stratified_sampling_sf

Spatially stratified random sample points from an image by R package <code>sf</code>

A novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. 'SpaTopic' is scalable to large-scale image datasets without extracting neighborhood information for every single cell. For more details on the methodology, see <https://xiyupeng.github.io/SpaTopic/>.

Xiyu Peng

SpaTopic

Topic Inference to Identify Tissue Architecture in Multiplexed
Images

stratified_sampling_sf function

<dl><dt>points</dt>
<dd>a data frame contains all points in a image with X, Y coordinates.</dd>
<dt>cellsize</dt>
<dd>a vector of length 2 contains the size of each grid square. 
Default c(600,600).</dd>
<dt>num_samples_per_stratum</dt>
<dd>number of point selected from each grid square.
Default 1.</dd></dl>

Arguments

Spatially stratified random sample points from an image. — stratified_sampling_sf

<dl>

<dt>points</dt>
<dd>a data frame contains all points in a image with X, Y coordinates.</dd>


<dt>cellsize</dt>
<dd>a vector of length 2 contains the size of each grid square. 
Default c(600,600).</dd>


<dt>num_samples_per_stratum</dt>
<dd>number of point selected from each grid square.
Default 1.</dd>

</dl>

stratified_sampling_sf: Spatially stratified random sample points from an image.

Description

Usage

Value

Arguments

Examples