z2labelmap 1.1.6

Convert lh/rh z-score vector to FreeSurfer labelmap

Table of Contents

• Abstract

• Synopsis

• Run

  • Using PyPI

  • Using docker run

• Examples

  • Create a sample/random z-score file and analyze

  • Control relative brightness and lower filter low z-scores from final labelmap

• Command line arguments

Abstract

zlabelmap.py generates FreeSurfer labelmaps from z-score vector files. Essentially the script consumes an input text vector file of

<str_structureName> <float_lh_zScore> <float_rh_zScore>

and creates a FreeSurfer labelmap where <str_structureName> colors correspond to the z-score (normalized between 0 and 255).

Currently, only the aparc.a2009s FreeSurfer segmentation is fully supported, however future parcellation support is planned.

Negative z-scores and positive z-scores are treated in the same manner but have sign-specific color specifications. Positive and negative z-Scores can be assigned some combination of the chars RGB to indicate which color dimension will reflect the z-Score. For example, a

--posColor R --negColor RG

will assign positive z-scores shades of red and negative z-scores shades of yellow (Red + Green = Yellow).

Synopsis

python z2labelmap.py                                            \
    [-v <level>] [--verbosity <level>]                          \
    [--random]                                                  \
    [-p <f_posRange>] [--posRange <f_posRange>]                 \
    [-n <f_negRange>] [--negRange <f_negRange>]                 \
    [-P <'RGB'>] [--posColor <'RGB'>]                           \
    [-N  <'RGB'> [--negColor <'RGB'>]                           \
    [-s <f_scaleRange>] [--scaleRange <f_scaleRange>]           \
    [-l <f_lowerFilter>] [--lowerFilter <f_lowerFilter>]        \
    [-u <f_upperFilter>] [--upperFilter <f_upperFilter>]        \
    [-z <zFile>] [--zFile <zFile>]                              \
    [--version]                                                 \
    [--man]                                                     \
    [--meta]                                                    \
    <inputDir>                                                  \
    <outputDir>

Run

This plugin can be run in two modes: natively as a python package or as a containerized docker image.

Using PyPI

To run from PyPI, simply do a

pip install z2labelmap

and run with

z2labelmap.py --man /tmp /tmp

to get inline help.

Using docker run

To run using docker, be sure to assign an “input” directory to /incoming and an output directory to /outgoing. Make sure that the $(pwd)/out directory is world writable!

Now, prefix all calls with

docker run --rm -v $(pwd)/in:/incoming -v $(pwd)/out:/outgoing      \
        fnndsc/pl-z2labelmap z2labelmap.py                          \

Thus, getting inline help is:

docker run --rm -v $(pwd)/in:/incoming -v $(pwd)/out:/outgoing      \
        fnndsc/pl-z2labelmap z2labelmap.py                          \
        --man                                                       \
        /incoming /outgoing

Examples

Create a sample/random z-score file and analyze

• In the absense of an actual z-score file, the script can create one. This can then be used in subsequent analysis:

mkdir in out
docker run --rm -v $(pwd)/in:/incoming -v $(pwd)/out:/outgoing  \
        fnndsc/pl-z2labelmap z2labelmap.py                      \
        --random                                                \
        --posRange 3.0 --negRange -3.0                          \
        /incoming /outgoing

or without docker

mkdir in out
z2labelmap.py                                                   \
        --random                                                \
        --posRange 3.0 --negRange -3.0                          \
        /in /out

In this example, z-scores range between 0.0 and (+/-) 3.0.

Control relative brightness and lower filter low z-scores from final labelmap

• To analyze a file already located at in/zfile.csv, apply a scaleRange and also filter out the lower 80% of z-scores:

docker run --rm -v $(pwd)/in:/incoming -v $(pwd)/out:/outgoing  \
        fnndsc/pl-z2labelmap z2labelmap.py                      \
        --scaleRange 2.0 --lowerFilter 0.8                      \
        --negColor B --posColor R                               \
        /incoming /outgoing

This assumes a file called ‘zfile.csv’ in the <inputDirectory> that ranges in z-score between 0.0 and 3.0, and uses the –scaleRange to reduce the apparent brightness of the map by 50 percent. Furthermore, the lower 80 percent of z-scores are removed (this has the effect of only showing the brightest 20 percent of zscores).

Command line arguments

<inputDir>
Required argument.
Input directory for plugin.

<outputDir>
Required argument.
Output directory for plugin.

[-v <level>] [--verbosity <level>]
Verbosity level for app. Not used currently.

[--random]
If specified, generate a z-score file based on <posRange> and <negRange>.

[-p <f_posRange>] [--posRange <f_posRange>]
Positive range for random max deviation generation.

[-n <f_negRange>] [--negRange <f_negRange>]
Negative range for random max deviation generation.

[-P <'RGB'>] [--posColor <'RGB'>]
Some combination of 'R', 'G', B' for positive heat.

[-N  <'RGB'> [--negColor <'RGB'>]
Some combination of 'R', 'G', B' for negative heat.

[-s <f_scaleRange>] [--scaleRange <f_scaleRange>]
Scale range for normalization. This has the effect of controlling the
brightness of the map. For example, if this 1.5 the effect
is increase the apparent range by 50% which darkens all colors values.

[-l <f_lowerFilter>] [--lowerFilter <f_lowerFilter>]
Filter all z-scores below (normalized) <lowerFilter> to 0.0.

[-u <f_upperFilter>] [--upperFilter <f_upperFilter>]
Filter all z-scores above (normalized) <upperFilter> to 0.0.

[-z <zFile>] [--zFile <zFile>]
z-score file to read (relative to input directory). Defaults to 'zfile.csv'.

[--version]
If specified, print version number.

[--man]
If specified, print (this) man page.

[--meta]
If specified, print plugin meta data.