main directories:
/mnt/home/kittipat/Dropbox/random_MATLAB_codes/fMRI/mvpa_Haxby_experiment/biclustering_haxby
/mnt/home/kittipat/Dropbox/random_MATLAB_codes/toolbox_category-sensitive_measure
/NAS_II/Projects/MVPA_Language/haxby_data/category_sensitive_biclustering/subjectX
or
/share/Bot/Research/category_sensitive_biclustering/subjectX
In each subject directory, the files are named in the following format:
subj<subjectID>_<category-sensitive_measure>_<type>_C<desired_#_of_cluster>_<biclustering_algo_name>_<brain_region_name>_<optional>
For example,
subj1_DJS_OAA_C6_CoClust_vtc.mat
subj1_DJS_OAA_C6_CoClust_vtc.txt
subj1_DJS_OAA_C6_CoClust_vtc_category_winner.nii.gz
subj1_DJS_OAA_C6_CoClust_vtc_robust_category_winner.nii.gz
Filename.mat
contains all the necessary matrices and variables as results from biclustering algorithm and the post-process.
Filename.txt
contains the corresponding label# and its category names. For instance,
label#1 :Shoes
label#2 :Cat Scrambled
label#3 :House
label#4 :Bottle Chair
label#5 :Scissors
label#6 :Face
indicates that "Shoes" are labeled with 1. "Cat" and "Scrambled" are grouped together in label 2, and so on
Filename.nii/nii.gz
is nifti format, which can be visualized using FSLview
One way is to select row/column/robust winner biclusters.
Need to read more gene expression papers to get some ideas.
Nov 22, 2012: I made hierarchical biclustering algorithm toolbox available, and apply it on the subject#1. However, the robust winner is usually sporadic! Therefore, I realize what does it mean by the "real" biclustering algorithm, which is subspace clustering. Indeed, we are looking for "interesting" subspaces of the input data matrix that is the direct objective of subspace clustering algorithm. Such subspace CANNOT be obtained by simply using hierarchical biclustering because such an algorithm take all the dimension into account and not just some subspace.
Nov 23, 2012: I test the non-negative matrix factorization for biclustering from Li and Ngom. Though the clustering topology does not look stable across multiple runs, the results look OK. There are pros and cons for this method:
This table might be easier to read:
* A clustering method is stable means that it gives the same clustering topology every time we run it.
There are some good resources and classic variants of subspace clustering and biclustering:
all the papers are downloaded already in "Download/biclustering_papers".
Preliminary results part1 -- Visualization results from Sonya.