Q-value 、P-value and P-adjust

Q-value, also known as the False Discovery Rate (FDR), is a statistical measure used in hypothesis testing to control the rate of false positives. It is the expected proportion of false discoveries among all significant results, and is calculated by adjusting the P-values for multiple comparisons. A lower Q-value indicates a lower rate of false positives.

P-value is a statistical measure that indicates the probability of obtaining a result as extreme or more extreme than the observed result, assuming that the null hypothesis is true. It is commonly used in hypothesis testing to determine whether the null hypothesis can be rejected or not. A lower P-value indicates stronger evidence against the null hypothesis.

P-adjust, also known as the adjusted P-value, is the P-value adjusted for multiple comparisons using a method such as the Bonferroni correction or the False Discovery Rate (FDR) correction. This adjustment is necessary to control the overall Type I error rate, which is the probability of rejecting the null hypothesis when it is actually true, and is particularly important when conducting multiple tests or comparing multiple groups. A lower adjusted P-value indicates stronger evidence against the null hypothesis while controlling for multiple comparisons.