Strope Benjamin S, Pendleton Katherine E, Bowie William Z, Echeverria Gloria V, Zhu Qian
Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.
Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA.
bioRxiv. 2023 Sep 5:2023.09.04.556109. doi: 10.1101/2023.09.04.556109.
Xenograft models are attractive models that mimic human tumor biology and permit one to perturb the tumor microenvironment and study its drug response. Spatially resolved transcriptomics (SRT) provide a powerful way to study the organization of xenograft models, but currently there is a lack of specialized pipeline for processing xenograft reads originated from SRT experiments. Xenomake is a standalone pipeline for the automated handling of spatial xenograft reads. Xenomake handles read processing, alignment, xenograft read sorting, quantification, and connects well with downstream spatial analysis packages. We additionally show that Xenomake can correctly assign organism specific reads, reduce sparsity of data by increasing gene counts, while maintaining biological relevance for studies.
异种移植模型是模拟人类肿瘤生物学的有吸引力的模型，它允许人们干扰肿瘤微环境并研究其药物反应。空间分辨转录组学（SRT）为研究异种移植模型的组织提供了一种强大的方法，但目前缺乏用于处理源自SRT实验的异种移植读数的专门流程。Xenomake是一个用于自动处理空间异种移植读数的独立流程。Xenomake处理读数处理、比对、异种移植读数分类、定量，并与下游空间分析软件包良好连接。我们还表明，Xenomake可以正确分配特定生物体的读数，通过增加基因计数来减少数据稀疏性，同时保持研究的生物学相关性。
