one-by-one pre-processing method is relevant for all researchers using microarrays.

Full Text

Duke Authors

Cited Authors

  • Falgreen, S; Ellern Bilgrau, A; Brøndum, RF; Hjort Jakobsen, L; Have, J; Lindblad Nielsen, K; El-Galaly, TC; Bødker, JS; Schmitz, A; H Young, K; Johnsen, HE; Dybkær, K; Bøgsted, M

Published Date

  • 2016

Published In

Volume / Issue

  • 11 / 10

Start / End Page

  • e0163711 -

PubMed ID

  • 27701436

Pubmed Central ID

  • PMC5049784

Electronic International Standard Serial Number (EISSN)

  • 1932-6203

Digital Object Identifier (DOI)

  • 10.1371/journal.pone.0163711

Language

  • eng

Conference Location

  • United States