generate_ashape2d

Understanding morphological variation is an important task in many applications. Recent studies in computational biology have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this R package, we introduce the alpha-shape sampler: a probabilistic framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data or explicitly stated by the user. The 'ashapesampler' package supports two mechanisms for sampling shapes in two and three dimensions. The first, empirically sampling based on an existing data set, was highlighted in the original main text of the paper. The second, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al. (2024) <doi:10.1101/2024.01.09.574919>.

Emily Winn-Nunez

ashapesampler

Generating Alpha Shapes

Lorin Crawford

generate_ashape2d function

<dl><dt>point_cloud</dt>
<dd>2 column matrix of all points from all shapes in initial
data set</dd>
<dt>J</dt>
<dd>number of shapes in initial (sub) data set</dd>
<dt>tau</dt>
<dd>tau bound vector for shapes input</dd>
<dt>delta</dt>
<dd>probability of not preserving homology; default is 0.05</dd>
<dt>afixed</dt>
<dd>boolean, whether to sample alpha or leave fixed based on tau. Default FALSE</dd>
<dt>mu</dt>
<dd>mean of truncated distribution from which alpha sampled; default tau/3</dd>
<dt>sig</dt>
<dd>standard deviation of truncated distribution from which alpha
sampled; default tau/12</dd>
<dt>sample_rad</dt>
<dd>radius of ball around each point in point cloud from which to
sample; default tau/8</dd>
<dt>acc_rad</dt>
<dd>radius of ball to check around potential sampled points for whether
to accept or reject new point; default tau/4</dd>
<dt>k_min</dt>
<dd>number of points needed in radius tau of point cloud to accept a sample</dd>
<dt>eps</dt>
<dd>amount to subtract from tau/2 to give alpha. Defaul 1e-4.</dd>
<dt>cores</dt>
<dd>number of computer cores for parallelizing. Default 1.</dd></dl>

Arguments

Generate 2D alpha shape — generate_ashape2d

<dl>

<dt>point_cloud</dt>
<dd>2 column matrix of all points from all shapes in initial
data set</dd>


<dt>J</dt>
<dd>number of shapes in initial (sub) data set</dd>


<dt>tau</dt>
<dd>tau bound vector for shapes input</dd>


<dt>delta</dt>
<dd>probability of not preserving homology; default is 0.05</dd>


<dt>afixed</dt>
<dd>boolean, whether to sample alpha or leave fixed based on tau. Default FALSE</dd>


<dt>mu</dt>
<dd>mean of truncated distribution from which alpha sampled; default tau/3</dd>


<dt>sig</dt>
<dd>standard deviation of truncated distribution from which alpha
sampled; default tau/12</dd>


<dt>sample_rad</dt>
<dd>radius of ball around each point in point cloud from which to
sample; default tau/8</dd>


<dt>acc_rad</dt>
<dd>radius of ball to check around potential sampled points for whether
to accept or reject new point; default tau/4</dd>


<dt>k_min</dt>
<dd>number of points needed in radius tau of point cloud to accept a sample</dd>


<dt>eps</dt>
<dd>amount to subtract from tau/2 to give alpha. Defaul 1e-4.</dd>


<dt>cores</dt>
<dd>number of computer cores for parallelizing. Default 1.</dd>

</dl>

generate_ashape2d: Generate 2D alpha shape

Description

Usage

Value

Arguments