How many (distinguishable) classes can we identify in
single-particle analysis?

J 2025

How many (distinguishable) classes can we identify in single-particle analysis?

LAUZIRIKA, Oier; Martin PERNICA; David HERREROS; Erney RAMÍREZ-APORTELA; James KRIEGER et al.

Základní údaje

Originální název

How many (distinguishable) classes can we identify in single-particle analysis?

Autoři

Vydání

ACTA CRYSTALLOGRAPHICA SECTION D, 2025, 2059-7983

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.800 v roce 2024

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14610/25:00142380

Organizační jednotka

Ústav výpočetní techniky

DOI

https://doi.org/10.1107/S2059798325007831

UT WoS

001589115300002

Klíčová slova anglicky

cryo-electron microscopy; 3D classification; structural heterogeneity; statistical significance; reproducibility analysis

Štítky

J-Q1, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 9. 12. 2025 16:14, doc. RNDr. Jiří Filipovič, Ph.D.

Anotace

V originále

Heterogeneity in cryoEM is essential for capturing the structural variability of macromolecules, reflecting their functional states and biological significance. However, estimating heterogeneity remains challenging due to particle mis{\-}classification and algorithmic biases, which can lead to reconstructions that blend distinct conformations or fail to resolve subtle differences. Furthermore, the low signal-to-noise ratio inherent in cryo-EM data makes it nearly impossible to detect minute structural changes, as noise often obscures subtle variations in macromolecular projections. In this paper, we investigate the use of {\it p}-values associated with the null hypothesis that the observed classification differs from a random partition of the input data set, thereby providing a statistical framework for determining the number of distinguishable classes present in a given data set.

Návaznosti

LM2023054, projekt VaV

Název: e-Infrastruktura CZ

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, e-Infrastruktura CZ

Citovat

LAUZIRIKA, Oier; Martin PERNICA; David HERREROS; Erney RAMÍREZ-APORTELA; James KRIEGER; Marcos GRAGERA; Mikel ICETA; Pablo CONESA; Yunior FONSECA; Jorge JIMÉNEZ; Jiří FILIPOVIČ; Jose Maria CARAZO a Carlos Oscar SORZANO. How many (distinguishable) classes can we identify in single-particle analysis? ACTA CRYSTALLOGRAPHICA SECTION D. 2025, roč. 81, č. 10, s. 535-544. ISSN 2059-7983. Dostupné z: https://doi.org/10.1107/S2059798325007831.

@article{2528155,
   author = {Lauzirika, Oier and Pernica, Martin and Herreros, David and RamírezandAportela, Erney and Krieger, James and Gragera, Marcos and Iceta, Mikel and Conesa, Pablo and Fonseca, Yunior and Jiménez, Jorge and Filipovič, Jiří and Carazo, Jose Maria and Sorzano, Carlos Oscar},
   article_number = {10},
   doi = {https://doi.org/10.1107/S2059798325007831},
   keywords = {cryo-electron microscopy; 3D classification; structural heterogeneity; statistical significance; reproducibility analysis},
   language = {eng},
   issn = {2059-7983},
   journal = {ACTA CRYSTALLOGRAPHICA SECTION D},
   title = {How many (distinguishable) classes can we identify in single-particle analysis?},
   url = {https://doi.org/10.1107/S2059798325007831},
   volume = {81},
   year = {2025}
}

TY  - JOUR
ID  - 2528155
AU  - Lauzirika, Oier - Pernica, Martin - Herreros, David - Ramírez-Aportela, Erney - Krieger, James - Gragera, Marcos - Iceta, Mikel - Conesa, Pablo - Fonseca, Yunior - Jiménez, Jorge - Filipovič, Jiří - Carazo, Jose Maria - Sorzano, Carlos Oscar
PY  - 2025
TI  - How many (distinguishable) classes can we identify in single-particle analysis?
JF  - ACTA CRYSTALLOGRAPHICA SECTION D
VL  - 81
IS  - 10
SP  - 535-544
EP  - 535-544
SN  - 20597983
KW  - cryo-electron microscopy
KW  - 3D classification
KW  - structural heterogeneity
KW  - statistical significance
KW  - reproducibility analysis
UR  - https://doi.org/10.1107/S2059798325007831
N2  - Heterogeneity in cryoEM is essential for capturing the structural variability of macromolecules, reflecting their functional states and biological significance. However, estimating heterogeneity remains challenging due to particle mis{\-}classification and algorithmic biases, which can lead to reconstructions that blend distinct conformations or fail to resolve subtle differences. Furthermore, the low signal-to-noise ratio inherent in cryo-EM data makes it nearly impossible to detect minute structural changes, as noise often obscures subtle variations in macromolecular projections. In this paper, we investigate the use of {\it p}-values associated with the null hypothesis that the observed classification differs from a random partition of the input data set, thereby providing a statistical framework for determining the number of distinguishable classes present in a given data set.
ER  -

LAUZIRIKA, Oier; Martin PERNICA; David HERREROS; Erney RAMÍREZ-APORTELA; James KRIEGER; Marcos GRAGERA; Mikel ICETA; Pablo CONESA; Yunior FONSECA; Jorge JIMÉNEZ; Jiří FILIPOVIČ; Jose Maria CARAZO a Carlos Oscar SORZANO. How many (distinguishable) classes can we identify in single-particle analysis? \textit{ACTA CRYSTALLOGRAPHICA SECTION D}. 2025, roč.~81, č.~10, s.~535-544. ISSN~2059-7983. Dostupné z: https://doi.org/10.1107/S2059798325007831.

Přehled o publikaci