Fast principal component analysis for cryo-electron microscopy images
Principal component analysis (PCA) plays an important role in the analysis of cryo-electron microscopy (cryo-EM) images for various tasks such as classification, denoising, compression, and ab initio modeling. We introduce a fast method for estimating a compressed representation of the 2-D covarianc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2023-01-01
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| Series: | Biological Imaging |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2633903X23000028/type/journal_article |
| _version_ | 1797687230903353344 |
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| author | Nicholas F. Marshall Oscar Mickelin Yunpeng Shi Amit Singer |
| author_facet | Nicholas F. Marshall Oscar Mickelin Yunpeng Shi Amit Singer |
| author_sort | Nicholas F. Marshall |
| collection | DOAJ |
| description | Principal component analysis (PCA) plays an important role in the analysis of cryo-electron microscopy (cryo-EM) images for various tasks such as classification, denoising, compression, and ab initio modeling. We introduce a fast method for estimating a compressed representation of the 2-D covariance matrix of noisy cryo-EM projection images affected by radial point spread functions that enables fast PCA computation. Our method is based on a new algorithm for expanding images in the Fourier–Bessel basis (the harmonics on the disk), which provides a convenient way to handle the effect of the contrast transfer functions. For
$ N $
images of size
$ L\times L $
, our method has time complexity
$ O\left({NL}^3+{L}^4\right) $
and space complexity
$ O\left({NL}^2+{L}^3\right) $
. In contrast to previous work, these complexities are independent of the number of different contrast transfer functions of the images. We demonstrate our approach on synthetic and experimental data and show acceleration by factors of up to two orders of magnitude. |
| first_indexed | 2024-03-12T01:16:15Z |
| format | Article |
| id | doaj.art-09f0de9c54054f7986d452b7dfee879b |
| institution | Directory Open Access Journal |
| issn | 2633-903X |
| language | English |
| last_indexed | 2024-03-12T01:16:15Z |
| publishDate | 2023-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Biological Imaging |
| spelling | doaj.art-09f0de9c54054f7986d452b7dfee879b2023-09-13T12:00:43ZengCambridge University PressBiological Imaging2633-903X2023-01-01310.1017/S2633903X23000028Fast principal component analysis for cryo-electron microscopy imagesNicholas F. Marshall0Oscar Mickelin1https://orcid.org/0000-0003-0167-1992Yunpeng Shi2https://orcid.org/0000-0003-2388-2766Amit Singer3Department of Mathematics, Oregon State University, Corvallis, Oregon 97331, USAProgram in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USAProgram in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USAProgram in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA Department of Mathematics, Princeton University, Princeton, New Jersey 08544, USAPrincipal component analysis (PCA) plays an important role in the analysis of cryo-electron microscopy (cryo-EM) images for various tasks such as classification, denoising, compression, and ab initio modeling. We introduce a fast method for estimating a compressed representation of the 2-D covariance matrix of noisy cryo-EM projection images affected by radial point spread functions that enables fast PCA computation. Our method is based on a new algorithm for expanding images in the Fourier–Bessel basis (the harmonics on the disk), which provides a convenient way to handle the effect of the contrast transfer functions. For $ N $ images of size $ L\times L $ , our method has time complexity $ O\left({NL}^3+{L}^4\right) $ and space complexity $ O\left({NL}^2+{L}^3\right) $ . In contrast to previous work, these complexities are independent of the number of different contrast transfer functions of the images. We demonstrate our approach on synthetic and experimental data and show acceleration by factors of up to two orders of magnitude.https://www.cambridge.org/core/product/identifier/S2633903X23000028/type/journal_articleCovariance estimationcryo-EMdenoisingFourier–Besselprincipal component analysissingle particle reconstruction |
| spellingShingle | Nicholas F. Marshall Oscar Mickelin Yunpeng Shi Amit Singer Fast principal component analysis for cryo-electron microscopy images Biological Imaging Covariance estimation cryo-EM denoising Fourier–Bessel principal component analysis single particle reconstruction |
| title | Fast principal component analysis for cryo-electron microscopy images |
| title_full | Fast principal component analysis for cryo-electron microscopy images |
| title_fullStr | Fast principal component analysis for cryo-electron microscopy images |
| title_full_unstemmed | Fast principal component analysis for cryo-electron microscopy images |
| title_short | Fast principal component analysis for cryo-electron microscopy images |
| title_sort | fast principal component analysis for cryo electron microscopy images |
| topic | Covariance estimation cryo-EM denoising Fourier–Bessel principal component analysis single particle reconstruction |
| url | https://www.cambridge.org/core/product/identifier/S2633903X23000028/type/journal_article |
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