Evidence map›Paper›PMID 38856169›Full record

ArticleBriefings in bioinformatics2024

CACIMAR: cross-species analysis of cell identities, markers, regulations, and interactions using single-cell RNA sequencing data.

Junyao Jiang, Jinlian Li, Sunan Huang, Fan Jiang, Yanran Liang, Xueli Xu, Jie Wang

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Junyao JiangCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Road, Huangpu District, Guangzhou 510530, China.
Jinlian LiCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Road, Huangpu District, Guangzhou 510530, China.
Sunan HuangCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Road, Huangpu District, Guangzhou 510530, China.
Fan JiangCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Road, Huangpu District, Guangzhou 510530, China.
Yanran LiangCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Road, Huangpu District, Guangzhou 510530, China.
Xueli XuCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Road, Huangpu District, Guangzhou 510530, China.
Jie WangCAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Road, Huangpu District, Guangzhou 510530, China.ORCID 0000-0002-7491-7001

Funding

Guangdong Province Science and Technology Program 2023B1212060050Ministry of Science and Technology of China 2022YFA1105400National Natural Science Foundation of China T2222003
6 · The paper itself

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 conservations. 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, zebrafish, and chick, we demonstrated four main functions of CACIMAR. First, CACIMAR allows to identify conserved cell types even in evolutionarily distant species. 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 for identifying conserved intercellular interactions. Overall, CACIMAR facilitates the identification of evolutionarily conserved cell types, marker genes, intracellular regulations, and intercellular interactions, providing insights into the cellular and molecular mechanisms of species evolution.

Indexed as

Sequence Analysis, RNASingle-Cell AnalysisZebrafishAnimalsBiomarkersChickensComputational BiologyGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksMiceSoftwareSpecies SpecificityBiomarkerscell–cell interactionsconservation analysiscross-species scRNA-seqregulatory networks

Identifiers

PMID38856169
PMCPMC11163379

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.