Difference between revisions of "SOCR EduMaterials AnalysesCommandLineVolumeMultipleRegression"

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* Example call:
 
* Example call:
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<code> java -cp /ifs/ccb/CCB_SW_Tools/others/Statistics/SOCR_Statistics/bin/SOCR_core.jar:/ifs/ccb/CCB_SW_Tools/others/Statistics/SOCR_Statistics/bin/SOCR_plugin.jar edu.ucla.stat.SOCR.analyses.command.SplitMaskPositiveNegativeAnalysisResults -dim Zmax Ymax XMax -input filename -mask mask_filename -threshold Value [-below filename] [-above filename] -data_type [1,4] </code>  
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<code> java -cp /ifs/ccb/CCB_SW_Tools/others/Statistics/SOCR_Statistics/bin/SOCR_core.jar:/ifs/ccb/CCB_SW_Tools/others/Statistics/SOCR_Statistics/bin/SOCR_plugin.jar edu.ucla.stat.SOCR.analyses.command.SplitMaskPositiveNegativeAnalysisResults -dim Zmax Ymax XMax -input filename -mask mask_filename -threshold Value [-below filename] [-above filename] -data_type [0,1,2,3,4] </code>  
  
 
* Options:
 
* Options:

Revision as of 23:22, 25 February 2009

This page includes the information on how to access the Multiple Linear Regression library for the purpose of computing VOLUME/IMAGE MLR analyses. Access is provided via shell-based command-line interface on local machines. More information about other SOCR Analyses command-line interfaces is available here.

Introduction

In addition to the graphical user interfaces, via a web-browser, all SOCR Analyses allow command-line shell execution on local systems.

General Usage

  • Get the latest SOCR JAR files from the SOCR page (http://socr.ucla.edu/htmls/jars/).
  • The command-line interface to SOCR Analyses generally uses EXAMPLE 1 from the list of example data files for the corresponding analysis.
  • All Input files are ASCII (see examples within each of the specific analyses).
  • a -h flag at the end of the command-line indicates that the first row in all ASCII input data files is a HEADER row (so it's not interpreted as data)
  • Number of variables can be indicated at the end (after -h flag). If no number of variables is specified, 3 is set as default.

Volume Multiple Linear Regression Usage

  • Generic Setting:

java -cp /ifs/ccb/CCB_SW_Tools/others/Statistics/SOCR_Statistics/bin/SOCR_core.jar:/ifs/ccb/CCB_SW_Tools/others/Statistics/SOCR_Statistics/bin/SOCR_plugin.jar edu.ucla.stat.SOCR.analyses.command.VolumeMultipleRegression -dm DesignMatrix.txt -h -regressors [name1,name2,...name_k] -dim Zmax Ymax XMax [-p PValue_Filename] [-r RValue_Filename] [-t TStat_Filename] -data_type [0,1,2,3,4] -mask /ifs/tmp/myMaskVolume.img

  • Options:
    • -help: print usage
    • -dm [DesignMatrix.txt]: specify a tab-separated text file containing the design matrix
    • -mask [Mask-volume.img]: specify a mask-volume (0 or 1 intensities) restricting the voxels, where the regression models are computed (optional), 1 Unsigned-Byte Analyze format volume of the same dimensions as the data (intensity spectrum [0:255], all intensities >0 are consuiderd part of the mask and processed)
    • -h: DesignMatrix contains a header (first row)
    • -regressors [name1,name2,...name_k]: specify which columns/variables should be used as regressors/covariates
    • -dim Zmax Ymax XMax: specify the dimension-sizes (for 2D images use ZMax=1, for 1D, Zmax=Y_Max=1
    • -p [PValue_Filename]: output the p-value volume (enter only the base of the filename)
    • -r [RValue_Filename]: output the effect-size/correlation volume (enter only the base of the filename)
    • -t [Tstat_Filename]: output the T-Statistics for the effect-size Beta (enter only the base of the filename)
    • -data_type [0,1,2,3,4]: Type=0 is for Unsigned Byte, Type=1 is for Signed Byte, Type=2 is for Unsigned Short Integer, Type=3 is for Signed Short Integer and Type=4 is for 4Byte=Float Volume Input;
    • -byteswap: Only enter this flag if you want the input data to be read in and byteswapped! Note that -byteswap effects : input data, mask-volume and output results!
    • Memory Use: Note that for some large file sizes, you may need to request more memory form the JVM. If your data is larger than 2003 then use these parameters after the initial java call (-ms2000m -mx4000m), see the example below. This requests 2-4GB or RAM memory for this process. You may need more or less memory depending on the number of volumes and dimension sizes.
  • Example: Edit a new file (VolumeMultipleRegression.csh) using any editor and paste this inside (make sure the file has executable permissions). Some operating systems/platforms may require variants of this (C-shell) script.

#!/bin/csh

date

java -ms2000m -mx4000m -cp /ifs/ccb/CCB_SW_Tools/others/Statistics/SOCR_Statistics/bin/SOCR_core.jar:/ifs/ccb/CCB_SW_Tools/others/Statistics/SOCR_Statistics/bin/SOCR_plugin.jar edu.ucla.stat.SOCR.analyses.command.VolumeMultipleRegression -dm /ifs/ccb/CCB_SW_Tools/others/Statistics/SOCR_Statistics/SOCR_CSV_test_Scripts_Data/DM.txt -h -regressors CDR,MMSE -dim 220 220 220 -p /ifs/tmp/P_Value -r /ifs/tmp/R_Value -t /ifs/tmp/TStat_Value -data_type 2

date

exit

Example Input data files

The design-matrix datafile must be provided as tab-separated ASCII/text file (DM.txt). The ASCII content of each of these files should follow the syntax below. Note that the first lines in these files are column headers. This requires the "-h" flag on the command line at execution so that these first lines are interpreted as column headers. The first two columns are the Subject Identifier and filenames for the corresponding imaging volumes, respectively. Columns 3 and on store the corresponding predictor variable (covariate) values. Typically there will be between 1 and 10 covariates.

SUBJECT_ID FILENAME SEX GROUP_ID AGE CDR MMSE
002_S_0413 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_002_S_0413.img F Normal 76.38 0 29
002_S_0559 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_002_S_0559.img M Normal 79.37 0 30
002_S_0729 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_002_S_0729.img F MCI 65.22 0.5 27
002_S_0954 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_002_S_0954.img F MCI 69.42 0.5 25
002_S_1018 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_002_S_1018.img F AD 70.75 0.5 26
002_S_1070 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_002_S_1070.img M MCI 73.73 0.5 25
002_S_1261 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_002_S_1261.img F Normal 71.2 0 30
002_S_1268 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_002_S_1268.img M MCI 82.78 0.5 28
002_S_1280 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_002_S_1280.img F Normal 70.8 0 30
005_S_0324 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_005_S_0324.img F MCI 75.35 0.5 24
005_S_0448 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_005_S_0448.img M MCI 85.65 0.5 26
005_S_0553 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_005_S_0553.img M Normal 84.76 0 30
005_S_0572 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_005_S_0572.img M MCI 78.87 0.5 26
005_S_0602 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_005_S_0602.img M Normal 70.87 0 29
005_S_0814 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_005_S_0814.img F AD 71.12 0.5 21
007_S_1206 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_007_S_1206.img M Normal 72.98 0 29
007_S_1222 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_007_S_1222.img F Normal 73.44 0 30
007_S_1304 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_007_S_1304.img F AD 74.76 1 25
012_S_0689 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_012_S_0689.img M AD 63.65 0.5 22
012_S_1009 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_012_S_1009.img M Normal 75.91 0 28
012_S_1212 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_012_S_1212.img F Normal 75.4 0 27
012_S_1292 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_012_S_1292.img M MCI 76.31 0.5 26
012_S_1321 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_012_S_1321.img M MCI 83.24 0.5 28
013_S_0996 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_013_S_0996.img F AD 90.99 0.5 26
013_S_1035 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_013_S_1035.img M Normal 87.33 0 30
013_S_1276 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_013_S_1276.img F Normal 71.94 0 30
016_S_1117 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_016_S_1117.img F MCI 68.98 0.5 26
016_S_1121 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_016_S_1121.img F MCI 56.25 0.5 24
016_S_1138 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_016_S_1138.img M MCI 67.49 0.5 27
016_S_1149 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_016_S_1149.img M MCI 84.44 0.5 29
016_S_1326 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_016_S_1326.img M MCI 66.37 0.5 28
018_S_0335 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_018_S_0335.img F AD 83.66 1 20
018_S_0369 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_018_S_0369.img M Normal 76.11 0 30
018_S_0406 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_018_S_0406.img M MCI 77.87 0.5 29
018_S_0450 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_018_S_0450.img M MCI 68.54 0.5 30
018_S_0633 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_018_S_0633.img M AD 83.37 1 20
020_S_1288 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_020_S_1288.img M Normal 59.98 0 30
021_S_0332 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_021_S_0332.img M MCI 70.03 0.5 25
021_S_0753 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_021_S_0753.img M AD 65.56 1 24
023_S_0030 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_023_S_0030.img F MCI 80.04 0.5 29
023_S_0031 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_023_S_0031.img F Normal 77.81 0 30
023_S_0058 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_023_S_0058.img M Normal 70.2 0 30
023_S_0061 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_023_S_0061.img F Normal 77.14 0 29
023_S_0078 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_023_S_0078.img F MCI 76.07 0.5 24
023_S_0139 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_023_S_0139.img F AD 65.94 0.5 25
023_S_0331 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_023_S_0331.img F MCI 64.66 0.5 27
023_S_0376 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_023_S_0376.img M MCI 70.55 0.5 28
023_S_0388 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_023_S_0388.img M MCI 71.3 0.5 25
023_S_0604 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_023_S_0604.img M MCI 86.59 0.5 25
023_S_0613 /ifs/adni/3T/PERMS/IVO/JACOBIANS/3T_bl_023_S_0613.img F MCI 84.09 0.5 24

Supplementary information

Auxiliary tools

Masking statistical volumes

This tool provides the functionality to construct Masks of Stat Analysis results (e.g., P, R or T-stat maps outputted by VolumeMultipleRegression) given a user-specified threshold value. The output masks are 0 and 1 Byte volumes (determined by the threshold value).

  • Example call:

java -cp /ifs/ccb/CCB_SW_Tools/others/Statistics/SOCR_Statistics/bin/SOCR_core.jar:/ifs/ccb/CCB_SW_Tools/others/Statistics/SOCR_Statistics/bin/SOCR_plugin.jar edu.ucla.stat.SOCR.analyses.command.SplitMaskPositiveNegativeAnalysisResults -dim Zmax Ymax XMax -input filename -mask mask_filename -threshold Value [-below filename] [-above filename] -data_type [0,1,2,3,4]

  • Options:
    • -help: print usage
    • -mask [Mask-volume.img]: specify a mask-volume (0 or 1 intensities) restricting the voxels where the regression models are computed (optional), 1Byte Analyze format volume of the same dimensions as the data
    • -dim Zmax Ymax XMax: specify the dimension-sizes (for 2D images use ZMax=1, for 1D, Zmax=Y_Max=1
    • -below [Filename]: output the p-value volume (1-Byte)
    • -above [Filename]: output the effect-size/correlation volume (1-Byte)
    • -threshold [threshold_value]: threshold value separating the below/above intensities of the input file
    • -input [Filename]: input file-name
    • -data_type [0,1,2,3,4]: Type=0 is for Unsigned Byte input volumes;
      • Type=1 is for Signed Byte input volumes;
      • Type=2 is for Unsigned-short integers
      • Type=3 is for Signed-short integers
      • Type=4 is for 4Byte=Float Volumes
    • -byteswap:Only enter this flag if you want the input data to be read in and byteswapped! Note that -byteswap effects: input data, mask-volume and output results!

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