Difference between revisions of "Walkthrough on PCA through the command line"

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[[PCA]] computations through the command line are governed through ''PCA workflow'' objects. We describe here how to create and handle them:
[[PCA]] computations through the command line are governed through ''PCA workflow'' objects. We describe here how to create and handle them:
= Creation of a synthetic data set=
= Creation of a synthetic data set=

Revision as of 15:32, 3 April 2020

PCA computations through the command line are governed through PCA workflow objects. We describe here how to create and handle them:

Select files (or drop them here)...

    Creation of a synthetic data set

    dtutorial ttest128 -M 64 -N 64 -linear_tags 1 -tight 1

    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

    Input elements

    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.


    dataFolder = 'ttest128/data';


    tableFile  = 'ttest128/real.tbl';


    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

    name = 'classtest128';

    Now we are ready to create the workflow:

     wb = dpkpca.new(name,'t',tableFile,'d',dataFolder,'m',mask);

    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.

    Mathematical parameters

    The main parameters that can be chosen in this area are:

    • bandpass
    • symmetry
    • binning level (to accelerate the computations)

    Computational parameters

    The main burden of the PCA computation is the creation of the cross correlation matrix.

    Computing device

    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.



    Correlation matrix

    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 reduction

    TSNE remaps the particles into 2D maps which can be visualised and operated interactively.



    Computed elements have been stored in the workflow folder. Some of them () can be directly access through workflow tools.

    Correlation matrix


    figure;dshow(cmm);h=gca();h.YDir = 'reverse';


    Series of plots

    To check all the eigencomponents, it is a good idea to do some scripting. The script below uses a handy Dynamo trick to create several plots in the same figure.

     gui = mbgraph.montage();
    for i=1:10
        % gui.gca captures the

    Series of histograms



    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. We see that in this particular experiment, eigencomponent 3 seems to have been "corrupted by the missing wedge"

    TSNE reduction

    general tool for exploring tables
    scatter tab tuned to show cobehaviour of first two eigencomponents
    scatterplot eigencomponents
    right click to get the options to manually select particles through a lasso tool
    lasso particles
    right click on the lasso to get options on that subset of particles
    first eigencomponent of each particle
    sliding montage on the distribution of several eigencomponent
    sliding montage of sets of eigencomponent
    eigenvolumes after nomrmalization
    correlation of eigencomponens with tilt
    right click on each point to access the particle
    tsne clustering
    right click on the axes for an automated clustering
    automated clustering
    right click on the axes to select a set of particles
    use the lasso tool to select a group
    lassoed particles can be averaged together
    average of particles inside lasso
    a second lasso can be created
    average all manually selected clusters
    dmapview on opening
    showing several volumes in dmapview
    correspondig slices of
    you can select a single slice for depiction
    use keys 1 and 2 to set anchors
    right click one anchor to show an intensity profile
    intensity profile