IntraTalker

This package enables condition- and cell-type-specific analysis of transcription factor activities and generates intracellular signaling Networks. The results can be combined with ligand-receptor predictions and analyzed differentially using CrossTalkeR.

To perform receptor perturbation simulation, use the Python package IntraTalkerpy

Abstract

Single-cell sequencing has advanced the study of cell-cell communication, yet most methods focus on intercellular ligand- receptor interactions while neglecting downstream intracellular signalling cascades and the possibility that downstream target genes themselves encode ligands, thereby propagating communication across multiple cells. We present In- traTalker+CrossTalkeR that combines intracellular (IntraTalker) and intercellular (CrossTalkeR) signalling from multimodal single-cell data. IntraTalker infers cell-type-specific transcription factor activities and constructs receptomes that link receptors to downstream target genes, which are then integrated with ligand-receptor predictions in CrossTalkeR. To prioritize signalling receptors, the framework performs in silico receptor perturbation.

Install

IntraTalker depends on two Bioconductor packages, which are not available from CRAN and therefore have to be installed with BiocManager:

Package	Used for
ComplexHeatmap	Heatmaps of transcription factor activities
scran	Marker detection in the differential transcription factor analysis

# Install BiocManager if necessary
install.packages("BiocManager")

BiocManager::install(c("ComplexHeatmap", "scran"))

All remaining dependencies are on CRAN and are installed automatically.

Then install the package from GitHub with devtools:

# Install devtools if necessary
install.packages("devtools")

# Install the package
devtools::install_github("CostaLab/IntraTalker")

or with the remotes package:

# Install remotes if necessary
install.packages("remotes")

# Install the package
remotes::install_github("CostaLab/IntraTalker")

Both installers read the biocViews field in DESCRIPTION and add the Bioconductor repositories automatically, so they can also resolve ComplexHeatmap and scran on their own. Installing them up front with BiocManager is the more reliable route, as it pins them to the Bioconductor release matching your R version.