bionic (1) sccan.1.gz

NAME

       sccan - part of ANTS registration suite

DESCRIPTION

   COMMAND:
              sccan

              A  tool  for  sparse  statistical analysis on images : scca, pscca (with options), mscca. Can also
              convert an imagelist/mask pair to a binary matrix image.

   OPTIONS:
       -h

              Print the help menu (short version).

       --help

              Print the help menu (long version).  <VALUES>: 1

       -o, --output outputImage

              Output dependent on which option is called.

       -p, --n_permutations 500

              Number of permutations to use in scca.

       -s, --smoother 0

              Smoothing function for variates

       -z, --row_sparseness 0

              Row sparseness - if (+) then keep values (+) otherwise allow +/- values --- always L1

       -i, --iterations 20

              Max iterations for scca optimization (min 20).

       -n, --n_eigenvectors 2

              Number of eigenvectors to compute in scca/spca.

       -r, --robustify 0

              rank-based scca

       -c, --covering 0

              try to make the decomposition cover the whole domain, if possible

       -g, --uselong 0

              use longitudinal formulation ( > 0 ) or not ( <= 0 )

       -l, --l1 0

              use l1 ( > 0 ) or l0 ( < 0 ) penalty, also sets gradient step size e.g. -l 0.5 ( L1 )  ,  -l  -0.5
              (L0) will set 0.5 grad descent step for either penalty

       --PClusterThresh 1

              cluster threshold on view P

       --QClusterThresh 1

              cluster threshold on view Q

       -e, --ridge_cca 0

              Ridge cca.

       --initialization NA

              Initialization file list for Eigenanatomy - must also pass mask option

       --initialization2 NA

              Initialization file list for SCCAN-Eigenanatomy - must also pass mask option

       --mask NA

              Mask file for Eigenanatomy initialization

       --mask2 NA

              Mask file for Eigenanatomy initialization 2

       --partial-scca-option PminusRQ

              Choices for pscca: PQ, PminusRQ, PQminusR, PminusRQminusR

       --prior-weight 0.0

              Scalar  value  weight on prior between 0 (prior is weak) and 1 (prior is strong).  Only engaged if
              initialization is used.

       --get-small 0.0

              Find smallest eigenvectors

       -v, --verbose 0

              set whether output is verbose

       --imageset-to-matrix [list.txt,mask.nii.gz]

              takes a list of image files names (one per line) and converts it to a 2D matrix / image in  binary
              or csv format depending on the filetype used to define the output.

       --timeseriesimage-to-matrix                                     [four_d_image.nii.gz,three_d_mask.nii.gz,
       optional-spatial-smoothing-param-in-spacing-units-default-zero,
       optional-temporal-smoothing-param-in-time-series-units-default-zero
              ]

              takes  a timeseries (4D) image and converts it to a 2D matrix csv format as output.If the mask has
              multiple labels ( more the one ) then the average time series in each label will be  computed  and
              put in the csv.

       --vector-to-image [vector.csv,three_d_mask.nii.gz, which-row-or-col ]

              converts  the 1st column vector in a csv file back to an image --- currently needs the csv file to
              have > 1 columns. if the number of entries in the column does not equal the number of  entries  in
              the mask but the number of rows does equal the number of entries in the mask, then it will convert
              the row vector to an image.

       --imageset-to-projections [list_projections.txt,list_images.txt, bool do-average-not-real-projection ]

              takes a list of image and projection files names (one per line) and writes them to a csv file  ---
              basically computing X*Y (matrices).

       --scca two-view[matrix-view1.mhd,matrix-view2.mhd,mask1,mask2,FracNonZero1,FracNonZero2]

              three-view[matrix-view1.mhd,matrix-view2.mhd,matrix-view3.mhd,mask1,mask2,mask3,FracNonZero1,FracNonZero2,FracNonZero3]
              partial[matrix-view1.mhd,matrix-view2.mhd,matrix-view3.mhd,mask1,mask2,mask3,FracNonZero1,FracNonZero2,FracNonZero3]
              dynsccan[matrix-view1.mhd,matrix-view2.mhd,mask1,mask2,FracNonZero1,FracNonZero2]

              Matrix-based  scca  operations  for 2 and 3 views.For all these options, the FracNonZero terms set
              the fraction of variables to use in the estimate. E.g. if one sets 0.5 then half of the  variables
              will  have non-zero values. If the FracNonZero is (+) then the weight vectors must be positive. If
              they are negative, weights can be (+) or  (-).  partial  does  partial  scca  for  2  views  while
              partialing out the 3rd view.

       --svd  sparse[matrix-view1.mhd,mask1,FracNonZero1,nuisance-matrix]  ---  will  only  use view1 ... unless
              nuisance matrix is specified.

              classic[matrix-view1.mhd,mask1,FracNonZero1,nuisance-matrix] --- will only use  view1  ...  unless
              nuisance  matrix  is  specified.   cgspca[matrix-view1.mhd,mask1,FracNonZero1,nuisance-matrix] ---
              will only use view1 ... unless nuisance matrix is specified, -i  controls  the  number  of  sparse
              approximations per eigenvector, -n controls the number of eigenvectors.  total output will then be
              i*n sparse eigenvectors.  prior[ matrix.mha ,  mask.nii.gz  ,  PriorList.txt  ,  PriorScale.csv  ,
              PriorWeightIn0to1  ,  sparseness  ]  ... if sparseness is set to zero, we take sparseness from the
              priors.                               network[matrix-view1.mhd,mask1,FracNonZero1,guidance-matrix]
              lasso[matrix-view1.mhd,mask1,Lambda,guidance-matrix]
              recon[matrix-view1.mhd,mask1,FracNonZero1,nuisance-matrix]
              recon4d[matrix-view1.mhd,mask1,FracNonZero1,nuisance-matrix]

              a  sparse svd implementation --- will report correlation of eigenvector with original data columns
              averaged over columns with non-zero weights.