Investigating protein patterns in human leukemia cell line experiments: A Bayesian approach for extremely small sample sizes

Thierry Chekouo, Francesco C. Stingo, Caleb A. Class, Yuanqing Yan, Zachary Bohannan, Yue Wei, Guillermo Garcia-Manero, Samir Hanash, Kim Anh Do

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1181-1196
Number of pages16
JournalStatistical methods in medical research
Volume29
Issue number4
DOIs
StatePublished - Apr 1 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author(s) 2019.

Keywords

  • AML/MDS
  • Bayesian mixture model
  • cell line experiments
  • protein isoform
  • small sample size

Fingerprint

Dive into the research topics of 'Investigating protein patterns in human leukemia cell line experiments: A Bayesian approach for extremely small sample sizes'. Together they form a unique fingerprint.

Cite this