Computes the F_beta score given an intenger number of True Positives (TP), True Negatives (TN). It is optimized for speed and n is thus not the total number of events

Given a high-dimensional dataset that typically represents a cytometry dataset, and a subset of the datapoints, this algorithm outputs an hyperrectangle so that datapoints within the hyperrectangle best correspond to the specified subset. In essence, this allows the conversion of clustering algorithms' outputs to gating strategies outputs.

Etienne Becht

hypergate

Machine Learning of Hyperrectangular Gating Strategies for
High-Dimensional Cytometry

Samuel Granjeaud

f function

<dl><dt>TP</dt>
<dd>Number of true positive events</dd>
<dt>TN</dt>
<dd>Number of true negative events</dd>
<dt>n</dt>
<dd>beta^2*(TP+FN)+TN+FP</dd>
<dt>beta2</dt>
<dd>squared-beta to weight precision (low beta) or recall (high beta) more</dd></dl>

Arguments

f — f

<dl>

<dt>TP</dt>
<dd>Number of true positive events</dd>


<dt>TN</dt>
<dd>Number of true negative events</dd>


<dt>n</dt>
<dd>beta^2*(TP+FN)+TN+FP</dd>


<dt>beta2</dt>
<dd>squared-beta to weight precision (low beta) or recall (high beta) more</dd>

</dl>

f: f

Description

Usage

Arguments