compute.spatial.kld.df

This function is to calculate how likely the cells relevant to
multiple gene sets are randomly distributed spatially

Analysis frameworks for pathway heterogeneity and pathway activity evaluation in single-cell data, including scRNA-seq data and spatial genomics data.

Qingnan, Liang 

gsdensity

Density-Based Gene Set Specificity Evaluation

Ken Chen

compute.spatial.kld.df function

<dl><dt>spatial.coords</dt>
<dd>a data frame with each row as a cell and each column
as a spatial coordinate (usually 2: x and y)</dd>
<dt>weight_df</dt>
<dd>output of run.rwr.list</dd>
<dt>n</dt>
<dd>split the spatial map for local density estimation;
n is the number of split for each dimension; for n = 10, the spatial map is
split to n * n = 100 grids for the density estimation</dd>
<dt>n.times</dt>
<dd>the same as n.times in function 'compute.spatial.kld'</dd></dl>

Arguments

This function is to calculate how likely the cells relevant to
multiple gene sets are randomly distributed spatially — compute.spatial.kld.df

<dl>

<dt>spatial.coords</dt>
<dd>a data frame with each row as a cell and each column
as a spatial coordinate (usually 2: x and y)</dd>


<dt>weight_df</dt>
<dd>output of run.rwr.list</dd>


<dt>n</dt>
<dd>split the spatial map for local density estimation;
n is the number of split for each dimension; for n = 10, the spatial map is
split to n * n = 100 grids for the density estimation</dd>


<dt>n.times</dt>
<dd>the same as n.times in function 'compute.spatial.kld'</dd>

</dl>

compute.spatial.kld.df: This function is to calculate how likely the cells relevant to multiple gene sets are randomly distributed spatially

Description

Usage

Value

Arguments

Examples