[3dem] [TEM] CTF and SNR

Wed Jun 7 06:54:14 PDT 2017

Hi John,
yes, that's fair enough. There is, indeed, no reason to suspect that water molecules wouldn't be subject to the same physics as everything else, and motion does explain why you don't see it in the averages.  However, the underlying point I was trying to make is that for purposes of single particle analysis, the "signal" is the perfect projection of a particle, and anything else is noise. Some component of this noise is subject to CTF oscillations, and some component is not. Some component of the oscillatory term coming from the ice doesn't change this fact. To properly estimate SSNR one needs to actually measure regions of the image not containing particles. I don't believe it can be properly modeled without doing this, since the image background in single particle data simply isn't "pure water".

Again, though, it isn't clear that having a more accurate SSNR estimate really helps you to get better structures. Yes, theoretically you should weight your data by SSNR, but the practical reality is that the uncertainty in the SSNR estimate makes it very difficult to achieve actual improvements using this.  What SSNR is useful for is evaluating data quality...

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Steven Ludtke, Ph.D.
Professor, Dept. of Biochemistry and Mol. Biol.                Those who do
Co-Director National Center For Macromolecular Imaging            ARE
Baylor College of Medicine                                     The converse
sludtke at bcm.edu<mailto:sludtke at bcm.edu>  -or-  stevel at alumni.caltech.edu<mailto:stevel at alumni.caltech.edu>               also applies
http://ncmi.bcm.edu/~stevel

On Jun 7, 2017, at 8:43 AM, John Rubinstein <john.rubinstein at utoronto.ca<mailto:john.rubinstein at utoronto.ca>> wrote:

***CAUTION:*** This email is not from a BCM Source. Only click links or open attachments you know are safe.
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Hi Steve,

As Greg McMullan and Richard Henderson showed recently, amorphous ice does give Thon rings. However, ice also exhibits a beam induced ‘Brownian’-like movement so that the power spectrum from the average of DDD frames does not often show Thon rings.
https://www.ncbi.nlm.nih.gov/pubmed/26103047<https://urldefense.proofpoint.com/v2/url?u=https-3A__www.ncbi.nlm.nih.gov_pubmed_26103047&d=DwMFaQ&c=ZQs-KZ8oxEw0p81sqgiaRA&r=GWA2IF6nkq8sZMXHpp1Xpg&m=bG1ISczT10r6qYocjTXyjaNx3S8A0R8UQjzs20Qymic&s=GMDYOeoavzqGZQPQDUJSg5nbPI14MXeOxMSSmElkzhg&e=>
Alexis Rohou modified CTF find in order to allow use of this signal in CTF estimation.

Best regards,
John

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John Rubinstein
Molecular Medicine Program
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On Jun 7, 2017, at 9:33 AM, Ludtke, Steven J <sludtke at bcm.edu<mailto:sludtke at bcm.edu>> wrote:

Hi Philip,
pure water ice, alas, does not exhibit Thon rings (at least not significantly). Other buffer constituents may. This makes modeling noise a bit tricky.

Many of the methods in the field start with the assumption that the image consists of a true projection of a macromolecule, convolved with the CTF with added uncorrelated Gaussian noise. That is, noise with a flat power spectrum and no correlation among nearby pixels. Prior to direct detectors and counting, the background noise power spectrum in typical cryoEM images had an exponential decay, and could with a little processing be "flattened" to produce something more or less like uncorrelated Gaussian noise.  The problem with this assumption, even then, is that CryoEM images actually contain a lot of other stuff, and from the perspective of single particle reconstruction, noise isn't just the scattering off the solvent, it's anything present in the image which is not a perfect projection of the particle.

This is what led to EMAN2's approach of computing the power spectrum of masked out particles, and masked out regions outside particles to compute an accurate estimate of the SNR of the particles within the images. Whether having this really produces better reconstructions is a separate question, but making this estimate DOES tell you quite a lot about particle data quality and whether you should expect to be able to achieve high resolution from your data.

I'm attaching some slides from about 10 years ago showing how SSNR estimates assuming a pretty flat background can give completely inaccurate estimates of the actual SSNR of the images. Hope they are reasonably self-explanatory. This compares the approach of considering noise to be anything in the power spectrum without Thon rings to be noise, with the approach of computing the noise from regions of the image not containing particles.

https://drive.google.com/file/d/0BxreryX3Fp-ZUVJtTFlYX0lmbzg/view?usp=sharing<https://urldefense.proofpoint.com/v2/url?u=https-3A__drive.google.com_file_d_0BxreryX3Fp-2DZUVJtTFlYX0lmbzg_view-3Fusp-3Dsharing&d=DwMFaQ&c=ZQs-KZ8oxEw0p81sqgiaRA&r=GWA2IF6nkq8sZMXHpp1Xpg&m=bG1ISczT10r6qYocjTXyjaNx3S8A0R8UQjzs20Qymic&s=vXuqF6vNwv8lPDzq0U9NsFqf83WiofR7JUZ8rhcguxE&e=>

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Steven Ludtke, Ph.D.
Professor, Dept of Biochemistry and Mol. Biol.         (www.bcm.edu/biochem<http://www.bcm.edu/biochem>)
Co-Director National Center For Macromolecular Imaging        (ncmi.bcm.edu<http://ncmi.bcm.edu/>)
Co-Director CIBR Center                          (www.bcm.edu/research/cibr<http://www.bcm.edu/research/cibr>)
Baylor College of Medicine
sludtke at bcm.edu<mailto:sludtke at bcm.edu>

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