X-Informatics

The new Cytoscape 3 is based on OSGI, which I barely know anything about. Even though Cytoscape 3 is still in beta, manuals, wikies and tutorials have been flowing around in the web, which could be overwhelming if you want to create a new C3 compatible bundle app. Today I tried to set up an Cytoscape 3 bundle App (aka Plugin in the Cytoscape 2.x world) development environment in Eclipse. To my surprise, it is without any hassle by following a tutorial and a wiki page.

First, by following this tutorial: Create a Bundle App Using IDE , it’s really straightforward to create the app with Maven and Eclipse (M2Eclipse is required). After everything is set up, I made sure that the app was installed by running “mvn install” (either in the app directory from command line or within eclipse) and checked my local maven repository ~/.m2/repository/groupid/architectid (yes I am using Linux).

Then I followed this wiki page: Interactive Shell , and deployed the app and tested it.

First download cytoscape 3:

> wget http://chianti.ucsd.edu/cytoscape-3.0.0-M4/cytoscape-unix-3.0.0-M4.tar.gz
> tar xzf cytoscape-unix-3.0.0-M4.tar.gz
> cd cytoscape-unix-3.0.0-M4
> ./cytoscape.sh # this will start cytoscape shell and the GUI
Cytoscape 3.0.0-M4> install mvn:groupid/artifactid
Bundle ID: 162
Cytoscape 3.0.0-M4> start 162

Now go to the Cytoscape GUI, and open Apps menu, and there it is: “Hello World!”

Here is an example of how to create a edge-labeled graph using igraph.

> a  
a
[,1] [,2] [,3]
[1,] 0 2 1
[2,] 2 0 3
[3,] 1 3 0
> g1  
g1
Vertices: 3
Edges: 6
Directed: TRUE
Edges:

[0] 0 -> 1
[1] 0 -> 2
[2] 1 -> 0
[3] 1 -> 2
[4] 2 -> 0
[5] 2 -> 1
> E(g1)$weight
[1] 2 1 2 3 1 3
> E(g1)
Edge sequence:

[0] 0 -> 1
[1] 0 -> 2
[2] 1 -> 0
[3] 1 -> 2
[4] 2 -> 0
[5] 2 -> 1
> get.adjacency(g1, attr="weight")
[,1] [,2] [,3]
[1,] 0 2 1
[2,] 2 0 3
[3,] 1 3 0

There are two easy ways to add noise, by scale the original data, or by mask some noise on the data.
First for a simple function , the following matlab code add 10% noise to it.

N = 100;
x = linspace(-pi, pi, N);
y = sin(x);
plot(x, y, 'r');
hold on;

% add 10% noise based on gaussian
scale = 0.1;
n1 = randn(1, N); % noise with mean=0 and std=1;
y1 = y + n1.*y*scale;
plot(x, y1, 'g');

% mask signal with noise
n2 = 0.1*randn(1,N)*sqrt(max(abs(y))); % noise with mean=0 and %std=max(amplitude);
y2 = y + n2;
plot(x, y2, 'b');

% Of course we can combine the two
y3 = y1 + n2;
plot(x, y3, 'm');

The final result looks like this:

We can also try to add noise to a more complicated synthetic data. For example, the famous swiss roll data^[1] in manifold learning. First, we can generate the dataset by this function:




Plot a scatter plot of will give us a swiss roll dataset. For example, the following matlab code will create this figure.

N = 500;
r = linspace(0,1,N);
t = (3*pi/2)*(1+2*r);
x = t.*cos(t);
y = t.*sin(t);
z = 20*rand(1,N);
scatter3(x, y, z, 12, t, 'filled');

Now after adding noise. the standard deviation of the noise is 2% of smallest dimension of the bounding box enclosing the data (as discussed in ^[2])

mindim = min(max(y)-min(y), max(x)-min(x));
x = x+0.02*randn(1,N)*sqrt(mindim);
y = y+0.02*randn(1,N)*sqrt(mindim);
scatter3(x, y, z, 12, t, 'filled');

1. Tenenbaum, J.B., Silva, V.D. & Langford, J.C. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319-2323 (2000).
2. Balasubramanian, M., Schwartz, E.L., Tenenbaum, J.B., de Silva, V. & Langford, J.C. The Isomap Algorithm and Topological Stability. Science 295, 7a (2002).