.. Doppio Tomography Tutorial documentation master file, created by sphinx-quickstart on Tue Feb 18 13:09:38 2025. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. 3D classification of particles =========================================== The duplicate removal job that we ran in the last step cannot remove all the bad particles. During picking, we approximated HIV capsids as spheres but they are really not. Also, some capsids are not complete. By approximating them to spheres and picking by sphere sampling, we include both true particles as well as some junk particles. Before we try high resolution refinement at bin 1 level, we need to remove those junk particles to achieve a good resolution. We do this cleaning up by running a 3D classification job on our previously refined and duplicate-removed particles. To do this in Doppio, go to :purple:`NEW JOB` tab in Doppio GUI and type **relion.class3d.tomo** in the search bar. Then select the class3d job and enter inpust parameters. Our input parameters are given in the image below: .. figure:: ../_static/images/STA/class3d-job-params1.png :alt: Example image :width: 100% :align: center .. figure:: ../_static/images/STA/class3d-job-params2.png :alt: Example image :width: 100% :align: center .. figure:: ../_static/images/STA/class3d-job-params3.png :alt: Example image :width: 100% :align: center Note that in our classification we've lowpass filtered the reference to 60 A to avoid (minimise) the reference bias, as well as the symmetry was chose to C1 because junk particles may not necessarily have C6 symmetry. We encourage you to read the RELION documentation for details. `Relion 3D classification documentation `_. Results -------- 3D classification results can be found in ``Class3D/job015/`` directory. We have asked for 30 iterations, the class distribution scores (probabilities) at the end of the 30 cycles are plotted below. You will find them in the :purple:`RESULTS` tab. .. Note:: If you want to look at intermediate results as the classification progresses in Doppio, you can do this by clicking on the (...) of the job button and selecting the *Recalculate results* option. .. figure:: ../_static/images/STA/class3d.png :alt: Example image :width: 100% :align: center Class001 and 002 are the two main classes came out at the end of classification. Out of these two class002 is the major with +0.55 probability. This class contains the most of the good particles, and the orthogonal slices of their average particle is shown in the top line of the right panel. The resolution of this class at iteration30 is 6.7 A as shown in the ``Class3D/job015/run_it030_model.star`` file. Below is a screenshot of the model file. .. figure:: ../_static/images/STA/class3d_it30_model.png :alt: Example image :width: 100% :align: center In this exercise, our main purpose of 3D classification was to obtain a particle set that are as homogenouse as possible, that will ultimately yeild us the best possible resolution. In your studies, the goals may be different. In the end, we dicided to move forward with class2 particles only, and there were 4630 particles. Selecting the class002 particles -------------------------------- We used the ccp-em pipeliner class selection routine implemented in Doppio for this. To call this functionality, got to :purple:`NEW JOB` tab in Doppio GUI and type **pipeliner.select.classes** in the search bar. *Classification optimiser file* is ``Class3D/job015/run_it030_optimiser.star`` and under the *Classes to select* type 2 and run the job. This will write out a new particle file. In our processing workflow it was ``Select/job017/particles.star``.