exFINDER: Identifying external communication signals using single-cell transcriptomics data

Introduction

exFINDER is a method that identifies external signals (received signals from the external system) in the single-cell transcriptomics datasets by utilizing the prior knowledge of signaling pathways. Specifically,exFINDER contains the following features:

• It develops a computational method that links the prior knowledge and scRNA-seq data to identify the external signals received by the cells.
• It uses a computational model that utilizes the graph theory to infer the external signal-target signaling networks (exSigNet) and link the expression data to signaling strength.
• It provides multiple ways for visualization and analysis of the external signal-target signaling networks (exSigNet).

Installation

exFINDER R package can be easily installed from Github using devtools (it may take a little while):

devtools::install_github("ChanghanGitHub/exFINDER")

Applications of exFINDER on different datasets

• exFINDER identifies differentiation-associated external signals during zebrafish neural crest (NC) development. , source data comes from Tatarakis et al., Cell reports, 2021

• exFINDER suggests critical external signals and targets during sensory neurogenesis in mouse. , source data comes from Faure et al., Nature communications, 2020

• exFINDER predicts the roles of external signals and uncovers transition paths in differentiation, source data comes from Faure et al., Nature communications, 2020

Benchmarking

• Benchmarking exFINDER using human skin data., source data comes from He, Helen, et al. Journal of Allergy and Clinical Immunology, 2020