ArticleScientific reports2018
Statistical Approach for Gene Set Analysis with Trait Specific Quantitative Trait Loci.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- The hitchhikers' guide to RNA sequencing and functional analysis.Briefings in bioinformatics · 2023Article
- Meta-analysis of RNA-Seq datasets highlights novel genes/pathways involved in fat deposition in fat-tail of sheep.Frontiers in veterinary science · 2023Article
- Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges.Entropy (Basel, Switzerland) · 2022Review
- Statistical Approach of Gene Set Analysis with Quantitative Trait Loci for Crop Gene Expression Studies.Entropy (Basel, Switzerland) · 2021Article
- Identification of Potential Cytokinin Responsive Key Genes in Rice Treated With Trans-Zeatin Through Systems Biology Approach.Frontiers in genetics · 2021Article
- Statistical Approach for Biologically Relevant Gene Selection from High-Throughput Gene Expression Data.Entropy (Basel, Switzerland) · 2020Article
- Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges.Entropy (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The analysis of gene sets is usually carried out based on gene ontology terms and known biological pathways. These approaches may not establish any formal relation between genotype and trait specific phenotype. In plant biology and breeding, analysis of gene sets with trait specific Quantitative Trait Loci (QTL) data are considered as great source for biological knowledge discovery. Therefore, we proposed an innovative statistical approach called Gene Set Analysis with QTLs (GSAQ) for interpreting gene expression data in context of gene sets with traits. The utility of GSAQ was studied on five different complex abiotic and biotic stress scenarios in rice, which yields specific trait/stress enriched gene sets. Further, the GSAQ approach was more innovative and effective in performing gene set analysis with underlying QTLs and identifying QTL candidate genes than the existing approach. The GSAQ approach also provided two potential biological relevant criteria for performance analysis of gene selection methods. Based on this proposed approach, an R package, i.e., GSAQ ( https://cran.r-project.org/web/packages/GSAQ ) has been developed. The GSAQ approach provides a valuable platform for integrating the gene expression data with genetically rich QTL data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.