compute.spatial.kld

This function is to calculate how likely the cells relevant to a gene set is
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 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_vec</dt>
<dd>output of run.rwr</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 weight_vec is shuffled several times (n.times) to generate
a background distribution (shuffled weights vs. equal weights) for statistical
significance estimation (p.value); larger n.times will be more time-consuming and
more accurate</dd></dl>

Arguments

6. find gene sets with spatial relevance — compute.spatial.kld

<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_vec</dt>
<dd>output of run.rwr</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 weight_vec is shuffled several times (n.times) to generate
a background distribution (shuffled weights vs. equal weights) for statistical
significance estimation (p.value); larger n.times will be more time-consuming and
more accurate</dd>

</dl>

compute.spatial.kld: 6. find gene sets with spatial relevance

Description

Usage

Value

Arguments