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However, RNA-seq experiments are still small sample size experiments due to the cost. Recently, an increased focus has been on meta-analysis methods for integrated differential expression analysis for exploration of potential biomarkers. In this study, we propose a<jats:italic>p<\/jats:italic>-value combination method for meta-analysis of multiple independent but\u00a0related RNA-seq studies that accounts for sample size of a study and direction of expression of genes in individual studies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed method generalizes the inverse-normal method without an increase in statistical or computational complexity and does not pre- or post-hoc filter genes that have conflicting direction of expression in different studies. Thus, the proposed method, as compared to the inverse-normal, has better potential for the discovery of differentially expressed genes (DEGs) with potentially conflicting differential signals from multiple studies related to disease. We demonstrated the use of the proposed method in detection of biologically relevant DEGs in glioblastoma (GBM), the most aggressive brain cancer. Our approach notably enabled the identification of over-expressed tumour suppressor gene<jats:italic>RAD51<\/jats:italic>in GBM compared to healthy controls, which has recently been shown to be a target for inhibition to enhance radiosensitivity of GBM cells during treatment. Pathway analysis identified multiple aberrant GBM related pathways as well as novel regulators such as<jats:italic>TCF7L2<\/jats:italic>and<jats:italic>MAPT<\/jats:italic>as important upstream regulators in GBM.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed meta-analysis method generalizes the existing inverse-normal method by providing a way to establish differential expression status for genes with conflicting direction of expression in individual RNA-seq studies. Hence, leading to further exploration of them as potential biomarkers for the disease.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-022-04859-9","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T09:04:53Z","timestamp":1659690293000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Fused inverse-normal method for integrated differential expression analysis of RNA-seq data"],"prefix":"10.1186","volume":"23","author":[{"given":"Birbal","family":"Prasad","sequence":"first","affiliation":[]},{"given":"Xinzhong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,5]]},"reference":[{"issue":"9","key":"4859_CR1","doi-asserted-by":"publisher","first-page":"1509","DOI":"10.1101\/gr.079558.108","volume":"18","author":"JC Marioni","year":"2008","unstructured":"Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. 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Genome Biol. 2014;15(12):550.","journal-title":"Genome Biol"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04859-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-04859-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04859-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T20:24:23Z","timestamp":1727727863000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-04859-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,5]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["4859"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-04859-9","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,5]]},"assertion":[{"value":"21 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The datasets analysed during the current study are available on Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) public repository. RNA-seq datasets used and available on Gene Expression Omnibus (GEO) can be found using accession numbers GSE123892, GSE151352 and GSE125583. TCGA-GBM RNA-seq dataset can be accessed here:. An implementation of the proposed method can be found here:.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and materials"}},{"value":"The authors declare that they have no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"320"}}