Data Item _em_classes.alignment_method

Browse:

General

Item name: _em_classes.alignment_method
Category name: em_classes
Attribute name: alignment_method
Required in PDB entries: no
Used in currrent PDB entries: No

Item Description

The alignment_method used

Data Type

Data type code: ucode
Data type detail: code item types/single words (case insensitive) ...
Primitive data type code: uchar
Regular expression: [_,.;:"&<>()/\{}'`~!@#$%A-Za-z0-9*|+-]*

Controlled Vocabulary

View/Hide Table

Allowed Value	Details
Alignment with the reference refinement	We assume that a set of particles in one orientation is available. Particles are not identical, but they share the same motif. begins with calculation of the global average to approximate the reference, then aligns all the images and calculates new average to obtain improved reference. These steps are iterated prescribed number of times.
Multireference alignment	We assume that a very large data set is available. It comprises particles in a few distinct orientations. The data set is sufficiently large that at least some of the similar views occur in similar in-plane orientations, and so can be averaged. Thus, if we can approximately center the particles, the subsequent classification step should reveal some of the classes. These classes are used as reference images in the next multireference alignment step, classification is repeated, and new classes are formed. This procedure is iterated until stable classes are obtained. Such a multireference alignment is sometimes called alignment through classification. This name reflects the idea that alignment is done separately within groups produced by the classification step. (a) - radius for alignment and mask -- should correspond to the particle radius; (b) - whether classification is done using all pixels within mask in the computation of Euclidean distance, or factors from Principal Component Analysis (PCA); (c) - if PCA is to be used, the number of factors has to be set; (d) - the number of groups into which the data set will be divided -- this determines the number of class averages that will be obtained; (e) - the number of times the procedure should be repeated.
Reference-based alignment	We assume that the reference image is known or that a good approximation of it is available. We expect all the particles to be noisy versions of the reference, with possible small variations. In this case the alignment problem becomes a pattern matching problem. We have to place every particle in an orientation in which it will best match the reference image. In the case of many reference images, in addition, we have to decide which reference is the most similar one. We must also try the mirror orientation since the particle may be flipped.
Reference-free alignment	The reference-free alignment Will seek such orientations of all the particles in the data set that all the possible pairs of images from this set are in the 'best' relative orientation as determined by the maximum of the CCF. The reference-free alignment programs were designed for very noisy data, for particles in many different orientations, and in general for cases in which a reference image is unknown or in which its usage could result in a bias and incorrect results.

Controlled Vocabulary at Deposition

View/Hide Table

Allowed Value	Details
Alignment with the reference refinement	We assume that a set of particles in one orientation is available. Particles are not identical, but they share the same motif. begins with calculation of the global average to approximate the reference, then aligns all the images and calculates new average to obtain improved reference. These steps are iterated prescribed number of times.
Multireference alignment	We assume that a very large data set is available. It comprises particles in a few distinct orientations. The data set is sufficiently large that at least some of the similar views occur in similar in-plane orientations, and so can be averaged. Thus, if we can approximately center the particles, the subsequent classification step should reveal some of the classes. These classes are used as reference images in the next multireference alignment step, classification is repeated, and new classes are formed. This procedure is iterated until stable classes are obtained. Such a multireference alignment is sometimes called alignment through classification. This name reflects the idea that alignment is done separately within groups produced by the classification step. (a) - radius for alignment and mask -- should correspond to the particle radius; (b) - whether classification is done using all pixels within mask in the computation of Euclidean distance, or factors from Principal Component Analysis (PCA); (c) - if PCA is to be used, the number of factors has to be set; (d) - the number of groups into which the data set will be divided -- this determines the number of class averages that will be obtained; (e) - the number of times the procedure should be repeated.
Reference-based alignment	We assume that the reference image is known or that a good approximation of it is available. We expect all the particles to be noisy versions of the reference, with possible small variations. In this case the alignment problem becomes a pattern matching problem. We have to place every particle in an orientation in which it will best match the reference image. In the case of many reference images, in addition, we have to decide which reference is the most similar one. We must also try the mirror orientation since the particle may be flipped.
Reference-free alignment	The reference-free alignment Will seek such orientations of all the particles in the data set that all the possible pairs of images from this set are in the 'best' relative orientation as determined by the maximum of the CCF. The reference-free alignment programs were designed for very noisy data, for particles in many different orientations, and in general for cases in which a reference image is unknown or in which its usage could result in a bias and incorrect results.