Matlab codes for principal component accumulation (PCAcc) method

PCAcc method focuses on improving class separability by using the information 
contained in multiple PC subspaces to identify different types of cancers. 
The programs for the method are implemented in MATLAB. MATLAB 6.5 or higher 
versions should be installed for runing these programs. 

The package consists of 11 MATLAB scripts. Their functions are as follows:
 1. class_AccPC.m: Main function of AccPC method
 2. analgroup.m: dividing samples into two groups
 3. svdpca.m: applying PCA on the training set and projecting the test set 
              onto PC subspaces
 4. disprotpcsallpoints.m: exhibiting samples distribution in PC plot
 5. rotation.m: obtaining the resolutions of all the PC subspaces and finding 
                the PC subspace with maximal resolution
 6. rotpcs: maximizing the distance between groups by using Fisher criterion
 7. stats.m: selecting PC subspaces
 8. dispsumR.m: exhibiting samples distribution along the projected or the 
                accumulated PC axis
 9. dispsumRs.m: calculating mean value, standard deviation and resolution
10. disclass.m: assigning the class label of a sample
11. fastdist.m: calculating Euclidean distance between the samples with the 
                centroid of each group