There are several cell states involved in cell development or disease occurrence (e.g., progenitor, precursor, immature, and mature), each state maintained by a unique gene program (gene regulatory modules). Decoding the inter- or intra-regulatory mechanisms among these modules can further elucidate the key mechanisms that regulate cell state transitions, including identifying key transcription factors that regulate cell fate decisions or cell differentiation. Most current gene regulatory network (GRN) analysis methods focus on intra-modular regulations; they select all cell states or single cell states to construct GRNs and neglect inter-modular regulations.
IReNA can address this gap by identifying transcription factors (TFs) that regulate other modules and inferring inter-modular interactions through hypergeometric tests. For instance, if IReNA identifies TF A from module a significantly activating module b, we can infer that TF A may regulate the differentiation of the progenitor state into the precursor state. In a second case, if IReNA identifies TF B from module c significantly repressing module d, we can infer that TF B represses the differentiation process from the immature state to the mature state.
IReNA needs R version 4.0 or higher, and Bioconductor version 3.12.
First, install Bioconductor, open R platform and run:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(version = "3.12")Next, install several Bioconductor dependencies:
BiocManager::install(c('Rsamtools', 'ChIPseeker', 'monocle',
'RcisTarget', 'RCy3', 'clusterProfiler'))Then, install IReNA from GitHub:
install.packages("devtools")
devtools::install_github("jiang-junyao/IReNA")Finally, check whether IReNA was installed correctly, restart R session and run:
library(IReNA)2024.11.12: add the signifiance of IReNA and update the workflow.
2024.10.17: add qucik start tutorial at the ReadME page.
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Regulatory network analysis through intergrating scRNA-seq data and scATAC-seq data
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Regulatory network analysis through intergrating scRNA-seq data and bulk ATAC-seq data
An example of applying IReNA to identify key transcription factors in retinal regeneration
Jiang J, Lyu P, Li J, et al. IReNA: Integrated regulatory network analysis of single-cell transcriptomes and chromatin accessibility profiles. iScience 2022, 25(11): 105359.
Hoang T, Wang J, Boyd P, et al. Gene regulatory networks controlling vertebrate retinal regeneration. Science 2020, 370 (6519): eabb8598.
If you have any question, comment or suggestion, please use github issue tracker to report issues of IReNA or contact [email protected]. I will answer you timely, and please remind me again if you have not received response more than three days.