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Validation of 3D EM Reconstructions: The Phantom in the Noise.

Heymann JB - AIMS Biophys (2015)

Bottom Line: This poses the risk of inappropriate data processing with dubious results.How can we test that a map is a coherent structure present in the images selected from the micrographs?Adopting such a test can aid the microscopist in assessing the usefulness of the micrographs taken before committing to lengthy processing with questionable outcomes.

View Article: PubMed Central - HTML - PubMed

Affiliation: National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, 50 South Dr, Bethesda, MD 20892, USA.

ABSTRACT

Validation is a necessity to trust the structures solved by electron microscopy by single particle techniques. The impressive achievements in single particle reconstruction fuel its expansion beyond a small community of image processing experts. This poses the risk of inappropriate data processing with dubious results. Nowhere is it more clearly illustrated than in the recovery of a reference density map from pure noise aligned to that map-a phantom in the noise. Appropriate use of existing validating methods such as resolution-limited alignment and the processing of independent data sets ("gold standard") avoid this pitfall. However, these methods can be undermined by biases introduced in various subtle ways. How can we test that a map is a coherent structure present in the images selected from the micrographs? In stead of viewing the phantom emerging from noise as a cautionary tale, it should be used as a defining baseline. Any map is always recoverable from noise images, provided a sufficient number of images are aligned and used in reconstruction. However, with smaller numbers of images, the expected coherence in the real particle images should yield better reconstructions than equivalent numbers of noise or background images, even without masking or imposing resolution limits as potential biases. The validation test proposed is therefore a simple alignment of a limited number of micrograph and noise images against the final reconstruction as reference, demonstrating that the micrograph images yield a better reconstruction. I examine synthetic cases to relate the resolution of a reconstruction to the alignment error as a function of the signal-to-noise ratio. I also administered the test to real cases of publicly available data. Adopting such a test can aid the microscopist in assessing the usefulness of the micrographs taken before committing to lengthy processing with questionable outcomes.

No MeSH data available.


Resolution-limited alignment of proteinase K for SNR values of (a) 0.01 and (b) 0.1 show the difference between dominance of noise and alignable images. The dotted lines indicate the different resolution limits imposed: blue, 2 Å; green, 4 Å; orange, 8 Å; red, 16 Å. Images used: 104.
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Figure 3: Resolution-limited alignment of proteinase K for SNR values of (a) 0.01 and (b) 0.1 show the difference between dominance of noise and alignable images. The dotted lines indicate the different resolution limits imposed: blue, 2 Å; green, 4 Å; orange, 8 Å; red, 16 Å. Images used: 104.

Mentions: The next question is: How close should the resolution estimates be to those of noise-derived reconstructions to be judged unacceptable? It is clear in the case of PK (Figure 2a) that the images with an SNR of 0.01 yield reconstructions very similar to those from noise. To shed more light on the matter, images of PK were aligned using different resolution limits and the reconstructions calculated. Figure 3a shows that an SNR of 0.01 precludes recovery of significant information beyond the alignment limit. In contrast, images with an SNR of 0.1 produce reconstructions with information extending towards the Nyquist frequency (Figure 3b).


Validation of 3D EM Reconstructions: The Phantom in the Noise.

Heymann JB - AIMS Biophys (2015)

Resolution-limited alignment of proteinase K for SNR values of (a) 0.01 and (b) 0.1 show the difference between dominance of noise and alignable images. The dotted lines indicate the different resolution limits imposed: blue, 2 Å; green, 4 Å; orange, 8 Å; red, 16 Å. Images used: 104.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4440490&req=5

Figure 3: Resolution-limited alignment of proteinase K for SNR values of (a) 0.01 and (b) 0.1 show the difference between dominance of noise and alignable images. The dotted lines indicate the different resolution limits imposed: blue, 2 Å; green, 4 Å; orange, 8 Å; red, 16 Å. Images used: 104.
Mentions: The next question is: How close should the resolution estimates be to those of noise-derived reconstructions to be judged unacceptable? It is clear in the case of PK (Figure 2a) that the images with an SNR of 0.01 yield reconstructions very similar to those from noise. To shed more light on the matter, images of PK were aligned using different resolution limits and the reconstructions calculated. Figure 3a shows that an SNR of 0.01 precludes recovery of significant information beyond the alignment limit. In contrast, images with an SNR of 0.1 produce reconstructions with information extending towards the Nyquist frequency (Figure 3b).

Bottom Line: This poses the risk of inappropriate data processing with dubious results.How can we test that a map is a coherent structure present in the images selected from the micrographs?Adopting such a test can aid the microscopist in assessing the usefulness of the micrographs taken before committing to lengthy processing with questionable outcomes.

View Article: PubMed Central - HTML - PubMed

Affiliation: National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, 50 South Dr, Bethesda, MD 20892, USA.

ABSTRACT

Validation is a necessity to trust the structures solved by electron microscopy by single particle techniques. The impressive achievements in single particle reconstruction fuel its expansion beyond a small community of image processing experts. This poses the risk of inappropriate data processing with dubious results. Nowhere is it more clearly illustrated than in the recovery of a reference density map from pure noise aligned to that map-a phantom in the noise. Appropriate use of existing validating methods such as resolution-limited alignment and the processing of independent data sets ("gold standard") avoid this pitfall. However, these methods can be undermined by biases introduced in various subtle ways. How can we test that a map is a coherent structure present in the images selected from the micrographs? In stead of viewing the phantom emerging from noise as a cautionary tale, it should be used as a defining baseline. Any map is always recoverable from noise images, provided a sufficient number of images are aligned and used in reconstruction. However, with smaller numbers of images, the expected coherence in the real particle images should yield better reconstructions than equivalent numbers of noise or background images, even without masking or imposing resolution limits as potential biases. The validation test proposed is therefore a simple alignment of a limited number of micrograph and noise images against the final reconstruction as reference, demonstrating that the micrograph images yield a better reconstruction. I examine synthetic cases to relate the resolution of a reconstruction to the alignment error as a function of the signal-to-noise ratio. I also administered the test to real cases of publicly available data. Adopting such a test can aid the microscopist in assessing the usefulness of the micrographs taken before committing to lengthy processing with questionable outcomes.

No MeSH data available.