Detailed Information on Publication Record
2022
On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy
SORZANO, Carlos, Amaya JIMÉNEZ-MORENO, David MALUENDA, Marta MARTÍNEZ, Erney RAMÍREZ-APORTELA et. al.Basic information
Original name
On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy
Authors
SORZANO, Carlos, Amaya JIMÉNEZ-MORENO, David MALUENDA, Marta MARTÍNEZ, Erney RAMÍREZ-APORTELA, James KRIEGER, Roberto MELERO, Ana CUERVO, Javier CONESA, Jiří FILIPOVIČ (203 Czech Republic, belonging to the institution), Pablo CONESA, Laura del CAÑO, Yunior FONSECA, Jorge Jiménez-de LA MORENA, Patricia LOSANA, Ruben SÁNCHEZ-GARCÍA, David STŘELÁK (203 Czech Republic, guarantor, belonging to the institution), Estrella FERNÁNDEZ-GIMÉNEZ, Federico DE ISIDRO-GÓMEZ, David HERREROS, Jose Luis VILAS, Roberto MARABINI and Jose Maria CARAZO
Edition
Acta Crystallographica Section D: Structural Biology, Chester, International Union of Crystallography, 2022, 2059-7983
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United Kingdom of Great Britain and Northern Ireland
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 2.200
RIV identification code
RIV/00216224:14330/22:00125553
Organization unit
Faculty of Informatics
UT WoS
000777860500002
Keywords in English
single-particle analysis; cryo-electron microscopy; parameter estimation; image processing; bias; variance; overfitting; gold standard
Tags
Tags
International impact, Reviewed
Změněno: 28/3/2023 10:10, RNDr. Pavel Šmerk, Ph.D.
Abstract
V originále
Cryo-electron microscopy (cryoEM) has become a well established technique to elucidate the 3D structures of biological macromolecules. Projection images from thousands of macromolecules that are assumed to be structurally identical are combined into a single 3D map representing the Coulomb potential of the macromolecule under study. This article discusses possible caveats along the image-processing path and how to avoid them to obtain a reliable 3D structure. Some of these problems are very well known in the community. These may be referred to as sample-related (such as specimen denaturation at interfaces or non-uniform projection geometry leading to underrepresented projection directions). The rest are related to the algorithms used. While some have been discussed in depth in the literature, such as the use of an incorrect initial volume, others have received much less attention. However, they are fundamental in any data-analysis approach. Chiefly among them, instabilities in estimating many of the key parameters that are required for a correct 3D reconstruction that occur all along the processing workflow are referred to, which may significantly affect the reliability of the whole process. In the field, the term overfitting has been coined to refer to some particular kinds of artifacts. It is argued that overfitting is a statistical bias in key parameter-estimation steps in the 3D reconstruction process, including intrinsic algorithmic bias. It is also shown that common tools (Fourier shell correlation) and strategies (gold standard) that are normally used to detect or prevent overfitting do not fully protect against it. Alternatively, it is proposed that detecting the bias that leads to overfitting is much easier when addressed at the level of parameter estimation, rather than detecting it once the particle images have been combined into a 3D map. Comparing the results from multiple algorithms (or at least, independent executions of the same algorithm) can detect parameter bias. These multiple executions could then be averaged to give a lower variance estimate of the underlying parameters.
Links
| EF16_013/0001802, research and development project |
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| MUNI/A/1145/2021, interní kód MU |
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