[caret-users] multiple comparison correction on SUMA data

Donna Dierker donna at brainvis.wustl.edu
Mon Nov 16 10:00:13 CST 2009

On 11/16/2009 05:27 AM, Oers, C.A.M. van wrote:
> Hi,
> Although I saw a post by Donna Dierker regarding the subject, I am 
> afraid I still have a question.
> I have created SUMA files using a mixed model analysis. The resulting 
> files have an individual statistical threshold (and coefficient). I 
> would like to use Caret to do a multiple comparison correction with a 
> cluster size threshold.
I'm not sure how straightforward this will be, due to the randomization 
component.  The caret_command utility has these features:

      caret_command -metric-statistics-anova-one-way
      caret_command -metric-statistics-anova-two-way
      caret_command -metric-statistics-coordinate-difference
      caret_command -metric-statistics-interhemispheric-clusters
      caret_command -metric-statistics-kruskal-wallis
      caret_command -metric-statistics-levene-map
      caret_command -metric-statistics-normalization
      caret_command -metric-statistics-one-sample-t-test
      caret_command -metric-statistics-paired-t-test
      caret_command -metric-statistics-shuffled-cross-correlation-map
      caret_command -metric-statistics-shuffled-t-map
      caret_command -metric-statistics-subtract-group-average
      caret_command -metric-statistics-t-map
      caret_command -metric-statistics-two-sample-t-test
      caret_command -metric-statistics-z-map

Enter the command for the help for that feature, or enter "caret_command 
-help-ful > caret_command.txt" to get a text file with all the help.

Let's choose the case of a two-sample-t-test.  In this case, your 
caret_command line will look like so:


COMMAND="caret_command -metric-statistics-two-sample-t-test NO_TRANSFORM 

These sample scripts (e.g., tstt.sh and gen_composites.sh) are here:

login pub
password download

But note that this takes two composite shape files (imagine your 1D 
files concatenated together in one file, with one 1D column per subject, 
with the node number preceding all os them).  I don't think generating 
composites is hard (see below about adding header).

Rather, the tricky bit here is that you already have your stat -- not 
raw scores/activations.  The example above uses sulcal depth scores, but 
this could be any measure.  But if it is already a stat, then it's less 
clear to me how to randomize it.  With the two-sample t-test, a t-map is 
generated using the two groups' composites.  Then, the composites are 
combined and group memberships randomized to create permuted t-maps.  
The max size of each permuted map is stored to create a distribution, 
and the size of the 95th percentile is used as the significance cut-off.

If you've got stats already, how are we randomizing?

If you can create your own permuted t- or f-maps, as well as your own 
real t- or f-map, then there is a newer caret_stats feature that can 
take these real and permuted maps and do either cluster operations or 
TFCE on them.

But in your case, it isn't clear to me where/how the randomizing comes in.

> How can I convert a 1D surface file into a metric or surface_shape file?
This part is easy:

1.  Add a node number in column 1, using matlab or some other tool.

2.  Slap on a header as shown in 

metric-version 2
tag-number-of-nodes 71723
tag-number-of-columns 2
tag-title untitled
tag-column-name 0 Depth
tag-column-name 1 Smoothed Depth
tag-column-color-mapping 0 -1.000000 1.000000
0 1.803019 -0.045549
1 1.704132 -0.007309
2 1.523951 0.026103
71722 .822899  0.112485

If you can generate metric/surface_shape files from all your 1D files, 
then it's not hard to generate a composite using something like the 
gen_composites.sh script mentioned above).
> Which way to perform the correction?
> I appreciate the help,
> Casper
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