Investigating protein patterns in human leukemia cell line experiments: A Bayesian approach for extremely small sample sizes. (April 2020)

• Record Type: Journal Article
• Authors: Chekouo, Thierry; Stingo, Francesco C; Class, Caleb A; Yan, Yuanqing; Bohannan, Zachary; Wei, Yue; Garcia-Manero, Guillermo; Hanash, Samir; Do, Kim-Anh
• Other Names: Fox Jean-Paul guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 29:Number 4(2020)
• Page Start: 1181
• Page End: 1196
• Publication Date: 2020-04
• Subjects: AML/MDS -- Bayesian mixture model -- cell line experiments -- protein isoform -- small sample size; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280219852721
• Languages: English
• ISSNs: 0962-2802

Abstract

Human cancer cell line experiments are valuable for investigating drug sensitivity biomarkers. The number of biomarkers measured in these experiments is typically on the order of several thousand, whereas the number of samples is often limited to one or at most three replicates for each experimental condition. We have developed an innovative Bayesian approach that efficiently identifies clusters of proteins that exhibit similar patterns of expression. Motivated by the availability of ion mobility mass spectrometry data on cell line experiments in myelodysplastic syndrome and acute myeloid leukemia, our methodology can identify proteins that follow biologically meaningful trends of expression. Extensive simulation studies demonstrate good performance of the proposed method even in the presence of relatively small effects and sample sizes.