Scholarlink

<p>The false discovery rate (FDR) controlling method by Benjamini and Hochberg (BH) is a popular choice in the omics fields. Here, we demonstrate that in datasets with a large degree of dependencies between features, FDR correction methods like BH can sometimes counter-intuitively report very high numbers of false positives, potentially misleading researchers. We call the attention of researchers to use suited multiple testing strategies and approaches like synthetic null data (negative control) to identify and minimize caveats related to false discoveries, as in the cases where false findings do occur, they may be numerous.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Beware of counter-intuitive levels of false discoveries in datasets with strong intra-correlations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月18日

查看全文
  • Chakravarthi Kanduri,
  • Maria Mamica,
  • Emilie Willoch Olstad,
  • Manuela Zucknick,
  • Jingyi Jessica Li,
  • Geir Kjetil Sandve

摘要

The false discovery rate (FDR) controlling method by Benjamini and Hochberg (BH) is a popular choice in the omics fields. Here, we demonstrate that in datasets with a large degree of dependencies between features, FDR correction methods like BH can sometimes counter-intuitively report very high numbers of false positives, potentially misleading researchers. We call the attention of researchers to use suited multiple testing strategies and approaches like synthetic null data (negative control) to identify and minimize caveats related to false discoveries, as in the cases where false findings do occur, they may be numerous.