CACIMAR: Cross-species Analysis of Cell Identities, Markers, Regulations and Interactions Using Single-cell RNA Sequencing Data
Junyao JiangiD, Jinlian LiiD, Sunan Huang, Fan JiangiD, Yanran LiangiD, Xueli XuiD, Jie WangiD
Abstract
Transcriptomic analysis across species is increasingly used to reveal conserved gene regulations which implicate crucial regulators. Cross-species analysis of single-cell RNA sequencing (scRNA-seq) data provides new opportunities to identify the cellular and molecular conservations especially for cell types and cell type-specific gene regulations. However, few methods have been developed to analyze cross-species scRNA-seq data to uncover both molecular and cellular conservation patterns. Here, we built a tool called CACIMAR, which can perform cross-species analysis of cell identities, markers, regulations and interactions using scRNA-seq profiles. Based on the weighted sum models of the conserved features, we developed different conservation scores to measure the conservation of cell types, regulatory networks and intercellular interactions. Using publicly available scRNA-seq data on retinal regeneration in mice and zebrafish, we demonstrated four main functions of CACIMAR. First, CACIMAR allows to identify evolutionarily conserved cell types, including poorly conserved cell types. Second, the tool facilitates the identification of evolutionarily conserved or species-specific marker genes. Third, CACIMAR enables the identification of conserved intracellular regulations, including cell type-specific regulatory subnetworks and regulators. Lastly, CACIMAR provides a unique feature on the identification of conserved intercellular interactions. Overall, CACIMAR facilitates the identification of evolutionarily conserved cell types, marker genes, intracellular regulations and intercellular interactions, providing insights on the cellular and molecular mechanisms of species evolution.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 6
- FWCI
- 0.81
- Field percentile
- top 33%
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Cross-species cell-type assignment of single-cell RNA-seq by a heterogeneous graph neural network
Xingyan Liu, Qunlun Shen, Shihua Zhang·Genome ResearchPeer-reviewed·Jan 1, 2023
- Multi-species integration, alignment and annotation of single-cell RNA-seq data with CAMEX
Zhen-Hao Guo, De-Shuang Huang, Shihua Zhang·Nature CommunicationsPeer-reviewed·Feb 21, 2026
- Benchmarking strategies for cross-species integration of single-cell RNA sequencing data
Yuyao Song, Zhichao Miao, … Irene Papatheodorou·Nature CommunicationsPeer-reviewed·Oct 14, 2023
- Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes
Dustin Sokolowski, Mariela Faykoo-Martinez, … Michael D. Wilson·NAR Genomics and BioinformaticsPeer-reviewed·Jan 6, 2021
- SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species
Yuqi Tan, Patrick Cahan·Cell SystemsPeer-reviewed·Jul 31, 2019
- Cross-species imputation and comparison of single-cell transcriptomic profiles
Ran Zhang, Mu Yang, … William Stafford Noble·Genome biologyPeer-reviewed·Feb 26, 2025