Reconstruction of gene-gene network via low-dimentional projections (via epsilon-NN).

Provide a workflow to jointly embed chromatin accessibility peaks and expressed genes into a shared low-dimensional space using paired single-cell ATAC-seq (scATAC-seq) and single-cell RNA-seq (scRNA-seq) data. It integrates regulatory relationships among peak-peak interactions (via 'Cicero'), peak-gene interactions (via Lasso, random forest, and XGBoost), and gene-gene interactions (via principal component regression). With the input of paired scATAC-seq and scRNA-seq data matrices, it assigns a low-dimensional feature vector to each gene and peak. Additionally, it supports the reconstruction of gene-gene network with low-dimensional projections (via epsilon-NN) and then the comparison of the networks of two conditions through manifold alignment implemented in 'scTenifoldNet'. See <doi:10.1093/bioinformatics/btaf483> for more details.

Yuntong Hou

scPOEM

Single-Cell Meta-Path Based Omic Embedding

Yan Zhong

Yeran Chen

Youshi Chang

Yongjian Yang

Xinyue Zheng

James Cai

eNN function

<dl><dt>E_g</dt>
<dd>Embedding representations of genes.</dd></dl>

Arguments

Network Reconstruction via epsilon-NN — eNN

<dl>

<dt>E_g</dt>
<dd>Embedding representations of genes.</dd>

</dl>

eNN: Network Reconstruction via epsilon-NN

Description

Usage

Value

Arguments