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

Stat Methods Med Res. 2020 Apr;29(4):1181-1196. doi: 10.1177/0962280219852721. Epub 2019 Jun 7.

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.

Keywords: AML/MDS; Bayesian mixture model; cell line experiments; protein isoform; small sample size.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Cell Line
  • Humans
  • Leukemia, Myeloid, Acute* / drug therapy
  • Myelodysplastic Syndromes*
  • Sample Size