Regényi Enikő, Mashreghi Mir-Farzin, Schütte Christof, Sunkara Vikram
Systems Rheumatology, German Rheumatism Research Centre Berlin, Virchowweg 12, 10117 Berlin, Germany.
Visual and Data-Centric Computing, Zuse Institute Berlin, Takustraße 7, 14195 Berlin, Germany.
NAR Genom Bioinform. 2024 Dec 18;6(4):lqae179. doi: 10.1093/nargab/lqae179. eCollection 2024 Dec.
There is a growing interest in generating bimodal, single-cell RNA sequencing (RNA-seq) data for studying biological pathways. These data are predominantly utilized in understanding phenotypic trajectories using RNA velocities; however, the shape information encoded in the two-dimensional resolution of such data is not yet exploited. In this paper, we present an elliptical parametrization of two-dimensional RNA-seq data, from which we derived statistics that reveal four different modalities. These modalities can be interpreted as manifestations of the changes in the rates of splicing, transcription or degradation. We performed our analysis on a cell cycle and a colorectal cancer dataset. In both datasets, we found genes that are not picked up by differential gene expression analysis (DGEA), and are consequently unnoticed, yet visibly delineate phenotypes. This indicates that, in addition to DGEA, searching for genes that exhibit the discovered modalities could aid recovering genes that set phenotypes apart. For communities studying biomarkers and cellular phenotyping, the modalities present in bimodal RNA-seq data broaden the search space of genes, and furthermore, allow for incorporating cellular RNA processing into regulatory analyses.
人们对生成用于研究生物途径的双峰单细胞RNA测序（RNA-seq）数据的兴趣与日俱增。这些数据主要用于通过RNA速度来理解表型轨迹；然而，此类数据二维分辨率中编码的形状信息尚未得到利用。在本文中，我们提出了二维RNA-seq数据的椭圆参数化方法，从中我们推导出了揭示四种不同模式的统计数据。这些模式可以解释为剪接、转录或降解速率变化的表现形式。我们对一个细胞周期数据集和一个结直肠癌数据集进行了分析。在这两个数据集中，我们发现了一些基因，这些基因在差异基因表达分析（DGEA）中未被检测到，因此未被注意到，但却能明显地描绘出表型。这表明，除了DGEA之外，寻找表现出所发现模式的基因可能有助于发现区分表型的基因。对于研究生物标志物和细胞表型的群体而言，双峰RNA-seq数据中存在的模式拓宽了基因的搜索空间，而且还允许将细胞RNA加工纳入调控分析。
