Kwee Ivo, Martinelli Axel, Khayal Layal Abo, Akhmedov Murodzhon
BigOmics Analytics, 6500 Bellinzona, Switzerland.
Bioinform Adv. 2022 Sep 9;2(1):vbac064. doi: 10.1093/bioadv/vbac064. eCollection 2022.
Accessing the collection of perturbed gene expression profiles, such as the LINCS L1000 connectivity map, is usually performed at the individual dataset level, followed by a summary performed by counting individual hits for each perturbagen. With the metaLINCS R package, we present an alternative approach that combines rank correlation and gene set enrichment analysis to identify meta-level enrichment at the perturbagen level and, in the case of drugs, at the mechanism of action level. This significantly simplifies the interpretation and highlights overarching themes in the data. We demonstrate the functionality of the package and compare its performance against those of three currently used approaches.
metaLINCS is released under GPL3 license. Source code and documentation are freely available on GitHub (https://github.com/bigomics/metaLINCS).
Supplementary data are available at online.
访问受干扰的基因表达谱集合，如LINCS L1000连接图谱，通常是在单个数据集层面进行，随后通过对每个干扰因素的单个命中数进行计数来进行汇总。通过metaLINCS R包，我们提出了一种替代方法，该方法结合了秩相关和基因集富集分析，以识别干扰因素层面的元水平富集，对于药物而言，还能识别作用机制层面的元水平富集。这显著简化了数据解释，并突出了数据中的总体主题。我们展示了该包的功能，并将其性能与目前使用的三种方法进行了比较。
metaLINCS在GPL3许可下发布。源代码和文档可在GitHub（https://github.com/bigomics/metaLINCS）上免费获取。
补充数据可在网上获取。
