TY - JOUR
T1 - hemaClass.org
T2 - Online One-By-One Microarray Normalization and Classification of Hematological Cancers for Precision Medicine
AU - Larsen, Steffen Falgreen
AU - Ellern Bilgrau, Anders
AU - Brøndum, Rasmus Froberg
AU - Hjort Jakobsen, Lasse
AU - Have, Jonas
AU - Lindblad Nielsen, Kasper
AU - El-Galaly, Tarec Christoffer
AU - Bødker, Julie Støve
AU - Schmitz, Alexander
AU - H Young, Ken
AU - Johnsen, Hans Erik
AU - Dybkær, Karen
AU - Bøgsted, Martin
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
U2 - 10.1371/journal.pone.0163711
DO - 10.1371/journal.pone.0163711
M3 - Journal article
C2 - 27701436
SN - 1932-6203
VL - 11
JO - P L o S One
JF - P L o S One
IS - 10
M1 - e0163711
ER -