Gsea go term. Avoiding the pitfalls of gene set enrichment analysis with SetRank

Discussion in 'video' started by Vishicage , Tuesday, March 15, 2022 4:00:40 AM.

  1. Doutilar

    Doutilar

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    Indeed, applying a stricter cutoff, only affects the sensitivity performance of our method, as fewer gene sets will be finally ranked. One base change has the potential to affect the protein that results from that gene being expressed; however, it also has the potential to have no effect at all. Scand J Stat. Contrary to the sensitivity, the statistics for prioritisation and false-positive rate show that our method significantly outperforms all other methods available. There are several tools that do functional enrichment analysis, but I would strongly urge you to use GSEA. Nucleic Acids Res.
     
  2. Mirn

    Mirn

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    rutex.online › bioinformatics › comments › gsea_vs_go_enrichment.The next best solution to construct a background set is to take all genes that are positively identified at least once in any of the sample groups in the dataset being analyzed.
     
  3. Vusida

    Vusida

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    Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically.Suppose you've performed an RNA-Seq experiment and want to know whether the DNA repair pathway is affected in a global sense, you could use Gene set enrichment analysis to test this.
     
  4. Yozshushura

    Yozshushura

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    The principle of GSEA is to search for sets of genes that are significantly over-represented in a given list of genes, compared to a background.PLoS Comput Biol.
     
  5. Mezibar

    Mezibar

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    Fundamentally, GSEA is an analysis method and the Gene Ontology is a dataset. There are two different types of entities present in GO: i) genes.This web-interface allows a user to upload one or more tables of gene identifiers with associated p -values.
    Gsea go term. Gene set enrichment analysis
     
  6. Bradal

    Bradal

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    One of the main uses of the GO is to perform enrichment analysis on gene sets. For example, given a set of genes that are up-regulated under certain conditions.Loading Similar Posts.
     
  7. Taudal

    Taudal

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    GSEA at 25% FDR revealed five pathways enriched in degenera- tive embryos and four pathways enriched in blastocysts (Table 2). In addition, GO terms were.For this analysis, we only considered those datasets that are derived from brain tissue, as this is the most abundant tissue type in the entire corpus of benchmarking datasets 9 out of
     
  8. Nikotaxe

    Nikotaxe

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    Gene set enrichment analysis (GSEA) (also functional enrichment analysis) is a method to NASQAR currently supports GO Term and KEGG Pathway enrichment with all.Tarca et al.
     
  9. Jutaxe

    Jutaxe

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    GSEA differs from differential gene expression analysis in the sense that it might KEGG, and GO gene sets are overlapping in terms of component genes.Gene set enrichment analysis can be used to understand the changes that cells undergo during carcinogenesis and metastasis.
    Gsea go term. GO enrichment analysis
     
  10. Kegar

    Kegar

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    Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a pre-defined set of genes (ex: those beloging to a specific GO term.To detect if a transcript for a given gene is detected in any of the samples in a microarray dataset, we used a simple filtering approach based on two cutoffs.
     
  11. Meztimuro

    Meztimuro

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    Metascape is a biologist-oriented gene-list analysis portal.
     
  12. Tygogor

    Tygogor

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    forum? When using a technology that detects absolute transcript or peptide levels, such as RNA-seq or shotgun proteomics, a background set is easily compiled.
     
  13. Kajishura

    Kajishura

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    This observation means that, for the majority of datasets in the benchmarking corpus, these methods would not detect the target sets either when applying a p -value cutoff of 0.Forum Gsea go term
     
  14. Gardalrajas

    Gardalrajas

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    Its only purpose is to help us track usage for reports to our funding agencies.
    Gsea go term.
     
  15. Fenrishakar

    Fenrishakar

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    Next, the PageRank values from randomly generated networks with the same node and edge count as the original network, are determined.
     
  16. Taugore

    Taugore

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    forum? Edge thickness reflects the size of the intersection between two gene sets, using the Jaccard similarity.
     
  17. Fenritilar

    Fenritilar

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    Table 2 Benchmarking results for SetRank compared to other methods Full size table.
     
  18. Gak

    Gak

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    For example, given a set of genes that are up-regulated under certain conditions, an enrichment analysis will find which GO terms are over-represented or under-represented using annotations for that gene set.
     
  19. Tojar

    Tojar

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    In a first step, a primary p -value is calculated for every gene set in the collection.
     
  20. Tazahn

    Tazahn

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    Tools differ in the algorithms they use, the statistical tests they perform, and the frequency at which the underlying GO data are updated.
     
  21. Kizahn

    Kizahn

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    In both runs, the target gene sets were ranked in 10 out of 42 datasets.
     
  22. Tesida

    Tesida

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    We distinguish between three different types of edges: subsets, intersections, and overlaps see Fig.
    Gsea go term.
     
  23. Jujinn

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    Full size image.
     
  24. Dunris

    Dunris

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    Db database.
    Gsea go term.
     
  25. Vudogar

    Vudogar

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    The quality of an edge depends on the SetRank value of the originating node.
     
  26. Teran

    Teran

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    The gene ontology GO annotation for plant species and GO enrichment analysis is available.
    Gsea go term.
     
  27. Mezigis

    Mezigis

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    In this case, it is possible to determine which one of these two sets is the most significant, based on the p -value obtained after subtracting the intersection.
     
  28. Met

    Met

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    The SetRank value is determined by calculating the PageRank value for each node, considering only the overlap edges in the gene set network and using a damping factor of 0.
     
  29. Bakus

    Bakus

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    Suppose you've performed an RNA-Seq experiment and want to know whether the DNA repair pathway is affected in a global sense, you could use Gene set enrichment analysis to test this.
     
  30. Vinris

    Vinris

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    Background frequency and sample frequency Background frequency is the number of genes annotated to a GO term in the entire background set, while sample frequency is the number of genes annotated to that GO term in the input list.
     
  31. Faushura

    Faushura

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    Proceedings of the National Academy of Sciences.
     
  32. Zulkinos

    Zulkinos

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    forum? A prioritisation score of 0.
     
  33. Vudal

    Vudal

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    Integrative analysis of runx1 downstream pathways and target genes.
    Gsea go term.
     
  34. Fenrilabar

    Fenrilabar

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    The list could be GO terms, but is not limited to that.
     
  35. Male

    Male

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    Martian RNA?
     
  36. Mosar

    Mosar

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    Depending on whether the input gene list is ranked or not, a different method is used to calculate the primary p -values of all gene sets in the input gene set collection.Forum Gsea go term
    Gsea go term.
     
  37. Vurg

    Vurg

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    forum? However, this method of comparison is not sensitive enough to detect the subtle differences between the expression of individual genes, because diseases typically involve entire groups of genes.
     
  38. Kazracage

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    Provided by the Springer Nature SharedIt content-sharing initiative.
     
  39. Tulkis

    Tulkis

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    If we, however, define the sensitivity as the median of the initial p -values of the target gene sets, we obtain a value of 0.
    Gsea go term.
     
  40. Dishicage

    Dishicage

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    It builds on the idea of Jiang and Gentleman [ 17 ] to detect and remove false positive hits based on their overlap with other gene sets.
     
  41. Vodal

    Vodal

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    Falcon S, Gentleman R.
     
  42. Kacage

    Kacage

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    Gene Set Enrichment Analysis was developed [2] to focus on the changes of expression in groups of a priori defined gene sets.
     
  43. Meztizilkree

    Meztizilkree

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    The results are delivered through interactive and configurable interface.
     

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