2015Heymann Alignability: Difference between revisions

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(Created page with "== 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 t...")
 
 
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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 reconstr
single particle reconstruction fuel its expansion  
uction fuel its expansion  
beyond a small community of image processing experts. This poses the risk of inappropriate data  
beyond a small community of image pr
processing with dubious results. Nowhere is it more clearly
ocessing experts. This poses th
illustrated than in
e risk of inappropriate data  
the recovery of a  
processing with dubious results. Nowh
reference density map from pure noise aligned to that map—a phantom
ere is it more clearly
in the noise. Appropriate use  
illustrated than in
of existing validating methods such as resolution-limited alignment and the processing of  
the recovery of a  
reference density map from pure noi
se aligned to that map—a phantom
in the noise. Appropriate use  
of existing validating methods such as resolu
tion-limited alignment and the processing of  
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 defi
tale, it should be used as a defining baseline. Any map is always  
ning 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 sufficie
reconstruction. However, with smaller numbers of images, the expected  
nt number of images are aligned and used in  
reconstruction. However, with smalle
r numbers of images, the expected  
coherence in the  
coherence in the  
real particle  
real particle  
images should yield better reconstr
images should yield better reconstructions than equivalent number
uctions 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, demons
reconstruction as reference, demonstrating that the micrograph images
trating that the micrograph images
yield a better reconstruction. I  
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-noi
function of the signal-to-noise ratio. I also administered the test to real cases of publicly available  
se ratio. I also administer
data. Adopting such a test can aid the microscopist in assessing  
ed the test to real cases of publicly available  
the usefulness of the micrographs taken before committing to lengthy processing with questionable outcomes.
data. Adopting such a test can ai
d the microscopist in assessing  
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

Links

http://www.aimspress.com/aimsbpoa/ch/reader/create_pdf.aspx?file_no=Biophys-10&year_id=2015&quarter_id=1&falg=1

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