scholarly journals Robust rank aggregation for gene list integration and meta-analysis

2012 ◽  
Vol 28 (4) ◽  
pp. 573-580 ◽  
Author(s):  
Raivo Kolde ◽  
Sven Laur ◽  
Priit Adler ◽  
Jaak Vilo
Keyword(s):  
Meta Analysis ◽  
Gene List ◽  
Rank Aggregation

scholarly journals Coex-Rank: An approach incorporating co-expression information for combined analysis of microarray data

2012 ◽  
Vol 9 (1) ◽  
pp. 32-43 ◽  
Author(s):  
Jinlu Cai ◽  
Henry L. Keen ◽  
Curt D. Sigmund ◽  
Thomas L. Casavant
Keyword(s):  
Microarray Data ◽  
Statistical Power ◽  
Meta Analysis ◽  
Rank Aggregation ◽  
Microarray Data Analysis ◽  
Combined Approach ◽  
Linear Modeling ◽  
Modeling Process ◽  
Genome Wide ◽  
Gene Level

Summary Microarrays have been widely used to study differential gene expression at the genomic level. They can also provide genome-wide co-expression information. Biologically related datasets from independent studies are publicly available, which requires robust combined approaches for integration and validation. Previously, meta-analysis has been adopted to solve this problem.As an alternative to meta-analysis, for microarray data with high similarity in biological experimental design, a more direct combined approach is possible. Gene-level normalization across datasets is motivated by the different scale and distribution of data due to separate origins. However, there has been limited discussion about this point in the past. Here we describe a combined approach for microarray analysis, including gene-level normalization and Coex-Rank approach. After normalization, a linear modeling process is used to identify lists of differentially expressed genes. The Coex-Rank approach incorporates co-expression information into a rank-aggregation procedure. We applied this computational approach to our data, which illustrated an improvement in statistical power and a complementary advantage of the Coex-Rank approach from a biological perspective.Our combined approach for microarray data analysis (Coex-rank) is based on normalization, which is naturally driven. The Coex-rank process not only takes advantage of merging the power of multiple methods regarding normalization but also assists in the discovery of functional clusters of genes.

scholarly journals Transcriptomic profiles in Parkinson’s disease

2020 ◽  
pp. 153537022096732
Author(s):  
Lille Kurvits ◽  
Freddy Lättekivi ◽  
Ene Reimann ◽  
Liis Kadastik-Eerme ◽  
Kristjan M Kasterpalu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Brain Tissue ◽  
Differentially Expressed Genes ◽  
Meta Analysis ◽  
Rank Aggregation ◽  
Differentially Expressed ◽  
Peripheral Tissues

Transcriptomics in Parkinson’s disease offers insights into the pathogenesis of Parkinson’s disease but obtaining brain tissue has limitations. In order to bypass this issue, we profile and compare differentially expressed genes and enriched pathways (KEGG) in two peripheral tissues (blood and skin) of 12 Parkinson’s disease patients and 12 healthy controls using RNA-sequencing technique and validation with RT-qPCR. Furthermore, we compare our results to previous Parkinson’s disease post mortem brain tissue and blood results using the robust rank aggregation method. The results show no overlapping differentially expressed genes or enriched pathways in blood vs. skin in our sample sets (25 vs. 1068 differentially expressed genes with an FDR ≤ 0.05; 1 vs. 9 pathways in blood and skin, respectively). A meta-analysis from previous transcriptomic sample sets using either microarrays or RNA-Seq yields a robust rank aggregation list of cortical gene expression changes with 43 differentially expressed genes; a list of substantia nigra changes with 2 differentially expressed genes and a list of blood changes with 1 differentially expressed gene being statistically significant at FDR ≤ 0.05. In cortex 1, KEGG pathway was enriched, four in substantia nigra and two in blood. None of the differentially expressed genes or pathways overlap between these tissues. When comparing our previously published skin transcription analysis, two differentially expressed genes between the cortex robust rank aggregation and skin overlap. In this study, for the first time a meta-analysis is applied on transcriptomic sample sets in Parkinson’s disease. Simultaneously, it explores the notion that Parkinson’s disease is not just a neuronal tissue disease by exploring peripheral tissues. The comparison of different Parkinson’s disease tissues yields surprisingly few significant differentially expressed genes and pathways, suggesting that divergent gene expression profiles in distinct cell lineages, metabolic and possibly iatrogenic effects create too much transcriptomic noise for detecting significant signal. On the other hand, there are signs that point towards Parkinson’s disease-specific changes in non-neuronal peripheral tissues in Parkinson’s disease, indicating that Parkinson’s disease might be a multisystem disorder.

scholarly journals Prediction of molecular markers of bovine mastitis by meta-analysis of differentially expressed genes using combined p-value and robust rank aggregation

2021 ◽  
Author(s):  
Anushri Umesh ◽  
Praveen Kumar Guttula ◽  
Mukesh Kumar Gupta
Keyword(s):  
Molecular Markers ◽  
Differentially Expressed Genes ◽  
Meta Analysis ◽  
Mirna Gene ◽  
Bovine Mastitis ◽  
Rank Aggregation ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
P Value ◽  
Expression Levels

Bovine mastitis causes significant economic loss to the dairy industry by affecting milk quality and quantity. E.coli and S.aureus are the two common mastitis-causing bacteria among the consortia of mastitis pathogens, wherein E.coli is an opportunistic environmental pathogen, and S.aureus is a contagious pathogen. This study was designed to predict molecular markers of bovine mastitis by meta-analysis of differentially expressed genes (DEG) in E.coli or S.aureus infected mammary epithelial cells (MECs) using p-value combination and robust rank aggregation (RRA) methods. High throughput transcriptome of bovine (MECs, infected with E.coli or S.aureus, were analyzed, and correlation of z-scores were computed for the expression datasets to identify the lineage profile and functional ontology of DEGs. Key pathways enriched in infected MECs were deciphered by Gene Set Enrichment Analysis (GSEA), following which combined p-value and RRA were used to perform DEG meta-analysis to limit type I error in the analysis. The miRNA-Gene networks were then built to uncover potential molecular markers of mastitis. Lineage profiling of MECs showed that the gene expression levels were associated with mammary tissue lineage. The up-regulated genes were enriched in immune-related pathways whereas down-regulated genes influenced the cellular processes. GSEA analysis of DEGs deciphered the involvement of Toll-like receptor (TLR), and NF- Kappa B signalling pathway during infection. Comparison after meta-analysis yielded with genes ZC3H12A, RND1 and MAP3K8 having significant expression levels in both E.coli and S.aureus dataset and on evaluating miRNA-Gene network 7 pairs were common to both sets identifying them as potential molecular markers.

scholarly journals Circulating miRNA biomarkers for sporadic Amyotrophic Lateral Sclerosis: a systematic meta-analysis and empirical validation

2021 ◽  
Author(s):  
Narges Daneshafrooz ◽  
Mahammad Taghi Joghataei ◽  
Mehdi Mehdizadeh ◽  
Afagh Alavi ◽  
Mahmood Barati ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Meta Analysis ◽  
Disease Onset ◽  
Interaction Network ◽  
Rank Aggregation ◽  
Gene Profiling ◽  
Sporadic Amyotrophic Lateral Sclerosis ◽  
Relative Expression ◽  
Circulating Mirna ◽  
Lateral Sclerosis

Abstract Amyotrophic lateral sclerosis (ALS) is a lethal neurodegenerative disease that occurs as sporadic (sALS) in most cases. The disease is not curable, and its pathogenesis is not understood as yet. Given the intricacy of underlying molecular interactions and heterogeneity of ALS, the discovery of molecules contributing to disease onset and progression will open the way for advancement in prevention and therapeutic intervention. Here we conducted a meta-analysis of 12 circulating miRNA profiling studies using the robust rank aggregation (RRA) method, followed by enrichment analysis. We identified miR-451a and let-7f-5p as meta-signature miRNAs whose targets are involved in critical pathogenic pathways underlying ALS, including FoxO, MAPK, and apoptosis. A systematic review of 7 gene profiling studies elucidated that 241 genes upregulated in sALS circulation with concomitant being targets of the meta-signature miRNAs. Protein-protein interaction network analysis for selected targets revealed the main subcluster is involved in multiple cascades, which eventually leads to apoptosis. Besides, evaluation of relative expression of miRNAs by TaqMan RT-qPCR verified let-7f-5p is significantly downregulated in the plasma of patients. Furthermore, we verified the relative expression of two top-ranked upregulated miRNAs and found miR-338-3p significantly upregulated in ALS patients. Receiver operating characteristic (ROC) analysis indicated that let-7f-5p and miR-338-3p are useful plasms biomarkers for diagnosis and a potential therapeutic target for ALS disease.

Dysregulated microRNAs in laryngeal cancer: a comprehensive meta-analysis using a robust rank aggregation approach

2020 ◽  
Vol 16 (33) ◽  
pp. 2723-2734
Author(s):  
Zaizai Cao ◽  
Yu Guo ◽  
Yinjie Ao ◽  
Shuihong Zhou
Keyword(s):  
Target Genes ◽  
Laryngeal Squamous Cell Carcinoma ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Rank Aggregation ◽  
The Cancer Genome Atlas ◽  
Aggregation Method ◽  
Multiple Cancer ◽  
Cancer Genome Atlas ◽  
Genome Atlas

We need a reasonable method of compiling data from different studies regarding the expression of microRNA (miRNA) in laryngeal squamous cell carcinoma (LSCC). The robust rank aggregation method was used to integrate the rank lists of miRNAs from 11 studies. The enrichment analysis was performed on target genes of meta-signature miRNAs. The Cancer Genome Atlas database was used to confirm the results of meta-analysis. Three meta-signature miRNAs (miR-21-5p, miR-196a-5p and miR-145-5p) were obtained. All three miRNAs could be prognostic for LSCC. The enrichment analysis showed that these miRNAs were associated significantly with multiple cancer-related signaling pathways. The robust rank aggregation approach is an effective way to identify important miRNAs from different studies. All identified miRNAs could be candidates for LSCC diagnostic and prognostic biomarkers.

Meta-analysis of ChIP-seq Datasets Through the Rank Aggregation Approach

2020 ◽  
Author(s):  
Semyon K. Kolmykov ◽  
Yury V. Kondrakhin ◽  
Ruslan N. Sharipov ◽  
Ivan S. Yevshi ◽  
Anna S. Ryabova ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Rank Aggregation

scholarly journals Differentially expressed circulating miRNAs in postmenopausal osteoporosis: a meta-analysis

2019 ◽  
Vol 39 (5) ◽  
Author(s):  
Elif Pala ◽  
Tuba Denkçeken
Keyword(s):  
Postmenopausal Osteoporosis ◽  
Signaling Pathway ◽  
Target Genes ◽  
Meta Analysis ◽  
Adherens Junction ◽  
Enrichment Analysis ◽  
Rank Aggregation ◽  
Cochrane Library ◽  
Pathway Enrichment Analysis ◽  
Hub Genes

AbstractMicroRNAs (miRNAs) have been proven to play a crucial role in postmenopausal osteoporosis (PMO), and studies on their diagnostic value have been increasing. In our study, we aim to identify the key miRNAs in the PMO that might be potential biomarkers. A comprehensive systematic literature search was conducted by searching PubMed, Web of Science, Embase and Cochrane Library databases. In the total of 16 independent miRNA expression studies which contained 327 PMO patients and 328 postmenopausal (PM) healthy control samples, miRNAs were evaluated by using robust rank aggregation (RRA) method. A statistically significant meta-signature of up-regulated hsa-miR-133a-3p (P = 1.38e−03) was determined. Then bioinformatics analysis to recruit putative target genes prediction of hsa-miR-133a-3p and pathway enrichment analysis to reveal what biological processes this miRNA may affect were conducted. It was indicated that pathways were commonly associated with adrenergic signaling in cardiomyocytes, adherens junction, PI3K-Akt signaling pathway and AMPK signaling pathway. Furthermore, STRING and Cytoscape tools were used to visualize the interactions between target genes of hsa-miR-133a-3p. Six genes were detected as hub genes among 576 targets which were CDC42, RHOA, EGFR, VAMP2, PIK3R2 and FN1. After Kyoto Encyclopedia of Genes and Genomes pathway analysis, it was detected that these hub genes were mostly enriched in signaling pathways and cancer. In this meta-analysis, it is stated that circulating hsa-miR-133a-3p may serve as a potential non-invasive biomarker and therapeutic target in PMO.

scholarly journals Dynamic data-driven meta-analysis for prioritisation of host genes implicated in COVID-19

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Nicholas Parkinson ◽  
Natasha Rodgers ◽  
Max Head Fourman ◽  
Bo Wang ◽  
Marie Zechner ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Gene List ◽  
Host Factors ◽  
Data Driven ◽  
Robust Evidence ◽  
Omic Data ◽  
Gene Rank ◽  
Ranked List ◽  
Host Genes

AbstractThe increasing body of literature describing the role of host factors in COVID-19 pathogenesis demonstrates the need to combine diverse, multi-omic data to evaluate and substantiate the most robust evidence and inform development of therapies. Here we present a dynamic ranking of host genes implicated in human betacoronavirus infection (SARS-CoV-2, SARS-CoV, MERS-CoV, seasonal coronaviruses). We conducted an extensive systematic review of experiments identifying potential host factors. Gene lists from diverse sources were integrated using Meta-Analysis by Information Content (MAIC). This previously described algorithm uses data-driven gene list weightings to produce a comprehensive ranked list of implicated host genes. From 32 datasets, the top ranked gene was PPIA, encoding cyclophilin A, a druggable target using cyclosporine. Other highly-ranked genes included proposed prognostic factors (CXCL10, CD4, CD3E) and investigational therapeutic targets (IL1A) for COVID-19. Gene rankings also inform the interpretation of COVID-19 GWAS results, implicating FYCO1 over other nearby genes in a disease-associated locus on chromosome 3. Researchers can search and review the gene rankings and the contribution of different experimental methods to gene rank at https://baillielab.net/maic/covid19. As new data are published we will regularly update the list of genes as a resource to inform and prioritise future studies.

Comprehensive Meta-analysis of MicroRNA Expression Using a Robust Rank Aggregation Approach

2014 ◽  
pp. 361-373 ◽  
Author(s):  
Urmo Võsa ◽  
Raivo Kolde ◽  
Jaak Vilo ◽  
Andres Metspalu ◽  
Tarmo Annilo
Keyword(s):  
Meta Analysis ◽  
Rank Aggregation ◽  
Microrna Expression

scholarly journals Systematic review and meta-analysis identifies potential host therapeutic targets in COVID-19.

2020 ◽  
Author(s):  
Nicholas Parkinson ◽  
Natasha Rodgers ◽  
Max Head Fourman ◽  
Bo Wang ◽  
Marie Zechner ◽  
...  
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Gene List ◽  
Host Factors ◽  
Potential Host ◽  
Robust Evidence ◽  
Omic Data ◽  
Gene Rank ◽  
Host Genes

The increasing body of literature describing the role of host factors in COVID- 19 pathogenesis demonstrates the need to combine diverse, multi-omic data to evaluate and substantiate the most robust evidence and inform development of therapies. Here we present a dynamic ranking of host genes implicated in human betacoronavirus infection (SARS-CoV-2, SARS-CoV, MERS-CoV, seasonal coronaviruses). Researchers can search and review the ranked genes and the contribution of different experimental methods to gene rank at https://baillielab.net/maic/covid19. We conducted an extensive systematic review of experiments identifying potential host factors. Gene lists from diverse sources were integrated using Meta- Analysis by Information Content (MAIC). This previously described algorithm uses data-driven gene list weightings to produce a comprehensive ranked list of implicated host genes. From 32 datasets, the top ranked gene was PPIA, encoding cyclophilin A, a drug- gable target using cyclosporine.Other highly-ranked genes included proposed prognostic factors (CXCL10, CD4, CD3E) and investigational therapeutic tar- gets (IL1A) for COVID-19. Gene rankings also inform the interpretation of COVID-19 GWAS results, implicating FYCO1 over other nearby genes in a disease-associated locus on chromosome 3. As new data are published we will regularly update list of genes as a resource to inform and prioritise future studies.

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