scholarly journals Computational functional genomics-based reduction of disease-related gene sets to their key components

2018 ◽  
Vol 35 (14) ◽  
pp. 2362-2370 ◽  
Author(s):  
Catharina Lippmann ◽  
Alfred Ultsch ◽  
Jörn Lötsch
Keyword(s):  
Functional Genomics ◽  
Directed Acyclic Graph ◽  
R Package ◽  
Quantitative Information ◽  
Knowledge Bases ◽  
Supplementary Information ◽  
Biological Processes ◽  
Gene Set ◽  
Gene Sets ◽  
Disease Related Gene

Abstract Motivation The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number of candidates. Although expression based methods of gene set reduction are applied to laboratory-derived genetic data, the analysis of topical sets of genes gathered from knowledge bases requires a modified approach as no quantitative information about gene expression is available. Results We propose a computational functional genomics-based approach at reducing sets of genes to the most relevant items based on the importance of the gene within the polyhierarchy of biological processes characterizing the disease. Knowledge bases about the biological roles of genes can provide a valid description of traits or diseases represented as a directed acyclic graph (DAG) picturing the polyhierarchy of disease relevant biological processes. The proposed method uses a gene importance score derived from the location of the gene-related biological processes in the DAG. It attempts to recreate the DAG and thereby, the roles of the original gene set, with the least number of genes in descending order of importance. This obtained precision and recall of over 70% to recreate the components of the DAG charactering the biological functions of n=540 genes relevant to pain with a subset of only the k=29 best-scoring genes. Conclusions A new method for reduction of gene sets is shown that is able to reproduce the biological processes in which the full gene set is involved by over 70%; however, by using only ∼5% of the original genes. Availability and implementation The necessary numerical parameters for the calculation of gene importance are implemented in the R package dbtORA at https://github.com/IME-TMP-FFM/dbtORA. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ADAGE signature analysis: differential expression analysis with data-defined gene sets

2017 ◽  
Author(s):  
Jie Tan ◽  
Matthew Huyck ◽  
Dongbo Hu ◽  
René A. Zelaya ◽  
Deborah A. Hogan ◽  
...  
Keyword(s):  
Differential Expression ◽  
Web Server ◽  
R Package ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Signature Analysis ◽  
Biological Processes ◽  
Gene Set ◽  
Gene Sets ◽  
Public Data

AbstractBackgroundGene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data.ResultsHere we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server (http://adage.greenelab.com) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and Δanr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr.ConclusionsWe designed ADAGE signature analysis to perform gene set analysis using data-defined functional gene signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE signature analysis to the community.

scholarly journals TFEA.ChIP: a tool kit for transcription factor binding site enrichment analysis capitalizing on ChIP-seq datasets

2019 ◽  
Vol 35 (24) ◽  
pp. 5339-5340 ◽  
Author(s):  
Laura Puente-Santamaria ◽  
Wyeth W Wasserman ◽  
Luis del Peso
Keyword(s):  
Genomic Analysis ◽  
Enrichment Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Web Based ◽  
Factor Binding Site ◽  
Gene Sets ◽  
Transcription Regulators ◽  
Computational Identification ◽  
On Chip

Abstract Summary The computational identification of the transcription factors (TFs) [more generally, transcription regulators, (TR)] responsible for the co-regulation of a specific set of genes is a common problem found in genomic analysis. Herein, we describe TFEA.ChIP, a tool that makes use of ChIP-seq datasets to estimate and visualize TR enrichment in gene lists representing transcriptional profiles. We validated TFEA.ChIP using a wide variety of gene sets representing signatures of genetic and chemical perturbations as input and found that the relevant TR was correctly identified in 126 of a total of 174 analyzed. Comparison with other TR enrichment tools demonstrates that TFEA.ChIP is an highly customizable package with an outstanding performance. Availability and implementation TFEA.ChIP is implemented as an R package available at Bioconductor https://www.bioconductor.org/packages/devel/bioc/html/TFEA.ChIP.html and github https://github.com/LauraPS1/TFEA.ChIP_downloads. A web-based GUI to the package is also available at https://www.iib.uam.es/TFEA.ChIP/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Gene set enrichment for reproducible science: comparison of CERNO and eight other algorithms

2019 ◽  
Vol 35 (24) ◽  
pp. 5146-5154 ◽  
Author(s):  
Joanna Zyla ◽  
Michal Marczyk ◽  
Teresa Domaszewska ◽  
Stefan H E Kaufmann ◽  
Joanna Polanska ◽  
...  
Keyword(s):  
False Positive Rate ◽  
R Package ◽  
Supplementary Information ◽  
Computational Time ◽  
P Value ◽  
Gene Set ◽  
Related Data ◽  
Novel Approach ◽  
Positive Rate ◽  
Real World Datasets

Abstract Motivation Analysis of gene set (GS) enrichment is an essential part of functional omics studies. Here, we complement the established evaluation metrics of GS enrichment algorithms with a novel approach to assess the practical reproducibility of scientific results obtained from GS enrichment tests when applied to related data from different studies. Results We evaluated eight established and one novel algorithm for reproducibility, sensitivity, prioritization, false positive rate and computational time. In addition to eight established algorithms, we also included Coincident Extreme Ranks in Numerical Observations (CERNO), a flexible and fast algorithm based on modified Fisher P-value integration. Using real-world datasets, we demonstrate that CERNO is robust to ranking metrics, as well as sample and GS size. CERNO had the highest reproducibility while remaining sensitive, specific and fast. In the overall ranking Pathway Analysis with Down-weighting of Overlapping Genes, CERNO and over-representation analysis performed best, while CERNO and GeneSetTest scored high in terms of reproducibility. Availability and implementation tmod package implementing the CERNO algorithm is available from CRAN (cran.r-project.org/web/packages/tmod/index.html) and an online implementation can be found at http://tmod.online/. The datasets analyzed in this study are widely available in the KEGGdzPathwaysGEO, KEGGandMetacoreDzPathwaysGEO R package and GEO repository. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PhenoExam: an R package and Web application for the examination of phenotypes linked to genes and gene sets

2021 ◽  
Author(s):  
Alejandro Cisterna García ◽  
Aurora González-Vidal ◽  
Daniel Ruiz Villa ◽  
Jordi Ortiz Murillo ◽  
Alicia Gómez-Pascual ◽  
...  
Keyword(s):  
Web Application ◽  
Enrichment Analysis ◽  
R Package ◽  
Web Interface ◽  
Gene Set ◽  
New Genes ◽  
Gene Sets ◽  
Phenotype Analysis ◽  
New Gene ◽  
Early Onset Parkinson’S Disease

Gene set based phenotype enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) can improve the rate of genetic diagnoses amongst other research purposes. To facilitate diverse phenotype analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases. PhenoExam achieves these tasks by integrating databases or resources such as the HPO, MGD, CRISPRbrain, CTD, ClinGen, CGI, OrphaNET, UniProt, PsyGeNET, and Genomics England Panel App. PhenoExam accepts both human and mouse genes as input. We developed PhenoExam to assist a variety of users, including clinicians, computational biologists and geneticists. It can be used to support the validation of new gene-to-disease discoveries, and in the detection of differential phenotypes between two gene sets (a phenotype linked to one of the gene set but no to the other) that are useful for differential diagnosis and to improve genetic panels. We validated PhenoExam performance through simulations and its application to real cases. We demonstrate that PhenoExam is effective in distinguishing gene sets or Mendelian diseases with very similar phenotypes through projecting the disease-causing genes into their annotation-based phenotypic spaces. We also tested the tool with early onset Parkinson's disease and dystonia genes, to show phenotype-level similarities but also potentially interesting differences. More specifically, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy. Therefore, PhenoExam effectively discovers links between phenotypic terms across annotation databases through effective integration. The R package is available at https://github.com/alexcis95/PhenoExam and the Web tool is accessible at https://snca.atica.um.es/PhenoExamWeb/.

Path2Surv: Pathway/gene set-based survival analysis using multiple kernel learning

2019 ◽  
Vol 35 (24) ◽  
pp. 5137-5145 ◽  
Author(s):  
Onur Dereli ◽  
Ceyda Oğuz ◽  
Mehmet Gönen
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Supplementary Information ◽  
Support Vector ◽  
Gene Set ◽  
Multiple Kernel ◽  
Pathway Gene ◽  
Gene Sets

Abstract Motivation Survival analysis methods that integrate pathways/gene sets into their learning model could identify molecular mechanisms that determine survival characteristics of patients. Rather than first picking the predictive pathways/gene sets from a given collection and then training a predictive model on the subset of genomic features mapped to these selected pathways/gene sets, we developed a novel machine learning algorithm (Path2Surv) that conjointly performs these two steps using multiple kernel learning. Results We extensively tested our Path2Surv algorithm on 7655 patients from 20 cancer types using cancer-specific pathway/gene set collections and gene expression profiles of these patients. Path2Surv statistically significantly outperformed survival random forest (RF) on 12 out of 20 datasets and obtained comparable predictive performance against survival support vector machine (SVM) using significantly fewer gene expression features (i.e. less than 10% of what survival RF and survival SVM used). Availability and implementation Our implementations of survival SVM and Path2Surv algorithms in R are available at https://github.com/mehmetgonen/path2surv together with the scripts that replicate the reported experiments. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals i2d: an R package for simulating data from images and the implications in biomedical research

2020 ◽  
Author(s):  
Xiaoyu Liang ◽  
Ying Hu ◽  
Chunhua Yan ◽  
Ke Xu
Keyword(s):  
Gene Networks ◽  
Simulated Data ◽  
R Package ◽  
Quantitative Information ◽  
Human Vision ◽  
Supplementary Information ◽  
Biological Research ◽  
Limited Capacity ◽  
Complex Information ◽  
Quality Imaging

Abstract Motivation High-quality imaging analyses have been proposed to drive innovation in biomedical and biological research. However, the application of images remains underexploited because of the limited capacity of human vision and the challenges in extracting quantitative information from images. Computationally extracting quantitative information from images is critical to overcoming this limitation. Here, we present a novel R package, i2d, to simulate data from an image based on digital convolution. Results The R package i2d allows users to transform an image into a simulated dataset that can be used to extract and analyze complex information in biomedical and biological research. The package also includes three novel and efficient methods for graph clustering based on simulated data, which can be used to dissect complex gene networks into sub-clusters that have similar biological functions. Availability and implementation The code, the documentation, a tutorial and example data are available on an open source at: github.com/XiaoyuLiang/i2d. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ClusterMine: a Knowledge-integrated Clustering Approach based on Expression Profiles of Gene Sets

2018 ◽  
Author(s):  
Hong-Dong Li ◽  
Yunpei Xu ◽  
Xiaoshu Zhu ◽  
Quan Liu ◽  
Gilbert S. Omenn ◽  
...  
Keyword(s):  
Expression Profiles ◽  
R Package ◽  
Biological Data ◽  
Supplementary Information ◽  
Consensus Clustering ◽  
Cluster Membership ◽  
Link Type ◽  
Novel Approach ◽  
Gene Sets ◽  
Biological Interpretation

ABSTRACTMotivationClustering analysis is essential for understanding complex biological data. In widely used methods such as hierarchical clustering (HC) and consensus clustering (CC), expression profiles of all genes are often used to assess similarity between samples for clustering. These methods output sample clusters, but are not able to provide information about which gene sets (functions) contribute most to the clustering. So interpretability of their results is limited. We hypothesized that integrating prior knowledge of annotated biological processes would not only achieve satisfying clustering performance but also, more importantly, enable potential biological interpretation of clusters.ResultsHere we report ClusterMine, a novel approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets, e.g., in Gene Ontology. In addition to outputting cluster membership of each sample as conventional approaches do, it outputs gene sets that are most likely to contribute to the clustering, a feature facilitating biological interpretation. Using three cancer datasets, two single cell RNA-sequencing based cell differentiation datasets, one cell cycle dataset and two datasets of cells of different tissue origins, we found that ClusterMine achieved similar or better clustering performance and that top-scored gene sets prioritized by ClusterMine are biologically relevant.Implementation and availabilityClusterMine is implemented as an R package and is freely available at: www.genemine.org/[email protected] InformationSupplementary data are available at Bioinformatics online.

scholarly journals MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations

2020 ◽  
Author(s):  
Sisheng Liu ◽  
Jinpeng Liu ◽  
Yanqi Xie ◽  
Tingting Zhai ◽  
Eugene W Hinderer ◽  
...  
Keyword(s):  
Mutation Rate ◽  
De Novo ◽  
R Package ◽  
Supplementary Information ◽  
Driver Mutations ◽  
Mutual Exclusivity ◽  
Statistical Framework ◽  
Gene Sets ◽  
Genome Wide ◽  
Background Mutation Rate

Abstract Motivation Cancer somatic driver mutations associated with genes within a pathway often show a mutually exclusive pattern across a cohort of patients. This mutually exclusive mutational signal has been frequently used to distinguish driver from passenger mutations and to investigate relationships among driver mutations. Current methods for de novo discovery of mutually exclusive mutational patterns are limited because the heterogeneity in background mutation rate can confound mutational patterns, and the presence of highly mutated genes can lead to spurious patterns. In addition, most methods only focus on a limited number of pre-selected genes and are unable to perform genome-wide analysis due to computational inefficiency. Results We introduce a statistical framework, MEScan, for accurate and efficient mutual exclusivity analysis at the genomic scale. Our framework contains a fast and powerful statistical test for mutual exclusivity with adjustment of the background mutation rate and impact of highly mutated genes, and a multi-step procedure for genome-wide screening with the control of false discovery rate. We demonstrate that MEScan more accurately identifies mutually exclusive gene sets than existing methods and is at least two orders of magnitude faster than most methods. By applying MEScan to data from four different cancer types and pan-cancer, we have identified several biologically meaningful mutually exclusive gene sets. Availability and implementation MEScan is available as an R package at https://github.com/MarkeyBBSRF/MEScan. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A method for downstream analysis of gene set enrichment results facilitates the biological interpretation of vaccine efficacy studies

2016 ◽  
Author(s):  
Yan Tan ◽  
Jernej Godec ◽  
Felix Wu ◽  
Pablo Tamayo ◽  
Jill P. Mesirov ◽  
...  
Keyword(s):  
Transcriptional Response ◽  
Leading Edge ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Biological Processes ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
Biological Interpretation ◽  
Downstream Analysis

AbstractGene set enrichment analysis (GSEA) is a widely employed method for analyzing gene expression profiles. The approach uses annotated sets of genes, identifies those that are coordinately up‐ or down-regulated in a biological comparison of interest, and thereby elucidates underlying biological processes relevant to the comparison. As the number of gene sets available in various collections for enrichment analysis has grown, the resulting lists of significant differentially regulated gene sets may also become larger, leading to the need for additional downstream analysis of GSEA results. Here we present a method that allows the rapid identification of a small number of co-regulated groups of genes – “leading edge metagenes” (LEMs) - from high scoring sets in GSEA results. LEM are sub-signatures which are common to multiple gene sets and that “explain” their enrichment specific to the experimental dataset of interest. We show that LEMs contain more refined lists of context-dependent and biologically meaningful genes than the parental gene sets. LEM analysis of the human vaccine response using a large database of immune signatures identified core biological processes induced by five different vaccines in datasets from human peripheral blood mononuclear cells (PBMC). Further study of these biological processes over time following vaccination showed that at day 3 post-vaccination, vaccines derived from viruses or viral subunits exhibit patterns of biological processes that are distinct from protein conjugate vaccines; however, by day 7 these differences were less pronounced. This suggests that the immune response to diverse vaccines eventually converge to a common transcriptional response. LEM analysis can significantly reduce the dimensionality of enriched gene sets, improve the identification of core biological processes active in a comparison of interest, and simplify the biological interpretation of GSEA results.Author SummaryGenome-wide expression profiling is a widely used tool to identify biological mechanisms in a comparison of interest. One analytic method, Gene set enrichment analysis (GSEA) uses annotated sets of genes and identifies those that are coordinately up‐ or down-regulated in a biological comparison of interest. This approach capitalizes on the fact that alternations in biological processes often cause the coordinated change of a large number of genes. However, as the number of gene sets available in various collections for enrichment analysis has grown, the resulting lists of significant differentially regulated gene sets may also become larger, leading to the need for additional downstream analysis of GSEA results. Here we present a method that allows the identification of a small number of co-regulated groups of genes – “leading edge metagenes” (LEMs) – from high scoring sets in GSEA results. We show that LEMs contain more refined lists of context-dependent biologically meaningful genes than the parental gene sets and demonstrate the utility of this approach in analyzing the transcriptional response to vaccination. LEM analysis can significantly reduce the dimensionality of enriched gene sets, improve the identification of core biological processes active in a comparison of interest, and facilitate the biological interpretation of GSEA results.

scholarly journals Differential Gene Set Enrichment Analysis: a statistical approach to quantify the relative enrichment of two gene sets

2020 ◽  
Author(s):  
James H Joly ◽  
William E Lowry ◽  
Nicholas A Graham
Keyword(s):  
Synthetic Data ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Supplementary Information ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Transcriptomic Data ◽  
Relative Enrichment ◽  
Gene Sets ◽  
Differential Gene

Abstract Motivation Gene Set Enrichment Analysis (GSEA) is an algorithm widely used to identify statistically enriched gene sets in transcriptomic data. However, GSEA cannot examine the enrichment of two gene sets or pathways relative to one another. Here we present Differential Gene Set Enrichment Analysis (DGSEA), an adaptation of GSEA that quantifies the relative enrichment of two gene sets. Results After validating the method using synthetic data, we demonstrate that DGSEA accurately captures the hypoxia-induced coordinated upregulation of glycolysis and downregulation of oxidative phosphorylation. We also show that DGSEA is more predictive than GSEA of the metabolic state of cancer cell lines, including lactate secretion and intracellular concentrations of lactate and AMP. Finally, we demonstrate the application of DGSEA to generate hypotheses about differential metabolic pathway activity in cellular senescence. Together, these data demonstrate that DGSEA is a novel tool to examine the relative enrichment of gene sets in transcriptomic data. Availability and implementation DGSEA software and tutorials are available at https://jamesjoly.github.io/DGSEA/. Supplementary information Supplementary data are available at Bioinformatics online.

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