Walkthrough on PCA through the command line
PCA computations through the command line are governed through PCA workflow objects. We describe here how to create and handle them:
- 1 Creation of a synthetic data set
- 2 Creation of a workflow
- 3 Running
- 4 Visualization
Creation of a synthetic data set
This generates a set of 128 particles where 64 are slightly closer than the other 64. The particle subtomogram are randomly oriented, but the alignment parameters are known.
Creation of a workflow
The input of a PCA workflow are:
- a set of particles (called data container in this article)
- a table that expreses the alignment
- a mask that indicates the area of each alignment particle that will be taken into account during the classification procedure.
We create a cylindrical mask with the dimensions of the particles (40 pixels) mask = dcylinder([20,20],40);
We decide a name for the workflow itself, for instance
Now we are ready to create the workflow:
This creates an workflow object (arbitrarily called wb in the workspace during the current session). It also creates a folder called classtest128.PCA where results will be stored as they are produced.
The main parameters that can be chosen in this area are:
- binning level (to accelerate the computations)
The main burden of the PCA computation is the creation of the cross correlation matrix.
PCA computations can be run on GPUs of on CPUs, in both cases in parallel.
Size of parallel blocks
In this workflow we run the steps one by one to discuss them. In real workflows, you can use the run methods to just launch all steps sequentially.
All pairs of correlations are computed in blocks, as described above
The correlation matrix is diagonalised. The eigenvectors are used to expressed as the particles as combinations of weights.
These weights are ordered in descending order relative to their impact on the variance of the set, ideally a particle should be represented by its few components on this basis. The weights are stored in a regula Dynamo table. First eigencomponent of a particle goes into column 41.
The eigenvectors are expressed as three=dimensional volumes.
TSNE remaps the particles into 2D maps which can be visualised and operated interactively.
Correlation of tilts
It is a good idea to check if some eigenvolumes correlate strongly with the tilt.
In this plot, each point represents a particle in your data set.