Multiple Testing Method for Adjusting p -Values

Select a method for adjusting for multiple testing across all categories. Adjusted p-values are computed for the method that you select, and a corresponding -log 10 ( p -value) cutoff is computed for Volcano Plot s .

The AdaptiveHolm, AdaptiveHochberg, Bonferroni, Holm, Hommel, Sidak, StepBon, and StepSid methods all control for the familywise error rate. The methods based on FDR all control for false discovery rate.

Note : Bootstrap and permutation methods are not available for Parametric Analysis of Gene Set Enrichment (PAGE) tests.

Multiple Testing Method	Definition
None (blank entry)	• Select this option to enable the -log 10 (p-Value) Cutoff field and slider. This selection enables you to specify a -log 10 ( p -value) cutoff directly.
AdaptiveHolm	• Requests adjusted p -values by using the Hochberg and Benjamini (1990) 1 adaptive step-down Bonferroni method.
AdaptiveHochberg	• Requests adjusted p -values by using the Hochberg and Benjamini (1990) 1 adaptive step-up Bonferroni method.
Bonferroni	• Specifies that the Bonferroni adjustments (number of tests p -value) be computed for each test. • Note : These adjustments can be extremely conservative and should be viewed with caution.
Bootstrap	• Specifies adjusted p -values using the Bootstrap method of Westfall and Young (1993) 2 .
Hochberg	• Assumes that p -values are independent and uniformly distributed under their respective null hypotheses, Hochberg (1988) 3 demonstrates that Holm’s step-down adjustments control the familywise error rate even when calculated in step-up fashion. Since the adjusted p -values are uniformly smaller for Hochberg’s method than for Holm’s method, the Hochberg method is more powerful. However, this improved power comes at the cost of having to make the assumption of independence.
Hommel	• Requests adjusted p -values by using the method of Hommel (1988) 4 .
Holm	• See Stepbon , below.
Permutation	• Adjusted p -values are identical to the Bootstrap method, except that the within-stratum resampling is performed without replacement instead of with replacement.
Sidak	• Computes the Šidák adjustment for each test. • Note : These adjustments are slightly less conservative than the Bonferroni adjustments, but they still should be viewed with caution.
StepBon	• Requests adjusted p -values by using the step-down Bonferroni method of Holm (1988).
StepSid	• Requests adjusted p -values by using the Šidák method but in step-down fashion.
AdaptiveFDR	• Requests adjusted p -values by using the Benjamini and Hochberg (2000) 5 adaptive linear step-up method.
DependentFDR	• Requests adjusted p -values by using the method of Benjamini and Yekateuli (2001) 6 .
FDR	• Requests adjusted p -values by using the linear step-up method of Benjamini and Hochberg (1995) 7 . • These p -values do not control the familywise error rate, but they do control the false discovery rate in some cases
FDRBoot	• Requests the bootstrap-resampling false discovery rate controlling method of Yekateuli and Benjamini (1999) 8 .
FDRPerm	• Requests the permutation-resampling false discovery rate controlling method of Yekateuli and Benjamini (1999) 8 .
pFDR	• Computes the " q -values" of Storey (2002) and Storey, Taylor, and Siegmund (2004). PROC MULTTEST treats these " q -values" as adjusted p -values. • The computations depend on selecting a parameter and an estimation method for the false discovery rate.

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1

Hochberg, Y. and Benjamini, Y. (1990). More Powerful Procedures for Multiple Significance Testing. Statistics in Medicine 9: 811–818.

2

Westfall, P.J. and Young, S.S. (1993). Resampling-Based Multiple Testing, New York: John Wiley & Sons.

3

Hochberg, Y. (1988). A Sharper Bonferroni Procedure for Multiple Significance Testing. Biometrika 75: 800–803.

4

Hommel, G. (1988). A Comparison of Two Modified Bonferroni Procedures. Biometrika 75: 383–386.

5

Benjamini, Y. and Hochberg, Y. (2000). On the Adaptive Control of the False Discovery Rate in Multiple Testing with Independent Statistics. Journal of Educational and Behavioral Statistics 25: 60–83.

6

Benjamini, Y. and Yekateuli, D. (2001). The Control of the False Discovery Rate in Multiple Testing under Dependency. Annals of Statistics 29: 1165–1188.

7

Benjamini, Y. and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, B 57: 289–300.

8

Yekateuli, D. and Benjamini, Y. (1999). Resampling-Based False Discovery Rate Controlling Multiple Test Procedures for Correlated Test Statistics. Journal of Statistical Planning and Inference 82: 171-196.

To Specify a Multiple Testing Method:

Make a selection using the drop-down menu.

For Additional Information

Refer to p-Value Adjustments for more details about each of these methods.