Gene set enrichment analysis using linear models and diagnostics

DNA methylation, combined with RNA sequencing, provide novel insight into molecular classification of chordomas and their microenvironment

Chordomas are rare tumors of notochord remnants, occurring mainly in the sacrum and skull base. Despite of their unusually slow growth, chordomas are highly invasive and the involvement of adjacent critical structures causes treatment challenges. Due to the low incidence, the molecular pathogenesis of this entity remains largely unknown. This study aimed to investigate DNA methylation abnormalities and their impact on gene expression profiles in skull base chordomas. 32 tumor and 4 normal nucleus pulposus samples were subjected to DNA methylation and gene expression profiling with methylation microarrays and RNA sequencing. Genome-wide DNA methylation analysis revealed two distinct clusters for chordoma (termed subtypes C and I) with different patterns of aberrant DNA methylation. C Chordomas were characterized by general hypomethylation with hypermethylation of CpG islands, while I chordomas were generally hypermethylated. These differences were reflected by distinct distribution of differentially methylated probes (DMPs). Differentially methylated regions (DMRs) were identified, indicating aberrant methylation in known tumor-related genes in booth chordoma subtypes and regions encoding small RNAs in subtype C chordomas. Correlation between methylation and expression was observed in a minority of genes. Upregulation of TBXT in chordomas appeared to be related to lower methylation of tumor-specific DMR in gene promoter. Gene expression-based clusters of tumor samples did not overlap with DNA methylation-based subtypes. Nevertheless, they differ in transcriptomic profile that shows immune infiltration in I chordomas and up-regulation of cell cycle in C chordomas. Immune enrichment in chordomas I was confirmed with 3 independent deconvolution methods and immunohistochemistry. Copy number analysis showed higher chromosomal instability in C chordomas. Nine out of eight had deletion of CDKN2A/B loci and downregulation of genes encoded in related chromosomal band. No significant difference in patients' survival was observed between tumor subtypes, however, shorter survival was observed in patients with higher number of copy number alterations.

Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data

Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatize the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single-cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to re-purpose and contextualize new cells into a reference cell atlas. Using both simulated and real datasets, we show that scGSEA effectively recapitulates recurrent patterns of pathways' activity shared by cells from different experimental conditions. At the same time, we show that scMAP can reliably map and contextualize new single-cell profiles on a breast cancer atlas we recently released. Both tools are provided in an effective and straightforward workflow providing a framework to determine cell function and significantly improve annotation and interpretation of scRNA-seq data.

Cross-regulome profiling of RNA polymerases highlights the regulatory role of polymerase III on mRNA transcription by maintaining local chromatin architecture

BACKGROUND

Mammalian cells have three types of RNA polymerases (Pols), Pol I, II, and III. However, the extent to which these polymerases are cross-regulated and the underlying mechanisms remain unclear.

RESULTS

We employ genome-wide profiling after acute depletion of Pol I, Pol II, or Pol III to assess cross-regulatory effects between these Pols. We find that these enzymes mainly affect the transcription of their own target genes, while certain genes are transcribed by the other polymerases. Importantly, the most active type of crosstalk is exemplified by the fact that Pol III depletion affects Pol II transcription. Pol II genes with transcription changes upon Pol III depletion are enriched in diverse cellular functions, and Pol III binding sites are found near their promoters. However, these Pol III binding sites do not correspond to transfer RNAs. Moreover, we demonstrate that Pol III regulates Pol II transcription and chromatin binding of the facilitates chromatin transcription (FACT) complex to alter local chromatin structures, which in turn affects the Pol II transcription rate.

CONCLUSIONS

Our results support a model suggesting that RNA polymerases show cross-regulatory effects: Pol III affects local chromatin structures and the FACT-Pol II axis to regulate the Pol II transcription rate at certain gene loci. This study provides a new perspective for understanding the dysregulation of Pol III in various tissues affected by developmental diseases.

FUNAGE-Pro: comprehensive web server for gene set enrichment analysis of prokaryotes

Recent advances in the field of high throughput (meta-)transcriptomics and proteomics call for easy and rapid methods enabling to explore not only single genes or proteins but also extended biological systems. Gene set enrichment analysis is commonly used to find relations in a set of genes and helps to uncover the biological meaning in results derived from high-throughput data. The basis for gene set enrichment analysis is a solid functional classification of genes. Here, we describe a comprehensive database containing multiple functional classifications of genes of all (>55 000) publicly available complete bacterial genomes. In addition to the most common functional classes such as COG and GO, also KEGG, InterPro, PFAM, eggnog and operon classes are supported. As classification data for features is often not available, we offer fast annotation and classification of proteins in any newly sequenced bacterial genome. The web server FUNAGE-Pro enables fast functional analysis on single gene sets, multiple experiments, time series data, clusters, and gene network modules for any prokaryote species or strain. FUNAGE-Pro is freely available at http://funagepro.molgenrug.nl.

TWO-SIGMA-G: a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell-cell correlation

We propose TWO-SIGMA-G, a competitive gene set test for scRNA-seq data. TWO-SIGMA-G uses a mixed-effects regression model based on our previously published TWO-SIGMA to test for differential expression at the gene-level. This regression-based model provides flexibility and rigor at the gene-level in (1) handling complex experimental designs, (2) accounting for the correlation between biological replicates and (3) accommodating the distribution of scRNA-seq data to improve statistical inference. Moreover, TWO-SIGMA-G uses a novel approach to adjust for inter-gene-correlation (IGC) at the set-level to control the set-level false positive rate. Simulations demonstrate that TWO-SIGMA-G preserves type-I error and increases power in the presence of IGC compared with other methods. Application to two datasets identified HIV-associated interferon pathways in xenograft mice and pathways associated with Alzheimer's disease progression in humans.

Analysis of transcriptomic data sets supports the role of IL-6 in NETosis and immunothrombosis in severe COVID-19

Background

There is an urgent need to understand the key events driving pathogenesis of severe COVID-19 disease, so that precise treatment can be instituted. In this respect NETosis is gaining increased attention in the scientific community, as an important pathological process contributing to mortality. We sought to test if indeed there exists robust evidence of NETosis in multiple transcriptomic data sets from human subjects with severe COVID-19 disease. Gene set enrichment analysis was performed to test for up-regulation of gene set functional in NETosis in the blood of patients with COVID-19 illness.

Results

Blood gene expression functional in NETosis increased with severity of illness, showed negative correlation with blood oxygen saturation, and was validated in the lung of COVID-19 non-survivors. Temporal expression of IL-6 was compared between severe and moderate illness with COVID-19. Unsupervised clustering was performed to reveal co-expression of IL-6 with complement genes. In severe COVID-19 illness, there is transcriptional evidence of activation of NETosis, complement and coagulation cascade, and negative correlation between NETosis and respiratory function (oxygen saturation). An early spike in IL-6 is observed in severe COVID-19 illness that is correlated with complement activation.

Conclusions

Based on the transcriptional dynamics of IL-6 expression and its downstream effect on complement activation, we constructed a model that links early spike in IL-6 level with persistent and self-perpetuating complement activation, NETosis, immunothrombosis and respiratory dysfunction. Our model supports the early initiation of anti-IL6 therapy in severe COVID-19 disease before the life-threatening complications of the disease can perpetuate themselves autonomously.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-01001-1.

Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studies

Differential expression (DE) analysis and gene set enrichment (GSE) analysis are commonly applied in single cell RNA sequencing (scRNA-seq) studies. Here, we develop an integrative and scalable computational method, iDEA, to perform joint DE and GSE analysis through a hierarchical Bayesian framework. By integrating DE and GSE analyses, iDEA can improve the power and consistency of DE analysis and the accuracy of GSE analysis. Importantly, iDEA uses only DE summary statistics as input, enabling effective data modeling through complementing and pairing with various existing DE methods. We illustrate the benefits of iDEA with extensive simulations. We also apply iDEA to analyze three scRNA-seq data sets, where iDEA achieves up to five-fold power gain over existing GSE methods and up to 64% power gain over existing DE methods. The power gain brought by iDEA allows us to identify many pathways that would not be identified by existing approaches in these data.

Differential expression (DE) and gene set enrichment (GSE) analysis tend to be carried out separately. Here, the authors present iDEA (integrative Differential expression and gene set Enrichment Analysis) for the analysis of scRNAseq data which uses a Baysian approach to jointly model DE and GSE for improved power in both tasks.

Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses

BACKGROUND

Neonatal sepsis (NS) is considered as the most common cause of neonatal deaths that newborns suffer from. Although numerous studies focus on gene biomarkers of NS, the predictive value of the gene biomarkers is low. NS pathogenesis is still needed to be investigated.

METHODS

After data preprocessing, we used KEGG enrichment method to identify the differentially expressed pathways between NS and normal controls. Then, functional principal component analysis (FPCA) was adopted to calculate gene values in NS. In order to further study the key signaling pathway of the NS, elastic-net regression model, Mann-Whitney U test, and coexpression network were used to estimate the weights of signaling pathway and hub genes.

RESULTS

A total of 115 different pathways between NS and controls were first identified. FPCA made full use of time-series gene expression information and estimated F values of genes in the different pathways. The top 1000 genes were considered as the different genes and were further analyzed by elastic-net regression and MWU test. There were 7 key signaling pathways between the NS and controls, according to different sources. Among those genes involved in key pathways, 7 hub genes, PIK3CA, TGFBR2, CDKN1B, KRAS, E2F3, TRAF6, and CHUK, were determined based on the coexpression network. Most of them were cancer-related genes. PIK3CA was considered as the common marker, which is highly expressed in the lymphocyte group. Little was known about the correlation of PIK3CA with NS, which gives us a new enlightenment for NS study.

CONCLUSION

This research might provide the perspective information to explore the potential novel genes and pathways as NS therapy targets.

FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis

Motivation

Gene set enrichment analyses (GSEAs) are widely used in genomic research to identify underlying biological mechanisms (defined by the gene sets), such as Gene Ontology terms and molecular pathways. There are two caveats in the currently available methods: (i) they are typically designed for group comparisons or regression analyses, which do not utilize temporal information efficiently in time-series of transcriptomics measurements; and (ii) genes overlapping in multiple molecular pathways are considered multiple times in hypothesis testing.

Results

We propose an inferential framework for GSEA based on functional data analysis, which utilizes the temporal information based on functional principal component analysis, and disentangles the effects of overlapping genes by a functional extension of the elastic-net regression. Furthermore, the hypothesis testing for the gene sets is performed by an extension of Mann-Whitney U test which is based on weighted rank sums computed from correlated observations. By using both simulated datasets and a large-scale time-course gene expression data on human influenza infection, we demonstrate that our method has uniformly better receiver operating characteristic curves, and identifies more pathways relevant to immune-response to human influenza infection than the competing approaches.

Availability and Implementation

The methods are implemented in R package FUNNEL, freely and publicly available at: https://github.com/yunzhang813/FUNNEL-GSEA-R-Package.

Supplementary information

Supplementary data are available at Bioinformatics online.

pathVar: a new method for pathway-based interpretation of gene expression variability

Identifying the pathways that control a cellular phenotype is the first step to building a mechanistic model. Recent examples in developmental biology, cancer genomics, and neurological disease have demonstrated how changes in the variability of gene expression can highlight important genes that are under different degrees of regulatory control. Simple statistical tests exist to identify differentially-variable genes; however, methods for investigating how changes in gene expression variability in the context of pathways and gene sets are under-explored. Here we present pathVar, a new method that provides functional interpretation of gene expression variability changes at the level of pathways and gene sets. pathVar is based on a multinomial exact test, or an asymptotic Chi-squared test as a more computationally-efficient alternative. The method can be used for gene expression studies from any technology platform in all biological settings either with a single phenotypic group, or two-group comparisons. To demonstrate its utility, we applied the method to a diverse set of diseases, species and samples. Results from pathVar are benchmarked against analyses based on average expression and two methods of GSEA, and demonstrate that analyses using both statistics are useful for understanding transcriptional regulation. We also provide recommendations for the choice of variability statistic that have been informed through analyses on simulations and real data. Based on the datasets selected, we show how pathVar can be used to gain insight into expression variability of single cell versus bulk samples, different stem cell populations, and cancer versus normal tissue comparisons.

Detecting disease-associated genomic outcomes using constrained mixture of Bayesian hierarchical models for paired data

Detecting disease-associated genomic outcomes is one of the key steps in precision medicine research. Cutting-edge high-throughput technologies enable researchers to unbiasedly test if genomic outcomes are associated with disease of interest. However, these technologies also include the challenges associated with the analysis of genome-wide data. Two big challenges are (1) how to reduce the effects of technical noise; and (2) how to handle the curse of dimensionality (i.e., number of variables are way larger than the number of samples). To tackle these challenges, we propose a constrained mixture of Bayesian hierarchical models (MBHM) for detecting disease-associated genomic outcomes for data obtained from paired/matched designs. Paired/matched designs can effectively reduce effects of confounding factors. MBHM does not involve multiple testing, hence does not have the problem of the curse of dimensionality. It also could borrow information across genes so that it can be used for whole genome data with small sample sizes.

Overlapping Group Logistic Regression with Applications to Genetic Pathway Selection

Discovering important genes that account for the phenotype of interest has long been a challenge in genome-wide expression analysis. Analyses such as gene set enrichment analysis (GSEA) that incorporate pathway information have become widespread in hypothesis testing, but pathway-based approaches have been largely absent from regression methods due to the challenges of dealing with overlapping pathways and the resulting lack of available software. The R package grpreg is widely used to fit group lasso and other group-penalized regression models; in this study, we develop an extension, grpregOverlap, to allow for overlapping group structure using a latent variable approach. We compare this approach to the ordinary lasso and to GSEA using both simulated and real data. We find that incorporation of prior pathway information can substantially improve the accuracy of gene expression classifiers, and we shed light on several ways in which hypothesis-testing approaches such as GSEA differ from regression approaches with respect to the analysis of pathway data.

Mitochondrial iron chelation ameliorates cigarette smoke-induced bronchitis and emphysema in mice

Chronic obstructive pulmonary disease (COPD) is linked to both cigarette smoking and genetic determinants. We have previously identified iron-responsive element-binding protein 2 (IRP2) as an important COPD susceptibility gene and have shown that IRP2 protein is increased in the lungs of individuals with COPD. Here we demonstrate that mice deficient in Irp2 were protected from cigarette smoke (CS)-induced experimental COPD. By integrating RNA immunoprecipitation followed by sequencing (RIP-seq), RNA sequencing (RNA-seq), and gene expression and functional enrichment clustering analysis, we identified Irp2 as a regulator of mitochondrial function in the lungs of mice. Irp2 increased mitochondrial iron loading and levels of cytochrome c oxidase (COX), which led to mitochondrial dysfunction and subsequent experimental COPD. Frataxin-deficient mice, which had higher mitochondrial iron loading, showed impaired airway mucociliary clearance (MCC) and higher pulmonary inflammation at baseline, whereas mice deficient in the synthesis of cytochrome c oxidase, which have reduced COX, were protected from CS-induced pulmonary inflammation and impairment of MCC. Mice treated with a mitochondrial iron chelator or mice fed a low-iron diet were protected from CS-induced COPD. Mitochondrial iron chelation also alleviated CS-induced impairment of MCC, CS-induced pulmonary inflammation and CS-associated lung injury in mice with established COPD, suggesting a critical functional role and potential therapeutic intervention for the mitochondrial-iron axis in COPD.

Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification

For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression. Classical statistical learning approaches for specimen classification using omics data, however, often cannot provide diagnostic models with sufficient accuracy and robustness for heterogeneous diseases like cancers or neurodegenerative disorders. In recent years, new approaches for building multivariate biomarker models on omics data have been proposed, which exploit prior biological knowledge from molecular networks and cellular pathways to address these limitations. This survey provides an overview of these recent developments and compares pathway- and network-based specimen classification approaches in terms of their utility for improving model robustness, accuracy and biological interpretability. Different routes to translate omics-based multifactorial biomarker models into clinical diagnostic tests are discussed, and a previous study is presented as example.

A network model for angiogenesis in ovarian cancer

Background

We recently identified two robust ovarian cancer subtypes, defined by the expression of genes involved in angiogenesis, with significant differences in clinical outcome. To identify potential regulatory mechanisms that distinguish the subtypes we applied PANDA, a method that uses an integrative approach to model information flow in gene regulatory networks.

Results

We find distinct differences between networks that are active in the angiogenic and non-angiogenic subtypes, largely defined by a set of key transcription factors that, although previously reported to play a role in angiogenesis, are not strongly differentially-expressed between the subtypes. Our network analysis indicates that these factors are involved in the activation (or repression) of different genes in the two subtypes, resulting in differential expression of their network targets. Mechanisms mediating differences between subtypes include a previously unrecognized pro-angiogenic role for increased genome-wide DNA methylation and complex patterns of combinatorial regulation.

Conclusions

The models we develop require a shift in our interpretation of the driving factors in biological networks away from the genes themselves and toward their interactions. The observed regulatory changes between subtypes suggest therapeutic interventions that may help in the treatment of ovarian cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0551-y) contains supplementary material, which is available to authorized users.

Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis

Analyzing biological system abnormalities in cancer patients based on measures of biological entities, such as gene expression levels, is an important and challenging problem. This paper applies existing methods, Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis, to pathway abnormality analysis in lung cancer using microarray gene expression data. Gene expression data from studies of Lung Squamous Cell Carcinoma (LUSC) in The Cancer Genome Atlas project, and pathway gene set data from the Kyoto Encyclopedia of Genes and Genomes were used to analyze the relationship between pathways and phenotypes. Results, in the form of pathway rankings, indicate that some pathways may behave abnormally in LUSC. For example, both the cell cycle and viral carcinogenesis pathways ranked very high in LUSC. Furthermore, some pathways that are known to be associated with cancer, such as the p53 and the PI3K-Akt signal transduction pathways, were found to rank high in LUSC. Other pathways, such as bladder cancer and thyroid cancer pathways, were also ranked high in LUSC.

Synovial phenotypes in rheumatoid arthritis correlate with response to biologic therapeutics

INTRODUCTION

Rheumatoid arthritis (RA) is a complex and clinically heterogeneous autoimmune disease. Currently, the relationship between pathogenic molecular drivers of disease in RA and therapeutic response is poorly understood.

METHODS

We analyzed synovial tissue samples from two RA cohorts of 49 and 20 patients using a combination of global gene expression, histologic and cellular analyses, and analysis of gene expression data from two further publicly available RA cohorts. To identify candidate serum biomarkers that correspond to differential synovial biology and clinical response to targeted therapies, we performed pre-treatment biomarker analysis compared with therapeutic outcome at week 24 in serum samples from 198 patients from the ADACTA (ADalimumab ACTemrA) phase 4 trial of tocilizumab (anti-IL-6R) monotherapy versus adalimumab (anti-TNFα) monotherapy.

RESULTS

We documented evidence for four major phenotypes of RA synovium - lymphoid, myeloid, low inflammatory, and fibroid - each with distinct underlying gene expression signatures. We observed that baseline synovial myeloid, but not lymphoid, gene signature expression was higher in patients with good compared with poor European league against rheumatism (EULAR) clinical response to anti-TNFα therapy at week 16 (P =0.011). We observed that high baseline serum soluble intercellular adhesion molecule 1 (sICAM1), associated with the myeloid phenotype, and high serum C-X-C motif chemokine 13 (CXCL13), associated with the lymphoid phenotype, had differential relationships with clinical response to anti-TNFα compared with anti-IL6R treatment. sICAM1-high/CXCL13-low patients showed the highest week 24 American College of Rheumatology (ACR) 50 response rate to anti-TNFα treatment as compared with sICAM1-low/CXCL13-high patients (42% versus 13%, respectively, P =0.05) while anti-IL-6R patients showed the opposite relationship with these biomarker subgroups (ACR50 20% versus 69%, P =0.004).

CONCLUSIONS

These data demonstrate that underlying molecular and cellular heterogeneity in RA impacts clinical outcome to therapies targeting different biological pathways, with patients with the myeloid phenotype exhibiting the most robust response to anti-TNFα. These data suggest a path to identify and validate serum biomarkers that predict response to targeted therapies in rheumatoid arthritis and possibly other autoimmune diseases.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01119859

BicOverlapper 2.0: visual analysis for gene expression

Motivation: Systems biology demands the use of several point of views to get a more comprehensive understanding of biological problems. This usually leads to take into account different data regarding the problem at hand, but it also has to do with using different perspectives of the same data. This multifaceted aspect of systems biology often requires the use of several tools, and it is often hard to get a seamless integration of all of them, which would help the analyst to have an interactive discourse with the data.

Results: Focusing on expression profiling, BicOverlapper 2.0 visualizes the most relevant aspects of the analysis, including expression data, profiling analysis results and functional annotation. It also integrates several state-of-the-art numerical methods, such as differential expression analysis, gene set enrichment or biclustering.

Availability and implementation: BicOverlapper 2.0 is available at: http://vis.usal.es/bicoverlapper2

Contact:rodri@usal.es

Gene expression within the extended amygdala of 5 pairs of rat lines selectively bred for high or low ethanol consumption

The objectives of this study were to determine innate differences in gene expression in 2 regions of the extended amygdala between 5 different pairs of lines of male rats selectively bred for high or low ethanol consumption: a) alcohol-preferring (P) vs. alcohol-non-preferring (NP) rats, b) high-alcohol-drinking (HAD) vs. low-alcohol-drinking (LAD) rats (replicate line-pairs 1 and 2), c) ALKO alcohol (AA) vs. nonalcohol (ANA) rats, and d) Sardinian alcohol-preferring (sP) vs. Sardinian alcohol-nonpreferring (sNP) rats, and then to determine if these differences are common across the line-pairs. Microarray analysis revealed up to 1772 unique named genes in the nucleus accumbens shell (AcbSh) and 494 unique named genes in the central nucleus of the amygdala (CeA) that significantly differed [False Discovery Rate (FDR) = 0.10; fold-change at least 1.2] in expression between the individual line-pairs. Analysis using Gene Ontology (GO) and Ingenuity Pathways information indicated significant categories and networks in common for up to 3 or 4 line-pairs, but not for all 5 line-pairs. However, there were almost no individual genes in common within these categories and networks. ANOVAs of the combined data for the 5 line-pairs indicated 1014 and 731 significant (p < 0.01) differences in expression of named genes in the AcbSh and CeA, respectively. There were 4-6 individual named genes that significantly differed across up to 3 line-pairs in both regions; only 1 gene (Gsta4 in the CeA) differed in as many as 4 line-pairs. Overall, the findings suggest that a) some biological categories or networks (e.g., cell-to-cell signaling, cellular stress response, cellular organization, etc.) may be in common for subsets of line-pairs within either the AcbSh or CeA, and b) regulation of different genes and/or combinations of multiple biological systems may be contributing to the disparate alcohol drinking behaviors of these line-pairs.

ReportingTools: an automated result processing and presentation toolkit for high-throughput genomic analyses

It is common for computational analyses to generate large amounts of complex data that are difficult to process and share with collaborators. Standard methods are needed to transform such data into a more useful and intuitive format. We present ReportingTools, a Bioconductor package, that automatically recognizes and transforms the output of many common Bioconductor packages into rich, interactive, HTML-based reports. Reports are not generic, but have been individually designed to reflect content specific to the result type detected. Tabular output included in reports is sortable, filterable and searchable and contains context-relevant hyperlinks to external databases. Additionally, in-line graphics have been developed for specific analysis types and are embedded by default within table rows, providing a useful visual summary of underlying raw data. ReportingTools is highly flexible and reports can be easily customized for specific applications using the well-defined API.

AVAILABILITY

The ReportingTools package is implemented in R and available from Bioconductor (version ≥ 2.11) at the URL: http://bioconductor.org/packages/release/bioc/html/ReportingTools.html. Installation instructions and usage documentation can also be found at the above URL.

Investigating the concordance of Gene Ontology terms reveals the intra- and inter-platform reproducibility of enrichment analysis

BACKGROUND

Reliability and Reproducibility of differentially expressed genes (DEGs) are essential for the biological interpretation of microarray data. The microarray quality control (MAQC) project launched by US Food and Drug Administration (FDA) elucidated that the lists of DEGs generated by intra- and inter-platform comparisons can reach a high level of concordance, which mainly depended on the statistical criteria used for ranking and selecting DEGs. Generally, it will produce reproducible lists of DEGs when combining fold change ranking with a non-stringent p-value cutoff. For further interpretation of the gene expression data, statistical methods of gene enrichment analysis provide powerful tools for associating the DEGs with prior biological knowledge, e.g. Gene Ontology (GO) terms and pathways, and are widely used in genome-wide research. Although the DEG lists generated from the same compared conditions proved to be reliable, the reproducible enrichment results are still crucial to the discovery of the underlying molecular mechanism differentiating the two conditions. Therefore, it is important to know whether the enrichment results are still reproducible, when using the lists of DEGs generated by different statistic criteria from inter-laboratory and cross-platform comparisons. In our study, we used the MAQC data sets for systematically accessing the intra- and inter-platform concordance of GO terms enriched by Gene Set Enrichment Analysis (GSEA) and LRpath.

RESULTS

In intra-platform comparisons, the overlapped percentage of enriched GO terms was as high as ~80% when the inputted lists of DEGs were generated by fold change ranking and Significance Analysis of Microarrays (SAM), whereas the percentages decreased about 20% when generating the lists of DEGs by using fold change ranking and t-test, or by using SAM and t-test. Similar results were found in inter-platform comparisons.

CONCLUSIONS

Our results demonstrated that the lists of DEGs in a high level of concordance can ensure the high concordance of enrichment results. Importantly, based on the lists of DEGs generated by a straightforward method of combining fold change ranking with a non-stringent p-value cutoff, enrichment analysis will produce reproducible enriched GO terms for the biological interpretation.

A knock-in Npm1 mutation in mice results in myeloproliferation and implies a perturbation in hematopoietic microenvironment

Somatic Nucleophosmin (NPM1) mutation frequently occurs in acute myeloid leukemia (AML), but its role in leukemogenesis remains unclear. This study reports the first "conventional" knock-in mouse model of Npm1 mutation, which was achieved by inserting TCTG after nucleotide c.857 (c.854_857dupTCTG) to mimic human mutation without any "humanized" sequence. The resultant mutant peptide differed slightly different from that in humans but exhibited cytoplasmic pulling force. Homozygous (Npm1(c+/c+)) mice showed embryonic lethality before day E8.5, wheras heterozygous (Npm1(wt/c+)) mice appeared healthy at birth and were fertile. Approximately 36% of Npm1(wt/c+) mice developed myeloproliferative disease (MPD) with extramedullary hematopoiesis. Those Npm1(wt/c+) mice that did not develop MPD nevertheless gradually developed monocytosis and showed increased numbers of marrow myeloid precursors. This second group of Npm1(wt/c+) mice also showed compromised cobblestone area formation, suggesting pathology in the hematopoietic niche. Microarray experiments and bioinformatic analysis on mice myeloid precursor cells and 227 human samples revealed the expression of CXCR4/CXCL12-related genes was significantly suppressed in mutant cells from both mice and humans. Thus, our mouse model demonstrated that Npm1 mutation can result in MPD, but is insufficient for leukemogenesis. Perturbation of hematopoietic niche in mutant hematopoietic stem cells (implied by underrepresentation of CXCR4/CXCL12-related genes) may be important in the pathogenesis of NPM1 mutations.

Analyzing LC/MS metabolic profiling data in the context of existing metabolic networks

Metabolic profiling is the unbiased detection and quantification of low molecular-weight metabolites in a living system. It is rapidly developing in biological and translational research, contributing to disease mechanism elucidation, environmental chemical surveillance, biomarker detection, and health outcome prediction. Recent developments in experimental and computational technology allow more and more known metabolites to be detected and quantified from complex samples. As the coverage of the metabolic network improves, it has become feasible to examine metabolic profiling data from a systems perspective, i.e. interpreting the data and performing statistical inference in the context of pathways and genome-scale metabolic networks. Recently a number of methods have been developed in this area, and much improvement in algorithms and databases are still needed. In this review, we survey some methods for the analysis of metabolic profiling data based on metabolic networks.

Gene expression in the ventral tegmental area of 5 pairs of rat lines selectively bred for high or low ethanol consumption

The objective of this study was to determine if there are common innate differences in gene expression or gene pathways in the ventral tegmental area (VTA) among 5 different pairs of rat lines selectively bred for high (HEC) or low (LEC) ethanol consumption: (a) alcohol-preferring (P) vs. alcohol-non-preferring (NP) rats; (b) high-alcohol-drinking (HAD) vs. low-alcohol-drinking (LAD) rats (replicate line pairs 1 and 2); (c) ALKO alcohol (AA) vs. nonalcohol (ANA) rats; and (d) Sardinian alcohol-preferring (sP) vs. alcohol-nonpreferring (sNP) rats. Microarray analysis revealed between 370 and 1340 unique named genes that significantly differed in expression between the individual line-pairs. Analysis using Gene Ontology (GO) and Ingenuity Pathways information indicated significant categories and networks in common for up to 3 line-pairs, but not for all 5 line-pairs; moreover, there were few genes in common in these categories and networks. ANOVA of the combined data for the 5 line-pairs indicated 1295 significant (p<0.01) differences in expression of named genes. Although no individual named gene was significant across all 5 line-pairs, there were 22 genes that overlapped in the same direction in 3 or 4 of the line-pairs. Overall, the findings suggest that (a) some biological categories or networks may be in common for subsets of line-pairs; and (b) regulation of different genes and/or combinations of multiple biological systems (e.g., transcription, synaptic function, intracellular signaling and protection against oxidative stress) within the VTA (possibly involving dopamine and glutamate) may be contributing to the disparate alcohol drinking behaviors of these line-pairs.

iBBiG: iterative binary bi-clustering of gene sets

Motivation: Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods.

Results: We transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set—phenotype association that predicted tumor metastases within tumor subtypes.

Availability: Implemented in the Bioconductor package iBBiG

Contact:aedin@jimmy.harvard.edu

Camera: a competitive gene set test accounting for inter-gene correlation

Competitive gene set tests are commonly used in molecular pathway analysis to test for enrichment of a particular gene annotation category amongst the differential expression results from a microarray experiment. Existing gene set tests that rely on gene permutation are shown here to be extremely sensitive to inter-gene correlation. Several data sets are analyzed to show that inter-gene correlation is non-ignorable even for experiments on homogeneous cell populations using genetically identical model organisms. A new gene set test procedure (CAMERA) is proposed based on the idea of estimating the inter-gene correlation from the data, and using it to adjust the gene set test statistic. An efficient procedure is developed for estimating the inter-gene correlation and characterizing its precision. CAMERA is shown to control the type I error rate correctly regardless of inter-gene correlations, yet retains excellent power for detecting genuine differential expression. Analysis of breast cancer data shows that CAMERA recovers known relationships between tumor subtypes in very convincing terms. CAMERA can be used to analyze specified sets or as a pathway analysis tool using a database of molecular signatures.

Average rank-based score to measure deregulation of molecular pathway gene sets

Background

Deregulation of biological pathways has been shown to be involved in the turmorigenesis of a variety of cancers. The co-regulation of pathways in tumor and normal tissues has not been studied in a systematic manner.

Results

In this study we propose a novel statistic named AR-score (average rank based score) to measure pathway activities based on microarray gene expression profiles. We calculate and compare the AR-scores of pathways in microarray datasets containing expression profiles for a wide range of cancer types as well as the corresponding normal tissues. We find that many pathways undergo significant activity changes in tumors with respect to normal tissues. AR-scores for a small subset of pathways are capable of distinguishing tumor from normal tissues or classifying tumor subtypes. In normal tissues many pathways are highly correlated in their activities, whereas their correlations reduce significantly in tumors and cancer cell lines. The co-expression of genes in the same pathways was also significantly perturbed in tumors.

Conclusions

The co-regulation of genes in the same pathways and co-regulation of different pathways are significantly perturbed in tumors versus normal tissues. Our method provides a useful tool for better understanding the mechanistic changes in tumors, which can also be used for exploring other biological problems.

attract: A method for identifying core pathways that define cellular phenotypes

attract is a knowledge-driven analytical approach for identifying and annotating the gene-sets that best discriminate between cell phenotypes. attract finds distinguishing patterns within pathways, decomposes pathways into meta-genes representative of these patterns, and then generates synexpression groups of highly correlated genes from the entire transcriptome dataset. attract can be applied to a wide range of biological systems and is freely available as a Bioconductor package and has been incorporated into the MeV software system.

Capturing changes in gene expression dynamics by gene set differential coordination analysis

Analyzing gene expression data at the gene set level greatly improves feature extraction and data interpretation. Currently most efforts in gene set analysis are focused on differential expression analysis--finding gene sets whose genes show first-order relationship with the clinical outcome. However the regulation of the biological system is complex, and much of the change in gene expression dynamics do not manifest in the form of differential expression. At the gene set level, capturing the change in expression dynamics is difficult due to the complexity and heterogeneity of the gene sets. Here we report a systematic approach to detect gene sets that show differential coordination patterns with the rest of the transcriptome, as well as pairs of gene sets that are differentially coordinated with each other. We demonstrate that the method can identify biologically relevant gene sets, many of which do not show first-order relationship with the clinical outcome.

Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts

High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fisher's exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature.

DEGAS: de novo discovery of dysregulated pathways in human diseases

Background

Molecular studies of the human disease transcriptome typically involve a search for genes whose expression is significantly dysregulated in sick individuals compared to healthy controls. Recent studies have found that only a small number of the genes in human disease-related pathways show consistent dysregulation in sick individuals. However, those studies found that some pathway genes are affected in most sick individuals, but genes can differ among individuals. While a pathway is usually defined as a set of genes known to share a specific function, pathway boundaries are frequently difficult to assign, and methods that rely on such definition cannot discover novel pathways. Protein interaction networks can potentially be used to overcome these problems.

Methodology/Principal Findings

We present DEGAS (DysrEgulated Gene set Analysis via Subnetworks), a method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. We applied DEGAS to seven human diseases and obtained statistically significant results that appear to home in on compact pathways enriched with hallmarks of the diseases. In Parkinson's disease, we provide novel evidence for involvement of mRNA splicing, cell proliferation, and the 14-3-3 complex in the disease progression. DEGAS is available as part of the MATISSE software package (http://acgt.cs.tau.ac.il/matisse).

Conclusions/Significance

The subnetworks identified by DEGAS can provide a signature of the disease potentially useful for diagnosis, pinpoint possible pathways affected by the disease, and suggest targets for drug intervention.

ROAST: rotation gene set tests for complex microarray experiments

Motivation: A gene set test is a differential expression analysis in which a P-value is assigned to a set of genes as a unit. Gene set tests are valuable for increasing statistical power, organizing and interpreting results and for relating expression patterns across different experiments. Existing methods are based on permutation. Methods that rely on permutation of probes unrealistically assume independence of genes, while those that rely on permutation of sample are suitable only for two-group comparisons with a good number of replicates in each group.

Results: We present ROAST, a statistically rigorous gene set test that allows for gene-wise correlation while being applicable to almost any experimental design. Instead of permutation, ROAST uses rotation, a Monte Carlo technology for multivariate regression. Since the number of rotations does not depend on sample size, ROAST gives useful results even for experiments with minimal replication. ROAST allows for any experimental design that can be expressed as a linear model, and can also incorporate array weights and correlated samples. ROAST can be tuned for situations in which only a subset of the genes in the set are actively involved in the molecular pathway. ROAST can test for uni- or bi-direction regulation. Probes can also be weighted to allow for prior importance. The power and size of the ROAST procedure is demonstrated in a simulation study, and compared to that of a representative permutation method. Finally, ROAST is used to test the degree of transcriptional conservation between human and mouse mammary stems.

Availability: ROAST is implemented as a function in the Bioconductor package limma available from www.bioconductor.org

Contact:smyth@wehi.edu.au

Supplementary information:Supplementary data are available at Bioinformatics online.

GOing Bayesian: model-based gene set analysis of genome-scale data

The interpretation of data-driven experiments in genomics often involves a search for biological categories that are enriched for the responder genes identified by the experiments. However, knowledge bases such as the Gene Ontology (GO) contain hundreds or thousands of categories with very high overlap between categories. Thus, enrichment analysis performed on one category at a time frequently returns large numbers of correlated categories, leaving the choice of the most relevant ones to the user's; interpretation.

Here we present model-based gene set analysis (MGSA) that analyzes all categories at once by embedding them in a Bayesian network, in which gene response is modeled as a function of the activation of biological categories. Probabilistic inference is used to identify the active categories. The Bayesian modeling approach naturally takes category overlap into account and avoids the need for multiple testing corrections met in single-category enrichment analysis. On simulated data, MGSA identifies active categories with up to 95% precision at a recall of 20% for moderate settings of noise, leading to a 10-fold precision improvement over single-category statistical enrichment analysis. Application to a gene expression data set in yeast demonstrates that the method provides high-level, summarized views of core biological processes and correctly eliminates confounding associations.

Microarray data quality control improves the detection of differentially expressed genes

Microarrays have become a routine tool for biomedical research. Data quality assessment is an essential part of the analysis, but it is still not easy to perform objectively or in an automated manner, and as a result it is often neglected. Here, we compared two strategies of array-level quality control using five publicly available microarray experiments: outlier removal and array weights. We also compared them against no outlier removal and random array removal. We find that removing outlier arrays can improve the signal-to-noise ratio and thus strengthen the power of detecting differentially expressed genes. Using array weights is similarly effective, but its applicability is more limited. The quality metrics presented here are implemented in the Bioconductor package arrayQualityMetrics.

GeneSigDB--a curated database of gene expression signatures

The primary objective of most gene expression studies is the identification of one or more gene signatures; lists of genes whose transcriptional levels are uniquely associated with a specific biological phenotype. Whilst thousands of experimentally derived gene signatures are published, their potential value to the community is limited by their computational inaccessibility. Gene signatures are embedded in published article figures, tables or in supplementary materials, and are frequently presented using non-standard gene or probeset nomenclature. We present GeneSigDB (http://compbio.dfci.harvard.edu/genesigdb) a manually curated database of gene expression signatures. GeneSigDB release 1.0 focuses on cancer and stem cells gene signatures and was constructed from more than 850 publications from which we manually transcribed 575 gene signatures. Most gene signatures (n = 560) were successfully mapped to the genome to extract standardized lists of EnsEMBL gene identifiers. GeneSigDB provides the original gene signature, the standardized gene list and a fully traceable gene mapping history for each gene from the original transcribed data table through to the standardized list of genes. The GeneSigDB web portal is easy to search, allows users to compare their own gene list to those in the database, and download gene signatures in most common gene identifier formats.

Probabilistic retrieval and visualization of biologically relevant microarray experiments

MOTIVATION

As ArrayExpress and other repositories of genome-wide experiments are reaching a mature size, it is becoming more meaningful to search for related experiments, given a particular study. We introduce methods that allow for the search to be based upon measurement data, instead of the more customary annotation data. The goal is to retrieve experiments in which the same biological processes are activated. This can be due either to experiments targeting the same biological question, or to as yet unknown relationships.

RESULTS

We use a combination of existing and new probabilistic machine learning techniques to extract information about the biological processes differentially activated in each experiment, to retrieve earlier experiments where the same processes are activated and to visualize and interpret the retrieval results. Case studies on a subset of ArrayExpress show that, with a sufficient amount of data, our method indeed finds experiments relevant to particular biological questions. Results can be interpreted in terms of biological processes using the visualization techniques.

AVAILABILITY

The code is available from http://www.cis.hut.fi/projects/mi/software/ismb09.

Identifying responsive functional modules from protein-protein interaction network

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.