Skip to main content

Convert lh/rh z-score vector to FreeSurfer labelmap

Project description

https://badge.fury.io/py/z2labelmap.svg https://travis-ci.org/FNNDSC/z2labelmap.svg?branch=master https://img.shields.io/badge/python-3.5%2B-blue.svg

Abstract

zlabelmap.py generates FreeSurfer labelmaps from z-score vector files. These labelmap files are used by FreeSurfer to color-code parcellated brain regions. By calculating a z-score to labelmap transform, we are able to show a heat map and hightlight brain regions that differ from some comparative reference, as demonstrasted below

https://github.com/FNNDSC/pl-z2labelmap/wiki/images/subj1-heatmap/frame126.png

where positive volume deviations of a parcellated brain region are shown in red (i.e. the subject had a larger volume in that area than the reference), and negative volume deviations are shown in blue (i.e. the subject had a smaller volume in that area than reference).

Note that these are randomly generated z-scores purely for illustrative purposes.

Essentially the script consumes an input text vector file of

<str_structureName> <float_lh_zScore> <float_rh_zScore>

for example,

G_and_S_frontomargin     ,1.254318450576827,-0.8663546810093861
G_and_S_occipital_inf    ,1.0823728865077271,-0.7703944006354377
G_and_S_paracentral      ,0.20767669866335847,2.9023126278939912
G_and_S_subcentral       ,2.395503357157743,-1.4966482475891556
G_and_S_transv_frontopol ,-1.7849555258577423,-2.461419463760234
G_and_S_cingul-Ant       ,-2.3831737860960382,1.1892593438667625
G_and_S_cingul-Mid-Ant   ,0.03381695289572084,-0.7909116233500506
G_and_S_cingul-Mid-Post  ,-2.4096082230335485,1.166457973597625
                          ...
                          ...
S_postcentral            ,1.3277159068067768,-1.4042773812503526
S_precentral-inf-part    ,-1.9467169777576718,1.7216636236995733
S_precentral-sup-part    ,0.764673539853991,2.1081570332369504
S_suborbital             ,0.522368665639954,-2.3593237820349007
S_subparietal            ,-0.14697262729901928,-2.2116605141889094
S_temporal_inf           ,-1.8442944920810271,-0.6895142771486307
S_temporal_sup           ,-1.8645248463693804,2.740099589311164
S_temporal_transverse    ,-2.4244451521560073,2.286596403222344

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] [--seed <seed>]                                  \
    [-p <f_posRange>] [--posRange <f_posRange>]                 \
    [-n <f_negRange>] [--negRange <f_negRange>]                 \
    [-P <'RGB'>] [--posColor <'RGB'>]                           \
    [-N  <'RGB'>] [--negColor <'RGB'>]                          \
    [--imageSet <imageSetDirectory>]                            \
    [-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 --seed 1                                       \
        --posRange 3.0 --negRange -3.0                          \
        /incoming /outgoing

or without docker

mkdir in out
z2labelmap.py                                                   \
        --random --seed 1                                       \
        --posRange 3.0 --negRange -3.0                          \
        /in /out

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

Generate labelmap and also copy pre-calculated image set to output

  • Analyze a file already located at in/zfile.csv and copy pre-calculated image data

docker run --rm -v $(pwd)/in:/incoming -v $(pwd)/out:/outgoing  \
        fnndsc/pl-z2labelmap z2labelmap.py                      \
        --negColor B --posColor R                               \
        --imageSet ../data/set1                                 \
        /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).

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).

Using the above referenced z-score file, this results in:

0       Unknown                         0   0   0   0
11101       lh-G_and_S_frontomargin         0       0       0       0
11102       lh-G_and_S_occipital_inf        0       0       0       0
11103       lh-G_and_S_paracentral          0       0       0       0
11104       lh-G_and_S_subcentral           103     0       0       0
11105       lh-G_and_S_transv_frontopol     0       0       0       0
11106       lh-G_and_S_cingul-Ant           0       0       110     0
11107       lh-G_and_S_cingul-Mid-Ant       0       0       0       0
11108       lh-G_and_S_cingul-Mid-Post      0       0       111     0
                            ...
                            ...
12167       rh-S_postcentral                0       0       0       0
12168       rh-S_precentral-inf-part        0       0       0       0
12169       rh-S_precentral-sup-part        0       0       0       0
12170       rh-S_suborbital                 0       0       110     0
12171       rh-S_subparietal                0       0       103     0
12172       rh-S_temporal_inf               0       0       0       0
12173       rh-S_temporal_sup               119     0       0       0
12174       rh-S_temporal_transverse        0       0       0       0

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] [--seed <seed>]
If specified, generate a z-score file based on <posRange> and <negRange>.  In addition, if a further optional <seed> is passed, then initialize the random generator with that seed, otherwise system time is used.

[-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.

[--imageSet <imageSetDirectory>]
If specified, will copy the (container) prepopulated image set in <imageSetDirectory> to the output directory.

[-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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

z2labelmap-2.2.0.tar.gz (13.9 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page