scDiffCom-class

An object of this class stores the intercellular communication results
obtained when calling <code>run_interaction_analysis</code>.

Analysis tools to investigate changes in intercellular
communication from scRNA-seq data. Using a Seurat object as input,
the package infers which cell-cell interactions are present in the dataset
and how these interactions change between two conditions of interest
(e.g. young vs old). It relies on an internal database of ligand-receptor
interactions (available for human, mouse and rat) that have been gathered
from several published studies. Detection and differential analyses
rely on permutation tests. The package also contains several tools
to perform over-representation analysis and visualize the results. See
Lagger, C. et al. (2023) <doi:10.1038/s43587-023-00514-x> for a full
description of the methodology.

Cyril Lagger

scDiffCom

Differential Analysis of Intercellular Communication from
scRNA-Seq Data

Eugen Ursu

Anais Equey

scDiffCom-class function

<dl>
<dt><code>parameters</code></dt>
<dd>List of parameters passed to
<code>run_interaction_analysis</code> and used to build the object.</dd></dl>
<dt><code>cci_table_raw</code></dt>
<dd>Data.table with all hypothetic CCIs induced from
the original Seurat object and the internal LRI database. Can be erased
with <code>EraseRawCCI</code> to obtain a lighter object, but might be
worth keeping if one intends to modify the filtering parameters
(see also our
 <a href="https://cyrillagger.github.io/scDiffCom/articles/scDiffCom-vignette.html">vignette</a>).</dd>
<dt><code>cci_table_detected</code></dt>
<dd>Data.table with only the detected CCIs. If
<code>cci_table_raw</code> is not <code>NULL</code>, can be updated with new filtering
parameters without running the full permutation analysis (see
<code>FilterCCI</code>).</dd>
<dt><code>ora_table</code></dt>
<dd>List of data.tables with the results of the
over-representation analysis for each category. Results for additional
categories can be added with <code>RunORA</code>.</dd>
<dt><code>distributions</code></dt>
<dd>List of matrices with the null distributions of each
CCI. <code>NULL</code> by default.</dd>

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The scDiffCom Class — scDiffCom-class

<dl>
<dt><code>parameters</code></dt>
<dd>List of parameters passed to
<code>run_interaction_analysis</code> and used to build the object.</dd>


<dt><code>cci_table_raw</code></dt>
<dd>Data.table with all hypothetic CCIs induced from
the original Seurat object and the internal LRI database. Can be erased
with <code>EraseRawCCI</code> to obtain a lighter object, but might be
worth keeping if one intends to modify the filtering parameters
(see also our
 <a href='https://cyrillagger.github.io/scDiffCom/articles/scDiffCom-vignette.html'>vignette</a>).</dd>


<dt><code>cci_table_detected</code></dt>
<dd>Data.table with only the detected CCIs. If
<code>cci_table_raw</code> is not <code>NULL</code>, can be updated with new filtering
parameters without running the full permutation analysis (see
<code>FilterCCI</code>).</dd>


<dt><code>ora_table</code></dt>
<dd>List of data.tables with the results of the
over-representation analysis for each category. Results for additional
categories can be added with <code>RunORA</code>.</dd>


<dt><code>distributions</code></dt>
<dd>List of matrices with the null distributions of each
CCI. <code>NULL</code> by default.</dd>


</dl>

scDiffCom-class: The scDiffCom Class

Description

Arguments

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