Should I use p-value or Q value?
A p-value gives you the probability of a false positive on a single test; If you’re running hundreds or thousands of tests from small samples (which are common in fields like genomics), you should use q-values.
How do you find the p-value from Q value?
Here’s how to calculate a Q-value:
- Rank order the P-values from all of your multiple hypotheses tests in an experiment.
- Calculate qi = pi N / i.
- Replace qi with the lowest value among all lower-rank Q-values that you calculated.
What’s the difference between a p value and a Q value?
Another way to look at the difference is that a p-value of 0.05 implies that 5% of all tests will result in false positives. An FDR adjusted p-value (or q-value) of 0.05 implies that 5% of significant tests will result in false positives. The latter will result in fewer false positives.
How is the function LFDR used in qvalue?
The function lfdr is also called internally and calculates the estimated local FDR values. Arguments for these functions can be included via and will be utilized in the internal calls made in qvalue. See http://genomine.org/papers/Storey_FDR_2011.pdf for a brief introduction to FDRs and q-values.
How does the false discovery rate affect p-values?
The False Discovery Rate approach is a more recent development. This approach also determines adjusted p-values for each test. However, it controls the number of false discoveries in those tests that result in a discovery (i.e. a significant result).
How are p-values adjusted for multiple testing?
Multiple testing and the False Discovery Rate. While there are a number of approaches to overcoming the problems due to multiple testing, they all attempt to assign an adjusted p-value to each test or reduce the p-value threshold from 5% to a more reasonable value. Many traditional techniques such as the Bonferroni correction are too