In recent years, we observe a rapid growth of technology leading to the development of new measurement devices used in molecular biology. With the introduction of microarrays, it has become possible to measure the expression of thousands of genes in multiple samples and cells quickly. This allows for gaining insight into the biological underpinnings of phenotype variability. One of the leading steps in the bioinformatical analysis of high-throughput molecular biology data is investigating collections of gene sets (e.g. biological pathways) called gene set enrichment analysis or pathway analysis. Early methods for associating gene sets with phenotype changes between experimental conditions identified first a small pool of potentially relevant genes. In newer, ranking approaches, all genes were taken into consideration, with some measure given from statistical testing of the phenotype. The latest methods also consider gene-gene interactions in pathways. In parallel, algorithms known as single-sample approaches were developed, which allow the investigation of the heterogeneity of individual samples.
The primary aim of pathway analysis is to identify the enrichment or depletion of expression levels of a given set of genes that relate to a particular biological function. The transformation of information from the gene level to the gene set level reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Despite many different solutions, new sequencing technologies bring new challenges, e.g. high dropout rate in single-cell RNA sequencing. Moreover, pathway analysis in metabolomics or genome-wide association studies (GWAS) has other, technology-specific problems. In this research topic, we aim to gather original research papers that can provide guidelines for pathway analysis in various omics data and introduce new methods for pathway analysis.
This Research Topic is focused on the new computational methods of pathway analysis as well as their application, especially in the new sequencing technologies, to improve gaining of the biological insight. Topics of interest include, but are not limited to:
- Novel methods for pathway analysis in molecular biology (single- or multiomic)
- Pathway analysis methods for investigation of individual sample heterogeneity
- Complex network analysis and its integration into enrichment analysis
- Various applications of enrichment methods in the analysis of high-throughput biological data
- Methods and benchmarks for comparison of pathway analysis methods by their effectiveness
- Novel gene set collection databases and gene set extraction methods
In recent years, we observe a rapid growth of technology leading to the development of new measurement devices used in molecular biology. With the introduction of microarrays, it has become possible to measure the expression of thousands of genes in multiple samples and cells quickly. This allows for gaining insight into the biological underpinnings of phenotype variability. One of the leading steps in the bioinformatical analysis of high-throughput molecular biology data is investigating collections of gene sets (e.g. biological pathways) called gene set enrichment analysis or pathway analysis. Early methods for associating gene sets with phenotype changes between experimental conditions identified first a small pool of potentially relevant genes. In newer, ranking approaches, all genes were taken into consideration, with some measure given from statistical testing of the phenotype. The latest methods also consider gene-gene interactions in pathways. In parallel, algorithms known as single-sample approaches were developed, which allow the investigation of the heterogeneity of individual samples.
The primary aim of pathway analysis is to identify the enrichment or depletion of expression levels of a given set of genes that relate to a particular biological function. The transformation of information from the gene level to the gene set level reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Despite many different solutions, new sequencing technologies bring new challenges, e.g. high dropout rate in single-cell RNA sequencing. Moreover, pathway analysis in metabolomics or genome-wide association studies (GWAS) has other, technology-specific problems. In this research topic, we aim to gather original research papers that can provide guidelines for pathway analysis in various omics data and introduce new methods for pathway analysis.
This Research Topic is focused on the new computational methods of pathway analysis as well as their application, especially in the new sequencing technologies, to improve gaining of the biological insight. Topics of interest include, but are not limited to:
- Novel methods for pathway analysis in molecular biology (single- or multiomic)
- Pathway analysis methods for investigation of individual sample heterogeneity
- Complex network analysis and its integration into enrichment analysis
- Various applications of enrichment methods in the analysis of high-throughput biological data
- Methods and benchmarks for comparison of pathway analysis methods by their effectiveness
- Novel gene set collection databases and gene set extraction methods
