The false discovery rate (FDR) is a statistical method used to control the expected proportion of incorrectly rejected null hypotheses in multiple hypothesis testing. It's crucial in fields that rely on large-scale data analysis, such as genomics and transcriptomics, where numerous tests are conducted simultaneously. Controlling the FDR helps to balance the trade-off between discovering true effects and limiting false positives, making it a key consideration when interpreting results from methods like RNA-Seq.
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