Search for: All records

Abstract Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method’s practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development. 

Advances in single-cell RNA sequencing (scRNAseq) technologies have allowed us to study the heterogeneity of cell populations. The cell compositions of tissues from different hosts may vary greatly, indicating the condition of the hosts, from which the samples are collected. However, the high sequencing cost and the lack of fresh tissues make single-cell approaches less appealing. In many cases, it is practically impossible to generate single-cell data in a large number of subjects, making it challenging to monitor changes in cell type compositions in various diseases. Here we introduce a novel approach, named Deconvolution using Weighted Elastic Net (DWEN), that allows researchers to accurately estimate the cell type compositions from bulk data samples without the need of generating single-cell data. It also allows for the re-analysis of bulk data collected from rare conditions to extract more in-depth cell-type level insights. The approach consists of two modules. The first module constructs the cell type signature matrix from single-cell data while the second module estimates the cell type compositions of input bulk samples. In an extensive analysis using 20 datasets generated from scRNA-seq data of different human tissues, we demonstrate that DWEN outperforms current state-of-the-arts in estimating cell type compositions of bulk samples.