The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

Mubeen, Sarah; Hoyt, Charles Tapley; Gemünd, André; Hofmann-Apitius, Martin; Fröhlich, Holger; Domingo‐Fernándéz, Daniel

doi:10.1101/654442

Cited by 4 publications

(4 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…KEGG, wikiPathways), Gene Ontology to investigate molecular functions, or more detailed about biological domains (PANTHER, MSigDB). Moreover, some solutions to merge generalist pathways databases have shown good results, and it would be interesting to implement in this workflow, such as MPath[22] where KEGG, Reactome and wikiPathways are merged toward better covering of biological pathways.…”

Section: Resultsmentioning

confidence: 99%

Towards an integrative multi-omics workflow

Jeanneret

Gazut

2021

Preprint

View full text Add to dashboard Cite

The advent of high-throughput techniques has greatly enhanced biological discovery. Last years, analysis of multi-omics data has taken the front seat to improve physiological understanding. Handling functional enrichment results from various biological data raises practical questions. We propose an integrative workflow to better interpret biological process insights in a multi-omics approach applied to breast cancer data from The Cancer Genome Atlas (TCGA) related to Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC). Pathway enrichment by Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) has been conducted with both features information from differential expression analysis or selected features from multi-block sPLS-DA methods. Then, comprehensive comparisons of enrichment results have been carried out by looking at classical enrichment analysis, probabilities pooling by Stouffer's Z scores method and pathways clustering in biological themes. Our work shows that ORA enrichment with selected sPLS-DA features and pathways probabilities pooling by Stouffer's method lead to enrichment maps highly associated to physiological knowledge of IDC or ILC phenotypes, better than ORA and GSEA with differential expression driven features.

show abstract

Section: Resultsmentioning

confidence: 99%

Towards an integrative multi-omics workflow

Jeanneret

Gazut

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The gene set enrichment analysis highlighted cytokine, interleukin, and toll-like receptor signalling pathways that are involved in regulating various aspects of innate and adaptive immune responses (90). Such results may be compromised when other pathway databases such as KEGG (91) and WikiPathways (92) are employed, as the relevant pathways and molecular interactions in the pathways are different from Reactome (93,94). To circumvent this issue, one possibility could be to extract the overlapped networks between the different databases of pathways; however, this is not always possible due to the differences in annotation of genes and…”

Section: Discussionmentioning

confidence: 99%

Network and systems based re-engineering of dendritic cells with non-coding RNAs for cancer immunotherapy

Lai

Dreyer

Cantone

et al. 2020

Preprint

View full text Add to dashboard Cite

Dendritic cells (DCs) are professional antigen-presenting cells that induce and regulate adaptive immunity by presenting antigens to T cells. Due to their coordinative role in adaptive immune responses, DCs have been used as cell-based therapeutic vaccination against cancer. The capacity of DCs to induce a therapeutic immune response can be enhanced by re-wiring of cellular signalling pathways with microRNAs (miRNAs). Since the activation and maturation of DCs is controlled by an interconnected signalling network, we deploy an approach that combines RNA sequencing data and systems biology methods to delineate miRNA-based strategies that enhance DC-elicited immune responses.Through RNA sequencing of IKKβ-matured DCs that are currently being tested in a clinical trial on therapeutic anti-cancer vaccination, we identified 44 differentially expressed miRNAs. According to a network analysis, most of these miRNAs regulate targets that are linked to immune pathways, such as cytokine and interleukin signalling. We employed a network topology-oriented scoring model to rank the miRNAs, analysed their impact on immunogenic potency of DCs, and identified dozens of promising miRNA candidates with miR-15a and miR-16 as the top ones. The results of our analysis are incorporated in a database which constitutes a tool to identify DC-relevant miRNA-gene interactions with therapeutic potential (www.synmirapy.net/dc-optimization).

show abstract

“…Moreover, database redundancy and disagreement are not limited to collections of gene sets, e.g., [18,19]. The impact of the choice of databases has been tackled for specific applications on cancer development in Mubeen et al [20]. In contrast to other redundancy reduction methods, we aim to rank the original gene sets in the collections using Shapley values, thus relying on theoretical properties of fair allocation of resources.…”

Section: Related Workmentioning

confidence: 99%

Redundancy-aware unsupervised ranking based on game theory: Ranking pathways in collections of gene sets

et al. 2023

View full text Add to dashboard Cite

In Genetics, gene sets are grouped in collections concerning their biological function. This often leads to high-dimensional, overlapping, and redundant families of sets, thus precluding a straightforward interpretation of their biological meaning. In Data Mining, it is often argued that techniques to reduce the dimensionality of data could increase the maneuverability and consequently the interpretability of large data. In the past years, moreover, we witnessed an increasing consciousness of the importance of understanding data and interpretable models in the machine learning and bioinformatics communities. On the one hand, there exist techniques aiming to aggregate overlapping gene sets to create larger pathways. While these methods could partly solve the large size of the collections’ problem, modifying biological pathways is hardly justifiable in this biological context. On the other hand, the representation methods to increase interpretability of collections of gene sets that have been proposed so far have proved to be insufficient. Inspired by this Bioinformatics context, we propose a method to rank sets within a family of sets based on the distribution of the singletons and their size. We obtain sets’ importance scores by computing Shapley values; Making use of microarray games, we do not incur the typical exponential computational complexity. Moreover, we address the challenge of constructing redundancy-aware rankings where, in our case, redundancy is a quantity proportional to the size of intersections among the sets in the collections. We use the obtained rankings to reduce the dimension of the families, therefore showing lower redundancy among sets while still preserving a high coverage of their elements. We finally evaluate our approach for collections of gene sets and apply Gene Sets Enrichment Analysis techniques to the now smaller collections: As expected, the unsupervised nature of the proposed rankings allows for unremarkable differences in the number of significant gene sets for specific phenotypic traits. In contrast, the number of performed statistical tests can be drastically reduced. The proposed rankings show a practical utility in bioinformatics to increase interpretability of the collections of gene sets and a step forward to include redundancy-awareness into Shapley values computations.

show abstract

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

Cited by 4 publications

References 48 publications

Towards an integrative multi-omics workflow

Towards an integrative multi-omics workflow

Network and systems based re-engineering of dendritic cells with non-coding RNAs for cancer immunotherapy

Redundancy-aware unsupervised ranking based on game theory: Ranking pathways in collections of gene sets

Contact Info

Product

Resources

About