GmmThreshold-class

GmmThreshold

print,GmmThreshold-method

GmmThreshold-method

plot,GmmThreshold,missing-method

<code>GmmThreshold</code> contains output from the <code>gmm</code> method findThreshold. 
It includes parameters of two Gaussian fits and threshold cut.

Provides a computational framework for analyzing mutations in
immunoglobulin (Ig) sequences. Includes methods for Bayesian estimation of
antigen-driven selection pressure, mutational load quantification, building of
somatic hypermutation (SHM) models, and model-dependent distance calculations.
Also includes empirically derived models of SHM for both mice and humans.
Citations:
Gupta and Vander Heiden, et al (2015) <doi:10.1093/bioinformatics/btv359>,
Yaari, et al (2012) <doi:10.1093/nar/gks457>,
Yaari, et al (2013) <doi:10.3389/fimmu.2013.00358>,
Cui, et al (2016) <doi:10.4049/jimmunol.1502263>.

Susanna Marquez

shazam

Immunoglobulin Somatic Hypermutation Analysis

Mohamed Uduman

Namita Gupta

Julian Zhou

Nima Nouri

Noah Yann Lee

Ang Cui

Jason Vander Heiden

Gur Yaari

Steven Kleinstein

GmmThreshold-class function

<dl><dt>x</dt>
<dd>GmmThreshold object</dd>
<dt>y</dt>
<dd>ignored.</dd>
<dt>...</dt>
<dd>arguments to pass to plotGmmThreshold.</dd></dl>

Arguments

<dl>
<dt><code>x</code></dt>
<dd>input distance vector with NA or infinite values removed.</dd></dl>
<dt><code>model</code></dt>
<dd>first-second fit functions.</dd>
<dt><code>cutoff</code></dt>
<dd>type of threshold cut.</dd>
<dt><code>a1</code></dt>
<dd>mixing weight of the first curve.</dd>
<dt><code>b1</code></dt>
<dd>second parameter of the first curve. Either the mean of a Normal 
distribution or shape of a Gamma distribution.</dd>
<dt><code>c1</code></dt>
<dd>third parameter of the first curve. Either the standard deviation of a 
Normal distribution or scale of a Gamma distribution.</dd>
<dt><code>a2</code></dt>
<dd>mixing weight of the second curve.</dd>
<dt><code>b2</code></dt>
<dd>second parameter of the second curve. Either the mean of a Normal 
distribution or shape of a Gamma distribution.</dd>
<dt><code>c2</code></dt>
<dd>third parameter of the second curve. Either the standard deviation 
of a Normal distribution or scale of a Gamma distribution.</dd>
<dt><code>loglk</code></dt>
<dd>log-likelihood of the fit.</dd>
<dt><code>threshold</code></dt>
<dd>threshold.</dd>
<dt><code>sensitivity</code></dt>
<dd>sensitivity.</dd>
<dt><code>specificity</code></dt>
<dd>specificity.</dd>
<dt><code>pvalue</code></dt>
<dd>p-value from Hartigans' dip statistic (HDS) test. 
Values less than 0.05 indicate significant bimodality.</dd>

Slots

Output of the gmm method of findThreshold — GmmThreshold-class

<dl>

<dt>x</dt>
<dd>GmmThreshold object</dd>


<dt>y</dt>
<dd>ignored.</dd>


<dt>...</dt>
<dd>arguments to pass to plotGmmThreshold.</dd>

</dl>

<dl>
<dt><code>x</code></dt>
<dd>input distance vector with NA or infinite values removed.</dd>


<dt><code>model</code></dt>
<dd>first-second fit functions.</dd>


<dt><code>cutoff</code></dt>
<dd>type of threshold cut.</dd>


<dt><code>a1</code></dt>
<dd>mixing weight of the first curve.</dd>


<dt><code>b1</code></dt>
<dd>second parameter of the first curve. Either the mean of a Normal 
distribution or shape of a Gamma distribution.</dd>


<dt><code>c1</code></dt>
<dd>third parameter of the first curve. Either the standard deviation of a 
Normal distribution or scale of a Gamma distribution.</dd>


<dt><code>a2</code></dt>
<dd>mixing weight of the second curve.</dd>


<dt><code>b2</code></dt>
<dd>second parameter of the second curve. Either the mean of a Normal 
distribution or shape of a Gamma distribution.</dd>


<dt><code>c2</code></dt>
<dd>third parameter of the second curve. Either the standard deviation 
of a Normal distribution or scale of a Gamma distribution.</dd>


<dt><code>loglk</code></dt>
<dd>log-likelihood of the fit.</dd>


<dt><code>threshold</code></dt>
<dd>threshold.</dd>


<dt><code>sensitivity</code></dt>
<dd>sensitivity.</dd>


<dt><code>specificity</code></dt>
<dd>specificity.</dd>


<dt><code>pvalue</code></dt>
<dd>p-value from Hartigans' dip statistic (HDS) test. 
Values less than 0.05 indicate significant bimodality.</dd>


</dl>

GmmThreshold-class: Output of the `gmm` method of findThreshold

Description

Usage

Arguments

Slots

See Also