Quantitative reproducibility analysis for identifying reproducible targets from high-throughput experiments

• Published in: BMC systems biology Vol. 11; no. 1; p. 73
• Main Authors: Zhang, Wenfei, Liu, Ying, Zhang, Mindy, Zhu, Cheng, Lu, Yuefeng
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11.08.2017; BioMed Central
• Subjects: Analysis; Bayes Theorem; Bayesian analysis; Biological research; Cancer; Computer simulation; Experiments; Gene expression; Genomics; Identification methods; Meta-analysis; Methodology; Methods; Models, Biological; Oligonucleotide Array Sequence Analysis; Pulmonary fibrosis; Reproducibility; Standard deviation; Statistical analysis; Statistical tests; Studies; Target recognition; United States; Empirical Bayes; Gaussian mixture; Bayesian classification; Reproducibility; EM algorithm; High-throughput experiment

High-throughput assays are widely used in biological research to select potential targets. One single high-throughput experiment can efficiently study a large number of candidates simultaneously, but is subject to substantial variability. Therefore it is scientifically important to performance quant...