Bavais Julie, Chevallier Jessica, Spinelli Lionel, van de Pavert Serge A, Puthier Denis
Aix-Marseille Univ, INSERM, TAGC, Turing Centre for Living systems, 13288 Marseille, France.
Aix-Marseille Univ, CNRS, INSERM, CIML, Turing Centre for Living systems, 13009 Marseille, France.
NAR Genom Bioinform. 2025 Apr 17;7(2):lqaf043. doi: 10.1093/nargab/lqaf043. eCollection 2025 Jun.
The standard pipeline to analyze single-cell RNA-seq or spatial transcriptomics data focuses on a gene-centric approach that overlooks the collective behavior of genes. However, understanding cell populations necessitates recognizing intricate combinations of activated and repressed pathways. Therefore, a broader view of gene behavior offers more accurate insights into cellular heterogeneity in single-cell or spatial transcriptomics data. Here, we describe SciGeneX (Single-cell informative Gene eXplorer), a R package implementing a neighborhood analysis and a graph partitioning method to generate co-expression gene modules. These modules, whether shared or restricted to cell populations, collectively reflect cellular heterogeneity. Their combinations are able to highlight specific cell populations, even rare ones. SciGeneX uncovers rare and novel cell populations that were not observed before in human thymus spatial transcriptomics data. We show that SciGeneX outperforms existing methods on both artificial and experimental datasets. Overall, SciGeneX will aid in unravelling cellular and molecular diversity in single-cell and spatial transcriptomics studies.
分析单细胞RNA测序或空间转录组学数据的标准流程侧重于以基因为中心的方法，这种方法忽略了基因的集体行为。然而，理解细胞群体需要认识到激活和抑制途径的复杂组合。因此，对基因行为的更广泛视角能为单细胞或空间转录组学数据中的细胞异质性提供更准确的见解。在此，我们描述了SciGeneX（单细胞信息基因探索器），这是一个R包，它实现了一种邻域分析和图划分方法来生成共表达基因模块。这些模块，无论是共享的还是仅限于细胞群体的，都共同反映了细胞异质性。它们的组合能够突出特定的细胞群体，甚至是罕见的细胞群体。SciGeneX揭示了在人类胸腺空间转录组学数据中以前未观察到的罕见和新的细胞群体。我们表明，SciGeneX在人工数据集和实验数据集上均优于现有方法。总体而言，SciGeneX将有助于揭示单细胞和空间转录组学研究中的细胞和分子多样性。
