Enhancing gene set enrichment using networks.

Longitudinal gene expression profiles of subjects are collected in some clinical studies to monitor disease progression and understand disease etiology. The identification of gene sets that have coordinated changes with relevant clinical outcomes over time from these data could provide significant insights into the molecular basis of disease progression and lead to better treatments. In this article, we propose a Distance-Correlation based Gene Set Analysis (dcGSA) method for longitudinal gene expression data. dcGSA is a non-parametric approach, statistically robust, and can capture both linear and nonlinear relationships between gene sets and clinical outcomes. In addition, dcGSA is able to identify related gene sets in cases where the effects of gene sets on clinical outcomes differ across subjects due to the subject heterogeneity, remove the confounding effects of some unobserved time-invariant covariates, and allow the assessment of associations between gene sets and multiple related outcomes simultaneously. Through extensive simulation studies, we demonstrate that dcGSA is more powerful of detecting relevant genes than other commonly used gene set analysis methods. When dcGSA is applied to a real dataset on systemic lupus erythematosus, we are able to identify more disease related gene sets than other methods.

Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify potential biological insights on the gene set level. However, platforms exploring cancer multi-omics data using GSA methods are lacking. In this study, we upgraded our GSCALite to GSCA (gene set cancer analysis, http://bioinfo.life.hust.edu.cn/GSCA) for cancer GSA at genomic, pharmacogenomic and immunogenomic levels. In this improved GSCA, we integrated expression, mutation, drug sensitivity and clinical data from four public data sources for 33 cancer types. We introduced useful features to GSCA, including associations between immune infiltration with gene expression and genomic variations, and associations between gene set expression/mutation and clinical outcomes. GSCA has four main functional modules for cancer GSA to explore, analyze and visualize expression, genomic variations, tumor immune infiltration, drug sensitivity and their associations with clinical outcomes. We used case studies of three gene sets: (i) seven cell cycle genes, (ii) tumor suppressor genes of PI3K pathway and (iii) oncogenes of PI3K pathway to prove the advantage of GSCA over single gene analysis. We found novel associations of gene set expression and mutation with clinical outcomes in different cancer types on gene set level, while on single gene analysis level, they are not significant associations. In conclusion, GSCA is a user-friendly web server and a useful resource for conducting hypothesis tests by using GSA methods at genomic, pharmacogenomic and immunogenomic levels.

The focus of analyzing data from microarray experiments has shifted from the identification of associated individual genes to that of associated biological pathways or gene sets. In bioinformatics, a feature selection algorithm is usually used to cope with the high dimensionality of microarray data. In addition to those algorithms that use the biological information contained within a gene set as a priori to facilitate the process of feature selection, various gene set analysis methods can be applied directly or modified readily for the purpose of feature selection. Significance analysis of microarray to gene-set reduction analysis (SAM-GSR) algorithm, a novel direction of gene set analysis, is one of such methods. Here, we explore the feature selection property of SAM-GSR and provide a modification to better achieve the goal of feature selection. In a multiple sclerosis (MS) microarray data application, both SAM-GSR and our modification of SAM-GSR perform well. Our results show that SAM-GSR can carry out feature selection indeed, and modified SAM-GSR outperforms SAM-GSR. Given pathway information is far from completeness, a statistical method capable of constructing biologically meaningful gene networks is of interest. Consequently, both SAM-GSR algorithms will be continuously revaluated in our future work, and thus better characterized.

To date, many gene set analysis (GSA) approaches have been developed for identifying differentially expressed gene sets using microarray data. However, these methods are not directly applicable to RNA-Seq data due to intrinsic difference between two data structures. When testing the differential expression of gene sets, there is a critical assumption that the members in each gene set are sampled independently in most GSA methods. It means that the genes within a gene set don’t share a common biological function. The aim of this paper is twofold. First, we propose a powerful yet simple extension to GSA methods based on the de-correlation (DECO) algorithm that properly remove the correlation bias in the expression of each gene set. We then study the performance of our proposed method compared with other GSA methods through a real RNA-Seq dataset and simulation studies under various scenarios combining with four commonly used normalization methods. Second, we discuss the effect of the complex correlation structure of gene sets on four normalization methods. As a result, we found that our proposed method outperforms the others in terms of Type I error rate and empirical power. A comparative study on a public data showed that gene sets identified by our proposed method have better concordance with biological confirmed pathways than other methods.

Gene-set analysis (GSA) aims to identify sets of differentially expressed genes by a phenotype in DNA microarray studies. Challenges occur due to the salient characteristics of the data: (1) the number of genes is far larger than the number of observations; (2) gene expression measurements, especially within each gene set, can be highly correlated; and (3) the number of gene sets that can be examined is large and increasing rapidly. These challenges call for gene-set testing procedures that have both efficiency in computation for large GSAs and high power in the presence of the high correlation. We propose a new GSA approach called Linear Combination Test (LCT), incorporating the covariance matrix estimator of gene expression into the test statistic. The proposed LCT and two other GSA methods, a modification of Hotelling's T2 using a shrinkage covariance matrix and our SAM-GS (Dinu et. al. 2007), the two methods that have been reported by Tsai and Chen (2009) to perform best in terms of power, are evaluated in simulation studies and a real microarray study. The LCT method is more computationally efficient than the modified Hotelling's T2 and approximates the superb power of the modified Hotelling's T2. LCT is slightly faster than SAM-GS, but more powerful, due to incorporating the covariance matrix estimator. An extra step to enhance the interpretation of GSA results is also proposed in the form of a hierarchical LC (HLC) testing procedure, providing scientists useful hierarchical information on gene sets that LCT identified as differentially expressed.

Gene set analysis has been widely used to gain insight from high-throughput expression studies. Although various tools and methods have been developed for gene set analysis, there is no consensus among researchers regarding best practice(s). Most often, evaluation studies have reported contradictory recommendations of which methods are superior. Therefore, an unbiased quantitative framework for evaluations of gene set analysis methods will be valuable. Such a framework requires gene expression datasets where enrichment status of gene sets is known a priori. In the absence of such gold standard datasets, artificial datasets are commonly used for evaluations of gene set analysis methods; however, they often rely on oversimplifying assumptions that make them biased in favor of or against a given method. In this paper, we propose a quantitative framework for evaluation of gene set analysis methods by synthesizing expression datasets using real data, without relying on oversimplifying or unrealistic assumptions, while preserving complex gene–gene correlations and retaining the distribution of expression values. The utility of the quantitative approach is shown by evaluating ten widely used gene set analysis methods. An implementation of the proposed method is publicly available. We suggest using Silver to evaluate existing and new gene set analysis methods. Evaluation using Silver provides a better understanding of current methods and can aid in the development of gene set analysis methods to achieve higher specificity without sacrificing sensitivity.

In recent years, RNA-seq has become a very competitive alternative to microarrays. In RNA-seq experiments, the expected read count for a gene is proportional to its expression level multiplied by its transcript length. Even when two genes are expressed at the same level, differences in length will yield differing numbers of total reads. The characteristics of these RNA-seq experiments create a gene-level bias such that the proportion of significantly differentially expressed genes increases with the transcript length, whereas such bias is not present in microarray data. Gene-set analysis seeks to identify the gene sets that are enriched in the list of the identified significant genes. In the gene-set analysis of RNA-seq, the gene-level bias subsequently yields the gene-set-level bias that a gene set with genes of long length will be more likely to show up as enriched than will a gene set with genes of shorter length. Because gene expression is not related to its transcript length, any gene set containing long genes is not of biologically greater interest than gene sets with shorter genes. Accordingly the gene-set-level bias should be removed to accurately calculate the statistical significance of each gene-set enrichment in the RNA-seq.We present a new gene set analysis method of RNA-seq, called FDRseq, which can accurately calculate the statistical significance of a gene-set enrichment score by the grouped false-discovery rate. Numerical examples indicated that FDRseq is appropriate for controlling the transcript length bias in the gene-set analysis of RNA-seq data. To implement FDRseq, we developed the R program, which can be downloaded at no cost from http://home.mju.ac.kr/home/index.action?siteId=tyang.

Background: Bone metastasis-specific genes in breast cancer have been reported without considering the significant differences in ER status between bone and non-bone metastases. The aims of this study were to validate genes that had been reported as bone metastasis-specific genes using our data set and to identify bone metastasis-specific genes on the basis of biological differences between ERpositive and ER-negative breast cancers. Patients and Methods: We used Affymetrix GeneChip arrays to analyze tumor samples obtained from 365 primary invasive breast cancer patients who underwent surgery from 1999 to 2008. We excluded patients with HER2−positive breast cancer (normalized HER2 mRNA expression [probe set 216836_s_at] > 12.54). We classified the samples into 3 cohorts according to first metastatic site: bone, non-bone, or no metastasis. Differential expression of genes between bone and non-bone cohorts that were differentially expressed was identified using the Cox proportional hazards model, and gene sets was assessed using gene-set analysis. Results: Of the 365 patients, 34 (9.3%) were included in the bone cohort and 32 (8.8%) in the non-bone cohort. Two hundred fourteen (58.6%) had ER-positive and 151 (41.4%) had ER-negative breast cancer. First, we performed gene-set analysis using 5 gene sets that had been reported to be associated with bone metastasis. One gene set, which had been detected using an ER-negative breast cancer cell line, was validated as predicting bone metastasis in ER-positive breast cancer. None of the 5 gene sets predicted bone metastasis in ER-negative breast cancer. We then determined the levels of individual genes associated with bone metastasis by ER status using all 16,712 probe sets filtered by average gene expression level. When we analyzed all patients without any stratification by ER status, as in previous studies, 592 probe sets were significantly overexpressed in the bone cohort compared with the non-bone cohort, with a false discovery rate of ≤0.05. However, when we analyzed ER-positive and ER-negative breast cancers separately, no genes were found with significant differences between bone and non-bone cohorts. Finally, we used 2,246 functionally annotated gene sets assembled from Gene Ontology to examine possible biological differences between bone and non-bone cohorts. In the bone cohort, 151 and 125 gene sets were significantly overexpressed in ER-positive and ER-negative breast cancers, respectively (P ≤ 0.05). Ppathways related to Cellular growth and proliferation, and intracellular and second-messenger signaling were overexpressed in ER-positive breast cancer, whereas pathways related to nuclear receptor and cytokine signaling were overexpressed in ER-negative breast cancer. Most bone-metastasis-related pathways were different in ER-positive and ER-negative breast cancers (91.4% and 89.6% of the gene sets, respectively). Discussion: No genes were found that can predict bone metastasis. ER-positive and ER-negative breast cancers have different biological potentials for bone metastasis. Therefore, we need to assess the prediction model of bone metastasis based on the biological features for each ER status separately. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P4-16-02.

Genome-wide association studies (GWAS) for epithelial ovarian cancer (EOC), the most lethal gynecologic malignancy, have identified novel susceptibility loci. GWAS for survival after EOC have had more limited success. The association of each single-nucleotide polymorphism (SNP) individually may not be well suited to detect small effects of multiple SNPs, such as those operating within the same biologic pathway. Gene set analysis (GSA) overcomes this limitation by assessing overall evidence for association of a phenotype with all measured variation in a set of genes. To determine gene sets associated with EOC overall survival, we conducted GSA using data from two large GWAS (N cases = 2,813, N deaths = 1,116), with a novel Principal Component-Gamma GSA method. Analysis was completed for all cases and then separately for high-grade serous histologic subtype. Analysis of the high-grade serous subjects resulted in 43 gene sets with P < 0.005 (1.7%); of these, 21 gene sets had P < 0.10 in both GWAS, including intracellular signaling pathway (P = 7.3 × 10(-5)) and macrolide binding (P = 6.2 × 10(-4)) gene sets. The top gene sets in analysis of all cases were meiotic mismatch repair (P = 6.3 × 10(-4)) and macrolide binding (P = 1.0 × 10(-3)). Of 18 gene sets with P < 0.005 (0.7%), eight had P < 0.10 in both GWAS. This research detected novel gene sets associated with EOC survival. Novel gene sets associated with EOC survival might lead to new insights and avenues for development of novel therapies for EOC and pharmacogenomic studies.

Using Gene-Set Analysis To Gain Biological Knowledge Based On GWAS Results

Microarray, developed at the end of the last century, is one of the most significant achievements in biotechnology history. One of the most important applications of microarrays is to uncover the signature genes characterizing different samples or biological states. This application was initially used in individual gene analysis, but recently has been applied to gene set analysis. We compare these two approaches and compare the performance using JProGO (Java Tool for the Functional Analysis of Prokaryotic Microarray Data using the Gene Ontology). JProGO is able to examine microarray data from more than 20 different samples of prokaryotic and thus practical analysis of gene behavior data with regards to Gene Ontology (GO) is made possible. Two Escherichia coli datasets are used to compare the performance of the two analyses. The results show that the gene set analysis is more powerful because of its ability to detect the GO terms according to biological expectation. This research will provide a new platform and opportunities for further research or studies in microarray based gene sets.

Functional and genomic context in pathway analysis of GWAS data

The investigation of gene sets identified in experiments and analyses is a common task for biologists. This includes identification of relevant pathway networks, neighborhoods of protein interactions, and associations with drugs and diseases. However, services that provide these analyses face the following challenges: (1) Curated pathway content tends to lack biological context and lag behind recent findings, as it focuses on consensus biology and is expensive to maintain; (2) User interfaces for network analyses need to be transparent and intuitive, without requiring complex parameter choices; and (3) Users have diverse, changing network analysis needs, requiring an evolving portfolio of methods and data. To address these challenges, we present NDEx Integrated Query (IQuery), a novel and flexible resource for gene set analysis providing enrichment, protein-protein interaction, and protein association analyses based on pathways and networks from NDEx, the Network Data Exchange. IQuery provides researchers with a simple “one click” query interface which runs multiple simultaneous queries without the need to specify the analyses to perform, query parameters, or network data sources. The results are aggregated and presented in a rich, intuitive interface for the user to browse, preview, save, or analyze. For each network, the user can view descriptions, citations, author information and other metadata, and inspect the data associated with each node and edge. The IQuery database is NDEx, a public data commons which is constantly growing via the addition of new public network resources and networks submitted by the authors of publications. The incorporation of published networks in IQuery enables timely coverage of new research as well as clear biological context for each pathway, and fosters cross-discipline insights and collaboration between teams. IQuery is also integrated with Cytoscape, the widely used network visualization and analysis application, enabling query result networks to be seamlessly downloaded to Cytoscape for editing, merging with other networks, annotation with user datasets, or use with any of hundreds of community developed apps. IQuery is built as a modular, web service-oriented platform. The primary IQuery service distributes each query to all component services using a standard API. This enables easy addition of new analyses, including those written by collaborators. This allows IQuery to grow in two ways: Through the growth of the NDEx database, and through the addition of modules providing new gene set analysis tools. In sum, IQuery is a powerful and versatile new network analysis service with collaboration and extensibility at its core, which will allow users to find, access, and analyze networks relevant to their research, and provide opportunities for cross-discipline communication and insight. Citation Format: Dexter R. Pratt, Keiichiro Ono, Sophie Liu, Jing Chen, Christopher Churas. IQuery: A tool aggregating multiple gene set analysis methods, based on networks and pathways in a community-driven public data repository [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3206.

Gene set analysis is a mainstay of functional genomics, but it relies on curated databases of gene functions that are incomplete. Here we evaluate five Large Language Models (LLMs) for their ability to discover the common biological functions represented by a gene set, substantiated by supporting rationale, citations and a confidence assessment. Benchmarking against canonical gene sets from the Gene Ontology, GPT-4 confidently recovered the curated name or a more general concept (73% of cases), while benchmarking against random gene sets correctly yielded zero confidence. Gemini-Pro and Mixtral-Instruct showed ability in naming but were falsely confident for random sets, whereas Llama2–70b had poor performance overall. In gene sets derived from ‘omics data, GPT-4 identified novel functions not reported by classical functional enrichment (32% of cases), which independent review indicated were largely verifiable and not hallucinations. The ability to rapidly synthesize common gene functions positions LLMs as valuable ‘omics assistants.

Model studies in mice indicate that the severity of alcohol withdrawal is associated with polymorphic variation and expression of the MPDZ gene. Current knowledge about variation in the human MPDZ gene is limited; however, our data indicate its potential association with alcohol dependence. The multi-PDZ protein is an important part of the N-methyl-D-aspartate (NMDA)-dependent α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor trafficking cascade that controls glutamate-related excitatory neurotransmission. To investigate association of variation in the NMDA-dependent AMPA trafficking cascade with alcohol dependence, we performed a gene-set (pathway) analysis using single nucleotide polymorphism (SNP) data from the Study of Addiction: Genetic and Environment. Rather than testing for association with each SNP individually, which typically has low power to detect small effects of multiple SNPs, gene-set analysis applies a single statistical test to evaluate whether variation in a set of genes is associated with the phenotype of interest. Gene-set analysis of 988 SNPs in 13 genes in the pathway demonstrated a significant association with alcohol dependence, with P < 0.01 for the global effect of variation in this pathway. The statistically significant association of alcohol dependence with genetic variation in the NMDA-dependent AMPA receptor trafficking cascade indicates a need for further investigation of the role of this pathway in alcohol dependence.