[RASMB] difference of p = 0.95, 0.68 and 0.55, the confidence level in the sedfit c(s) distribution

[John Philo]

Sorry Arthur but I have to strongly disagree with your statement that the
peaks from SEDFIT's least-squares g(s) method are Gaussians to a good
approximation. You are making the assumption that this approach is really
equivalent to Walter's original method, but it is not. Peter has never
published any theory showing this Gaussian assumption is true. Fundamentally
there is no reason I can see to believe that a least-squares fitter using a
model that assumes no diffusion should always produce a Gaussian peak when
applied to data broadened by diffusion.
 
While your statement is probably true in certain cases (particularly for
high mass species and when you use a high degree of smoothing), it is quite
easy to show it is NOT true in general, as I have done, by simulating a
single species of say 20 kDa and then calculating the ls-g(s) distribution.
The result will be distinctly non-Gaussian. 
 
John Philo
Alliance Protein Laboratories

-----Original Message-----
From: rasmb-admin at server1.bbri.org [mailto:rasmb-admin at server1.bbri.org] On
Behalf Of Arthur Rowe
Sent: Monday, July 12, 2004 9:15 AM
To: Jacob Lebowitz; medakachou; rasmb at server1.bbri.org
Subject: Re: [RASMB] difference of p = 0.95, 0.68 and 0.55, the
confidencelevel in the sedfit c(s) distribution



Hi all

In Peter's absence let me make one point which he (very correctly) makes
concerning estimation of s values and relative concentrations. Which is that
the c(s) distribution for each individual species in a mixture is not a
gaussian distribution. Hence one should not do 'peak fitting' by the usual
algorithms, in ORIGIN or anything else. As Jack very correctly says, it is
numerical integration over appropriate ranges which you need.

What is valid to a very good approximation is to fit multiple gaussians to
the (least squares in SEDFIT) g(s) profile. Basically as per Walter
Stafford's original approach.  In our own experience we find this gives a
more objective description of such systems, albeit - since it is better at
resolving closely related species including dimers of lower M (i.e. more
rapidly diffusing) monomers - the outcome can be rather less flattering to
the perceived 'quality' of one's precious preparation!

All best wishes to everyone

Arthur



-- 
*************************
Arthur Rowe
Lab at Sutton Bonington
tel: +44 115 951 6156
fax: +44 115 951 6157
*************************






Since Peter is on a long vacation, I will attempt to answer your question.
You can integrate the peaks by pressing the ctrl and I keys simultaneously
which will give you a dialog box that states that you should hold the right
mouse button down and draw a rectangle to cover the s range to be
integrated. You will see once you do the integration you will obtain both
the % of the loading signal in the integration range and the weight average
s value. Also the results box states that this integration it is best done
without regularization, confidence level of zero. Regularization gives the
most parsimonious distribution for  the confidence level that you set. Total
removal of regularization may give you too many peaks that will merge at
higher confidence levels. You can still integrate over multiple peaks in the
s range you have selected and compare the result with integration of the
distribution you obtain at higher confidence levels. In my experience the
integration results over the same s range are comparable from no
regularization to using settings of p = 0.68 to 0.95.  At the latter p
selections you have the more realistic description of the sedimenting
species. Hope that the above is clear.

Jack Lebowitz


At 10:29 PM 7/12/2004 +0800, medakachou wrote:


Dear all,

Recently, I'm analyzing the sedimentation velocity spectra by continuous
c(s) distribution (SEDFIT). I've analyzed my data in three kind of
confidence level: p = 0.95, 0.68 and 0.55 and the regularization method is
maximum entropy. The s limit is 0.1 to 25S. I found every species is not
well seperated (they just fuse together) in p = 0.95. In p = 0.68, the
situation is better and the peaks are more significant. p= 0.55 can give me
the highest resolution and every peak is very clear cut. Now the question
is: if I want to calculate the area of peaks by Origin peak fitting module,
which results should I use? I've check Schuck's paper and he suggests using
p = 0.68 to 0.95 is enough. How about 0.55? I appreciate your response and
suggestion.

Sincerely,


Chi-Yuan Chou
PhD student, the Institutes of Life sciences, National Defense Medical
Center, Taipei, Taiwan


e-mail: r6243023 at yahoo.com.tw





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