Harvard Medical School, Boston, USA.
Department of Neurology, Massachusetts General Hospital, Boston, USA.
Genome Med. 2021 Apr 23;13(1):68. doi: 10.1186/s13073-021-00864-4.
Most two-group statistical tests find broad patterns such as overall shifts in mean, median, or variance. These tests may not have enough power to detect effects in a small subset of samples, e.g., a drug that works well only on a few patients. We developed a novel statistical test targeting such effects relevant for clinical trials, biomarker discovery, feature selection, etc. We focused on finding meaningful associations in complex genetic diseases in gene expression, miRNA expression, and DNA methylation. Our test outperforms traditional statistical tests in simulated and experimental data and detects potentially disease-relevant genes with heterogeneous effects.
大多数两组统计检验都能发现广泛的模式，如均值、中位数或方差的总体变化。这些检验可能没有足够的能力来检测一小部分样本中的效果，例如，一种只对少数患者有效的药物。我们开发了一种针对临床试验、生物标志物发现、特征选择等相关效应的新统计检验方法。我们专注于在基因表达、miRNA 表达和 DNA 甲基化等复杂的遗传疾病中寻找有意义的关联。我们的检验在模拟和实验数据中的表现优于传统的统计检验，并且可以检测出具有异质效应的潜在与疾病相关的基因。
