Test of Normality for Integrated Change Point Detection and Mixture Modeling

Test of Normality for Integrated Change Point Detection and Mixture Modeling Single-molecule data often show step-like changes in the quantity measured between constant levels. Analysis of this data consists of detecting the steps, i.e., change point detection (CPD), and determining the levels, i.e., clustering. We describe a novel algorithm which integrates these two analyses, based on a statistical test of a normal distribution. The test of normality (TON) algorithm integrates statistical CPD with gaussian mixture model clustering. We used TON with both simulated data and ion channel patch-clamp recordings. It performed well with simulated data except at a high signal-to-noise ratio and when the frequency of steps was high compared to the sampling frequency. TON has advantages over separate CPD and mixture modeling algorithms, especially for complex single-molecule data. This was illustrated by its application to the maxichannel, an ion channel with multiple subconductance states. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Membrane Biology Springer Journals

Test of Normality for Integrated Change Point Detection and Mixture Modeling

Loading next page...
 
/lp/springer_journal/test-of-normality-for-integrated-change-point-detection-and-mixture-D2Zwv30LEI
Publisher
Springer-Verlag
Copyright
Copyright © 2012 by Springer Science+Business Media New York
Subject
Life Sciences; Biochemistry, general; Human Physiology
ISSN
0022-2631
eISSN
1432-1424
D.O.I.
10.1007/s00232-012-9504-9
Publisher site
See Article on Publisher Site

Abstract

Single-molecule data often show step-like changes in the quantity measured between constant levels. Analysis of this data consists of detecting the steps, i.e., change point detection (CPD), and determining the levels, i.e., clustering. We describe a novel algorithm which integrates these two analyses, based on a statistical test of a normal distribution. The test of normality (TON) algorithm integrates statistical CPD with gaussian mixture model clustering. We used TON with both simulated data and ion channel patch-clamp recordings. It performed well with simulated data except at a high signal-to-noise ratio and when the frequency of steps was high compared to the sampling frequency. TON has advantages over separate CPD and mixture modeling algorithms, especially for complex single-molecule data. This was illustrated by its application to the maxichannel, an ion channel with multiple subconductance states.

Journal

The Journal of Membrane BiologySpringer Journals

Published: Oct 16, 2012

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off