Difference between revisions of "2015Heymann Alignability"
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by electron microscopy by single | by electron microscopy by single | ||
particle techniques. The impressive achievements in | 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 | |
| − | beyond a small community of image | + | processing with dubious results. Nowhere is it more clearly |
| − | + | illustrated than in | |
| − | + | the recovery of a | |
| − | processing with dubious results. | + | 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 | |
| − | |||
| − | reference density map from pure | ||
| − | |||
| − | |||
| − | of existing validating methods such as | ||
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independent data sets (“gold standard”) avoid | independent data sets (“gold standard”) avoid | ||
this pitfall. However, these methods can be | this pitfall. However, these methods can be | ||
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the micrographs? In stead of viewing the phantom | the micrographs? In stead of viewing the phantom | ||
emerging from noise as a cautionary | emerging from noise as a cautionary | ||
| − | tale, it should be used as a | + | 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 | |
| − | recoverable from noise images, provided a | + | reconstruction. However, with smaller numbers of images, the expected |
| − | |||
| − | reconstruction. However, with | ||
| − | |||
coherence in the | coherence in the | ||
real particle | real particle | ||
| − | images should yield better | + | images should yield better reconstructions than equivalent number |
| − | |||
s of noise or background images, | s of noise or background images, | ||
even without masking or imposing resolution limits | even without masking or imposing resolution limits | ||
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is therefore a simple alignment of a limited number | is therefore a simple alignment of a limited number | ||
of micrograph and noise images against the final | of micrograph and noise images against the final | ||
| − | reconstruction as reference, | + | 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 | examine synthetic cases to relate the resolution of a reconstruction to the alignment error as a | ||
| − | function of the signal-to- | + | 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. | |
| − | data. Adopting such a test can | ||
| − | |||
| − | the usefulness of the micrographs | ||
| − | taken before committing to lengthy processing with questionable outcomes. | ||
== Keywords == | == Keywords == | ||
Latest revision as of 12:32, 11 May 2015
Citation
Heymann, B. Validation of 3DEM Reconstructions: The phantom in the noise AIMS Biophysics, 2015, 2, 21-35
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 number s 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.
Keywords
Validation
