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{"documentCount":8,"nextId":8,"documentIds":{"0":"docs/pages/blog/gsea.mdx#a-quick-overview-of-gsea-analysis","1":"docs/pages/blog/gsea.mdx#why-gsea-analysis-","2":"docs/pages/blog/gsea.mdx#principles","3":"docs/pages/blog/gsea.mdx#footnote-label","4":"docs/pages/blog/#blog","5":"docs/pages/projects/#here-are-my-projects","6":"docs/pages/projects/#omicsverse","7":"docs/pages/projects/somavardb.mdx#somavardb"},"fieldIds":{"title":0,"titles":1,"text":2},"fieldLength":{"0":[6,1,1],"1":[4,6,90],"2":[1,6,1],"3":[1,6,70],"4":[1,1,1],"5":[4,1,1],"6":[1,4,56],"7":[1,1,9]},"averageFieldLength":[2.375,3.25,28.625],"storedFields":{"0":{"href":"/blog/gsea#a-quick-overview-of-gsea-analysis","html":"</header>\n","isPage":true,"text":"\n","title":"A quick overview of GSEA analysis","titles":[]},"1":{"href":"/blog/gsea#why-gsea-analysis-","html":"\n<p>Genome wide expression analysis has become become a mainstay of genomics research. However, there is still a wide range of tools for interpreting these gene expression profiles. They all have their advantages and disadvantages, and are still evolving. This, coupled with the fact that these studies rely on the testing of a large number of hypotheses and relatively small sample sizes, lead that whole-genome expression studies in particular, to be often not reproducible.\nThis is why reproducibility is one of the major challenges facing studies involving whole-genome expression data. <sup><a href=\"#user-content-fn-1\" id=\"user-content-fnref-1\" data-footnote-ref=\"true\" aria-describedby=\"footnote-label\">1</a></sup>, <sup><a href=\"#user-content-fn-2\" id=\"user-content-fnref-2\" data-footnote-ref=\"true\" aria-describedby=\"footnote-label\">2</a></sup>, <sup><a href=\"#user-content-fn-3\" id=\"user-content-fnref-3\" data-footnote-ref=\"true\" aria-describedby=\"footnote-label\">3</a></sup></p>\n<p>Finally, interpreting lists of thousands of differentially expressed genes is a tedious exercise for the biologist.</p>\n<p>The GSEA, by dezooming from the scale of the gene to that of the pathway. Improves the reproducibility of studies, while facilitating their interpetation.</p>\n","isPage":false,"text":"\nGenome wide expression analysis has become become a mainstay of genomics research. However, there is still a wide range of tools for interpreting these gene expression profiles. They all have their advantages and disadvantages, and are still evolving. This, coupled with the fact that these studies rely on the testing of a large number of hypotheses and relatively small sample sizes, lead that whole-genome expression studies in particular, to be often not reproducible.\nThis is why reproducibility is one of the major challenges facing studies involving whole-genome expression data. 1, 2, 3\nFinally, interpreting lists of thousands of differentially expressed genes is a tedious exercise for the biologist.\nThe GSEA, by dezooming from the scale of the gene to that of the pathway. Improves the reproducibility of studies, while facilitating their interpetation.\n","title":"Why GSEA Analysis ?","titles":["A quick overview of GSEA analysis"]},"2":{"href":"/blog/gsea#principles","html":"\n\n<section data-footnotes=\"true\" class=\"footnotes\">","isPage":false,"text":"\n\n","title":"Principles","titles":["A quick overview of GSEA analysis"]},"3":{"href":"/blog/gsea#footnote-label","html":"\n<ol>\n<li id=\"user-content-fn-1\">\n<p>Timothy E. Sweeney , Winston A. Haynes , Francesco Vallania , John P. Ioannidis\nand Purvesh Khatri. (2017). <em>Methods to increase reproducibility in differential gene expression via meta-analysis</em>. <strong>Nucleic Acids Research</strong>, Volume 45(Issue 1), Page Range. <a href=\"https://doi.org/10.1093/nar/gkw797\">DOI</a> <a href=\"#user-content-fnref-1\" data-footnote-backref=\"\" aria-label=\"Back to reference 1\" class=\"data-footnote-backref\">↩</a></p>\n</li>\n<li id=\"user-content-fn-2\">\n<p>Steven N Goodman , Daniele Fanelli , John P A Ioannidis. (2016). *What does research reproducibility mean? *. <strong>Sci Transl Med</strong>, 8(341),12. <a href=\"https://doi.org/10.1126/scitranslmed.aaf5027\">DOI</a> <a href=\"#user-content-fnref-2\" data-footnote-backref=\"\" aria-label=\"Back to reference 2\" class=\"data-footnote-backref\">↩</a></p>\n</li>\n<li id=\"user-content-fn-3\">\n<p>Lianbo Yu. (2020). <em>RNA-Seq Reproducibility Assessment of the Sequencing Quality Control Project</em>. <strong>Cancer Inform</strong>, Volume 19 <a href=\"https://doi.org/10.1177/1176935120922498\">DOI</a> <a href=\"#user-content-fnref-3\" data-footnote-backref=\"\" aria-label=\"Back to reference 3\" class=\"data-footnote-backref\">↩</a></p>\n</li>\n</ol>\n</section>","isPage":false,"text":"\n\n\nTimothy E. Sweeney , Winston A. Haynes , Francesco Vallania , John P. Ioannidis\nand Purvesh Khatri. (2017). Methods to increase reproducibility in differential gene expression via meta-analysis. Nucleic Acids Research, Volume 45(Issue 1), Page Range. DOI ↩\n\n\nSteven N Goodman , Daniele Fanelli , John P A Ioannidis. (2016). *What does research reproducibility mean? *. Sci Transl Med, 8(341),12. DOI ↩\n\n\nLianbo Yu. (2020). RNA-Seq Reproducibility Assessment of the Sequencing Quality Control Project. 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