lung5

multiplexed image data on tumor tissue sample from non small cell lung cancer patient

datasets

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

lung5 function

## `lung5`
A data frame with 100149 rows and 4 columns:<dl>
 <dt>image</dt>
<dd>Image ID</dd> <dt>X</dt>
<dd>X coordinate of the cell</dd> <dt>Y</dt>
<dd>Y coordinate of the cell</dd> <dt>type</dt>
<dd>cell type</dd>
</dl>

Format

Example input data for 'SpaTopic' — lung5

## `lung5`
A data frame with 100149 rows and 4 columns:<dl>
 <dt>image</dt>
<dd>Image ID</dd>

 <dt>X</dt>
<dd>X coordinate of the cell</dd>

 <dt>Y</dt>
<dd>Y coordinate of the cell</dd>

 <dt>type</dt>
<dd>cell type</dd>


</dl>

lung5: Example input data for 'SpaTopic'

Description

Usage

Arguments

Format

See Also