xmipp3.protocols.protocol_compute_likelihood module
- class xmipp3.protocols.protocol_compute_likelihood.XmippProtComputeLikelihood(**args)[source]
Bases:
ProtAnalysis3DThis protocol computes the log likelihood or correlation of a set of particles with assigned angles when compared to a set of maps or atomic models
AI Generated:
Overview
- The Log Likelihood protocol evaluates how well experimental cryo-EM particle
images agree with one or more reference structures. These references can be 3D volumes obtained from previous reconstructions or sets of volumes representing different structural states. For each particle, the protocol computes a statistical measure—typically a log likelihood score—that quantifies how consistent the particle image is with the projections of each reference.
In practice, this protocol answers a biologically meaningful question:
Given the current particle orientations, which structural model best explains each particle image?
This type of analysis is useful in several situations. It can help compare particles against multiple candidate structures, quantify the quality of particle assignments, or evaluate how well experimental data support a given model. The protocol can also automatically group particles according to the reference that best explains them, providing a simple form of likelihood-based classification.
Because the protocol relies on already assigned particle orientations, it is typically applied after alignment or refinement steps in the cryo-EM workflow.
Inputs and General Workflow
The protocol requires two main inputs:
Input particles
These must be particle images with assigned projection angles. In other words, each particle should already have orientation parameters describing how it relates to a 3D structure. These orientations usually come from previous reconstruction or refinement steps.
Reference volumes
The user can provide either: - a single volume, or - a set of volumes representing different structural hypotheses or conformational states.
For each particle, the protocol generates projections of the reference volume(s) using the particle’s assigned orientation and compares them with the experimental image.
The comparison produces a log likelihood score, reflecting how probable the experimental image is given the reference projection and the estimated noise. When multiple references are provided, the protocol compares each particle against all references and determines which one best explains the data.
Particle and Noise Regions
A key concept in this protocol is the separation between particle signal and background noise. The algorithm estimates the noise statistics from a region surrounding the particle and uses this information when computing the likelihood score.
Two parameters control this separation.
Particle radius. This defines the circular region that contains the particle signal. Ideally, this radius should include the full particle but avoid large solvent regions. Choosing a value that is too large may dilute the signal, while a value that is too small may exclude important structural features.
If this parameter is left at the default value, the protocol assumes that the particle occupies roughly half the image width.
Noise radius. This defines the outer radius used to estimate background noise. The region between the particle radius and the noise radius forms a ring where the algorithm measures noise statistics.
In practice:
The particle radius should cover the particle.
The noise radius should extend slightly beyond the particle.
If the noise radius is not specified, the protocol automatically uses the outer image region.
Correct estimation of noise is important because it strongly affects the likelihood calculation.
Reference Projections and Residual Images
For each particle and each reference volume, the protocol generates a projection of the reference using the particle’s assigned orientation. This projection is then compared with the experimental image.
The difference between the projection and the experimental particle image is called the residual image. Residuals contain information about: - noise in the experimental image - model inaccuracies - structural differences between particle and reference
These residuals are used internally to estimate the likelihood score and can optionally be stored for further analysis.
From a biological perspective, large residuals often indicate that the particle is poorly explained by the reference model.
Gray Level Optimization
Experimental particle images and simulated projections may differ in overall intensity scale. The protocol therefore includes an option to optimize the gray scale factor between the projection and the experimental image.
When enabled, the algorithm adjusts the intensity scale to maximize the agreement between the two images. This step improves robustness when image normalization or detector scaling differs across datasets.
A parameter called maximum gray change limits how much the scale can vary. Restricting this range prevents unrealistic intensity adjustments that could artificially inflate the likelihood score.
In most practical cases, enabling gray optimization improves stability and is recommended.
Normalization Options
The protocol optionally performs intensity normalization of both particles and reference projections. Normalization ensures that the likelihood calculation is not biased by global intensity differences.
Three normalization strategies are available:
Old Xmipp normalization
The entire image is normalized so that the mean intensity becomes zero and the standard deviation becomes one.
New Xmipp normalization
Normalization is performed using only the background region of the image. This approach better preserves the particle signal and is generally preferred for cryo-EM analysis.
Ramp normalization
This method subtracts background gradients before applying normalization. It is particularly useful when micrographs contain slow intensity variations or illumination gradients.
For most cryo-EM workflows, the newer normalization approaches provide more reliable statistical behavior.
CTF Considerations
The protocol can optionally ignore the Contrast Transfer Function (CTF) during the comparison between particles and projections.
In general, CTF effects should be considered during likelihood calculations because they influence the appearance of particle images. However, this option becomes useful when images have already been CTF-corrected using Wiener filtering or similar procedures.
If the dataset has already undergone strong CTF correction, disabling CTF application can avoid redundant processing.
Outputs and Their Interpretation
After execution, the protocol produces several outputs.
Particle set with likelihood values
Each particle receives a log likelihood score describing how well it matches each reference. These values can be used to evaluate particle quality or to study the consistency between particles and structural models.
Residual images (optional)
Residual images represent the difference between experimental particles and reference projections. Inspecting residuals can reveal systematic mismatches, noise patterns, or structural variability.
Likelihood matrix
Internally, the protocol constructs a matrix containing the likelihood of every particle with respect to every reference. This matrix provides a quantitative view of how strongly each particle supports each structural hypothesis.
3D classes based on likelihood
When multiple references are provided, the protocol assigns each particle to the reference with the highest likelihood score. The resulting grouping is stored as a set of 3D classes, where each class corresponds to one reference volume.
Biologically, this allows users to see which particles are most compatible with each structural state.
Practical Recommendations
- In most workflows, this protocol is applied after particle orientations
have been determined, typically following a refinement or reconstruction step.
A common use case is to compare particles against several candidate models representing different conformations or processing strategies. In this situation, the likelihood scores provide a quantitative way to determine which model best explains the experimental data.
When selecting the particle radius, it is usually better to slightly overestimate the particle size rather than risk excluding relevant structural features. However, the noise ring should still contain a reasonable background region for estimating noise variance.
If the dataset has inconsistent intensity scaling, enabling gray optimization usually improves the reliability of the results.
When analyzing heterogeneous datasets, the likelihood-based classification produced by the protocol can serve as an initial indicator of structural variability. However, it should not replace dedicated classification methods, which are typically more sensitive to subtle conformational differences.
Final Perspective
Likelihood evaluation provides a statistically grounded way to connect experimental particle images with structural models. Rather than relying solely on visual inspection or correlation scores, the log likelihood measures how probable each particle image is under a given structural hypothesis.
For cryo-EM users, this protocol offers a powerful tool for model validation, particle quality assessment, and reference comparison, helping ensure that biological interpretations are supported by the underlying experimental data.
- NORM_NEW = 1
- NORM_OLD = 0
- NORM_RAMP = 2
- stepsExecutionMode = 1