Laidlaw Ross F, Briggs Emma M, Matthews Keith R, Madany Mamlouk Amir, McCulloch Richard, Otto Thomas D
Centre for Parasitology, University of Glasgow, Glasgow, G12 8QQ, United Kingdom.
Institute for Immunology and Infection Research, University of Edinburgh, Edinburgh, EH8 9YL, United Kingdom.
Bioinformatics. 2025 Mar 4;41(3). doi: 10.1093/bioinformatics/btaf073.
Single-cell transcriptomics sequencing is used to compare different biological processes. However, often, those processes are asymmetric which are difficult to integrate. Current approaches often rely on integrating samples from each condition before either cluster-based comparisons or analysis of an inferred shared trajectory.
We present Trajectory Alignment of Gene Expression Dynamics (TrAGEDy), which allows the alignment of independent trajectories to avoid the need for error-prone integration steps. Across simulated datasets, TrAGEDy returns the correct underlying alignment of the datasets, outperforming current tools which fail to capture the complexity of asymmetric alignments. When applied to real datasets, TrAGEDy captures more biologically relevant genes and processes, which other differential expression methods fail to detect when looking at the developments of T cells and the bloodstream forms of Trypanosoma brucei when affected by genetic knockouts.
TrAGEDy is freely available at https://github.com/No2Ross/TrAGEDy, and implemented in R.
单细胞转录组测序用于比较不同的生物学过程。然而，这些过程通常是不对称的，难以整合。当前的方法通常依赖于在基于聚类的比较或推断的共享轨迹分析之前整合来自每种条件的样本。
我们提出了基因表达动态轨迹比对（TrAGEDy），它允许独立轨迹的比对，从而避免了容易出错的整合步骤。在模拟数据集中，TrAGEDy返回数据集正确的潜在比对结果，优于当前无法捕捉不对称比对复杂性的工具。当应用于真实数据集时，TrAGEDy能够捕捉到更多具有生物学相关性的基因和过程，而其他差异表达方法在研究T细胞发育以及受基因敲除影响时布氏锥虫血流形式的发展情况时未能检测到这些。
