PathActMarker: an R package for inferring pathway activity of complex diseases

The process of complex diseases is closely related to the dysregulation of key biological pathways, it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the individual sample level.
A research team led by Associate Professor Xingyi Li has published their new research in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.
The team proposed a powerful software package, PathActMarker, for pathway activity inference and analysis. PathActMarker has three main steps: (i) Preprocessing of the input gene expression data and including six prevalent pathway sources; (ii) Eight state-of-the-art tools are provided to convert the high-dimensional gene expression data into biologically interpretable low-dimensional pathway activity matrics, and extensive evaluations are also included to measure the performance of these tools; (iii) Based on the pathway activity matrix, pathways can be ranked using statistical and machine learning algorithms and a set of functions are provided for interpretation and analysis of top-ranked pathways.
PathActMarker allows input of two types of transcriptome data including RNA sequencing and DNA microarray, and it provides six prevalent pathway data, including five pathway sources in MSigDB database (Hallmark, BioCarta, GO, Wikipathways, Reactcome) and pathways in KEGG database. PathActMarker provides eight state-of-the-art pathway activity inference tools (SEPA, pathifier, GSVA, ssGSEA, CORGs, PLAGE, PLIRE, iPath) to convert high-dimensional gene expression data into a biologically interpretable low-dimensional pathway activity matrix. PathActMarker is capable of ranking pathways based on the pathway activity matrices, and top-ranked pathways are identified as biomarkers.
DOI: 10.1007/s11704-024-40420-y