# [BioC] Multiple hypothesis correction and pairwise tests

yuanji at mdanderson.org yuanji at mdanderson.org
Mon Dec 15 18:32:23 MET 2003

```Dear Adaikalavan,

Based on your null hypothesis, the F-test seems to be the right thing do.
The paired t-tests are more suitable for later investigations once the
F-test is rejected.

It helps to consider gene and grade as factors. So the only difference
between t-tests and F-tests is that  when the grade have more than 2
levels, we get an F-test for each gene, instead of a t-test.

Yuan Ji, Ph.D.

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Assistant Professor
Department of Biostatistics
The University of Texas M.D. Anderson Cancer Center
1515 Holcombe Blvd. - Unit 447
Houston, TX 77030-4009
(713)794-4153
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|      <bioconductor at stat.math.ethz.ch>                                                               |
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|      [BioC] Multiple hypothesis correction and pairwise tests                                       |
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Dear all,

Apologies if this is off-topic but I have been pondering about multiple
correction procedures when multiple groups are involved.

In a microarray context, say we have 20000 probes/genes and the responses
are either Grade 1, 2, 3 (assume all arrays correspond to some tumor). The
null hypothesis is that the gene expression means does not vary between the

Suppose, I perform a pairwise t-test for these 3 grade on a gene-by-gene
basis resulting in 3 p-values for each gene. Do I adjust the p-value within
each gene using some multiple correction technique followed adjustment for
multiple hypothesis for 20000 probes ? Or just perform the adjustment for
multiple probes only ?

The other solution I have is to perform F-test, adjust the F-test p-values
and select top \$n\$ genes. Then perform pairwise t-test on the \$n\$ genes
with adjustment to determine which group and how many group means differ. I
think this is the more sensible method. My second question is which of
these methods better ?