This is the documentation associated with each demo:

demo2D00

Synthesize one 2D image from all object models. Results will be one TIFF file, with six slices, one each for cell boundary, nuclear boundary, nucleoli, mitochondria, lysosomes, and endosomes

demo2D01

Trains a 2D generative model of protein location using the 2D HeLa dataset provided
by the Murphy Lab at http://murphylab.web.cmu.edu/data/

demo2D02

Synthesize one 2D image from the LAMP2 model trained in demo2D01

demo2D03

Trains a 2D generative model of protein location using the 2D HeLa dataset provided
by the Murphy Lab at http://murphylab.web.cmu.edu/data/ while making
pretty plots

demo3D00

Synthesizes 1 image using a lysosomal model with sampling mode
set to 'disc' and no convolution.
Results will be three TIFF files, one each for cell boundary,
nuclear boundary, and lysosomes, in folder "synthesizedImages/cell1"

demo3D01

Synthesize one 3D image from all object models,
with sampling mode set to 'disc' and no convolution.
Results will be six TIFF files, one each for
cell boundary, nuclear boundary, nucleoli, mitochondria, lysosomes,
and endosomes, in folder "synthesizedImages/cell1"

demo3D02

Take results from demo3D00 in
folder "../demo3D00/synthesizedImages/cell1"
and generate surface plot

demo3D03

Synthesize one 3D image from all object models,
with sampling mode set to 'sampled' at a density of 75 and no convolution.
Results will be six TIFF files, one each for
cell boundary, nuclear boundary, nucleoli, mitochondria, lysosomes,
and endosomes, in folder "synthesizedImages/cell1"

demo3D04

Synthesize one 3D image from microtubule model and no convolution.
Results will be three TIFF files, one each for
cell boundary, nuclear boundary, and microtubules,
in folder "synthesizedImages/cell1"

demo3D05

Synthesize one 3D image from all object models and a microtubule model,
with sampling mode set to 'sampling' and no convolution.
Results will be seven TIFF files, one each for
cell boundary, nuclear boundary, nucleoli, mitochondria, lysosomes,
endosomes, and microtubules, in folder "synthesizedImages/cell1"

demo3D06

Synthesize one 3D image from all object models and a microtubule model,
with sampling mode set to 'disc' and convolution with point-spread function.
Results will be seven TIFF files, one each for
cell boundary, nuclear boundary, nucleoli, mitochondria, lysosomes,
endosomes, and microtubules, in folder "synthesizedImages/cell1"

demo3D07

Synthesize one 3D image from all object models and a microtubule model,
with sampling mode set to 'sampled' at a density of 25 and
convolution with point-spread function.
Results will be seven TIFF files, one each for
cell boundary, nuclear boundary, nucleoli, mitochondria, lysosomes,
endosomes, and microtubules, in folder "synthesizedImages/cell1"

demo3D08

Synthesize one 3D image from all object models and a microtubule model,
with sampling mode set to 'disc' without convolution.
Results will be an indexed image in a single TIFF file, with one index
each for cell boundary, nuclear boundary, nucleoli, mitochondria, lysosomes,
endosomes, and microtubules, in folder "synthesizedImages/cell1"

demo3D09

Synthesize one 3D image from a lysosome model,
with sampling mode set to 'disc' without convolution.
Results will be three TIFF files, one each for
cell boundary, nuclear boundary, and lysosomes
in folder "synthesizedImages/cell1"
Also produce a mean projection of the cell boundary in
XY, XZ and YZ directions and save it in file 'projection.tif'

demo3D10

Synthesize 1 instance using a lamp2 model with sampling mode
set to 'disc' and no convolution.
Results will be three .obj files, one each for
cell boundary, nuclear boundary, and lamp2,
in folder "synthesizedImages/cell1"

demo3D11

Trains a generative model of the cell framework using the four patterns in the 3D HeLa
dataset from the Murphy Lab

demo3D12

Trains a generative model of the framework using one of the four patterns in the HeLa
dataset

demo3D13

This demo show the usage of syn2blender, a helper method that takes a
folder of synthesized images and exports the images as object files
that can be imported in Blender. This demo uses the images in demo3D03

demo3D14

This demo show the usage of syn2projection, a helper method that makes
projection using a folder of synthesized images

demo3D15

Synthesizes 1 image using a transferrin model for the protein and a diffeomorphic model
for the nuclear and cell shape
Results will be three TIFF files, one each for cell boundary,
nuclear boundary, and protein, in folder "synthesizedImages/cell1"

demo3D16

This method shows how to preprocess raw images to use as input for
CellOrganizer. The main idea behind this demo is to show the user they
can use their own binary images from raw experimental data they can use
to synthesize protein patterns. The current demo assumes the resolution
of the images is the same as the images that were used to train the
protein model

This method shows how to input an image to CellOrganizer.
The main idea behind this demo is to show the user they
can use their own binary images from raw experimental data. They can use
them to synthesize protein patterns. The current demo assumes the resolution
of the images is the same as the images that were used to train the
protein model. This demo uses the framework synthesized from demo3D15. In
this case, the resolution at which the diffeomorphic and vesicle model were
trained on are different. This demo also shows how to handle that situation
in CellOrganizer

demo3D18

Trains a generative model of the framework using the holefinding
functionality

demo3D19

This method shows the use of slml2report for creating comparisons between
parameters of CellOrganzier models.

demo3D20

Trains a generative model of the framework using one diffeomorphic model

demo3D21

Trains a generative model of the framework using the holefinding
functionality. The same demo as demo3D18 but with no scaling of the
images.

demo3D22

Synthesizes a protein pattern instance for each of the synthetic images
from demo3DDiffeoSynth

demo3DMultiresSynth

Synthesize multiple 3D images from a lysosome model,
at different resolutions

demo3DObjectAvoidance

Synthesizes 1 image using a lysosomal model with sampling mode
set to 'disc', no convolution using the object avoidance methods
Results will be three TIFF files, one each for cell boundary,
nuclear boundary, and lysosomes, in folder "synthesizedImages/cell1"

demo3DPrimitives

Synthesizes 1 image using a lysosomal model with sampling mode
set to 'disc', no convolution and output.SBML set to true
Results will be three TIFF files, one each for cell boundary,
nuclear boundary, and lysosomes, in folder "synthesizedImages/cell1"
Additionally, in the folder "synthesizedImages/" will be a
SBML-Spatial(v0.82a) formatted .xml file containing constructed solid
geometry(CSG) primitives for lysosomes and parametric objects for the
cell and nuclear shapes.
These files can then be read into VCell using the built in importer or
CellBlender using the helper function provided in this distribution.

Contents of demo3DDiffeoSynth:

demo3DDiffeoPick               - function [ output_args ] = demo3DDiffeoSynth( input_args )
demo3DDiffeoSynth              - This demo illustrates different ways to sample from points in a
demo3DDiffeoSynth_gmm          - function [ output_args ] = demo3DDiffeoSynth( input_args )
demo3DDiffeoSynth_grid         - function [ output_args ] = demo3DDiffeoSynth( input_args )
demo3DDiffeoSynth_grid_pick    - function [ output_args ] = demo3DDiffeoSynth( input_args )
demo3DDiffeoSynth_uniform      - function [ output_args ] = demo3DDiffeoSynth( input_args )


demo3DDiffeoSynth is both a directory and a function.

This demo illustrates different ways to sample from points in a
diffeomorphic model.

Contents of demo3DDiffeomorphicWindowSize:

demo3DDiffeomorphicWindowSize  - This demo exists to illustrate how padding size and window size effect the


demo3DDiffeomorphicWindowSize is both a directory and a function.

This demo exists to illustrate how padding size and window size effect the
performance of diffeomorphic metric. Currently incomplete GRJ 4/16/14

todo: output results

This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published
by the Free Software Foundation; either version 2 of the License,
or (at your option) any later version.

This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
02110-1301, USA.

For additional information visit http://murphylab.web.cmu.edu or
send email to murphy@cmu.edu

Contents of demo3DDynamic:

demo3DDynamic                  -


demo3DDynamic is both a directory and a function.

demo3DDynamic

Learns a random walk from time series images as in figure 6 and 7 of
Johnson 2015

Contents of demo3DSBML:

demo3DSBML                     - This demo converts a sample SBML file to an SBML-spatial instance using
makeCSGOnly                    - This function makes the SBML-spatial standard 'CSGOnly.xml' file.
makeMeshOnly                   - This function makes the SBML-spatial standard 'MeshOnly.xml' file.
makeMixedOnly                  - This function makes the SBML-spatial standard 'MixedOnly.xml' file.


demo3DSBML is both a directory and a function.

This demo converts a sample SBML file to an SBML-spatial instance using
the "matchSBML" function. This function takes an SBML file, matches the
compartments in the file with available models and synthesizes the
appropriate instances

This demo is to demonstrate the function that reads an image and creates a
3D mesh using the SBML-spatial paradigm.