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\n  \n 2019\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n SMAGEXP: a galaxy tool suite for transcriptomics data meta-analysis.\n \n \n \n \n\n\n \n Blanck, S.; and Marot, G.\n\n\n \n\n\n\n GigaScience, 8(2). February 2019.\n \n\n\n\n
\n\n\n\n \n \n \"SMAGEXP:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{blanck_smagexp:_2019,\n\ttitle = {{SMAGEXP}: a galaxy tool suite for transcriptomics data meta-analysis},\n\tvolume = {8},\n\tcopyright = {All rights reserved},\n\tshorttitle = {{SMAGEXP}},\n\turl = {https://academic.oup.com/gigascience/article/8/2/giy167/5304063},\n\tdoi = {10.1093/gigascience/giy167},\n\tabstract = {With the proliferation of available microarray and high-throughput sequencing experiments in the public domain, the use of meta-analysis methods increases. In these experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably enhance the statistical power and give more accurate results. For those purposes, it combines either effect sizes or results of single studies in an appropriate manner. R packages metaMA and metaRNASeq perform meta-analysis on microarray and next generation sequencing (NGS) data, respectively. They are not interchangeable as they rely on statistical modeling specific to each technology.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2019-03-07},\n\tjournal = {GigaScience},\n\tauthor = {Blanck, Samuel and Marot, Guillemette},\n\tmonth = feb,\n\tyear = {2019},\n}\n
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\n With the proliferation of available microarray and high-throughput sequencing experiments in the public domain, the use of meta-analysis methods increases. In these experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably enhance the statistical power and give more accurate results. For those purposes, it combines either effect sizes or results of single studies in an appropriate manner. R packages metaMA and metaRNASeq perform meta-analysis on microarray and next generation sequencing (NGS) data, respectively. They are not interchangeable as they rely on statistical modeling specific to each technology.\n
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\n  \n 2014\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n MPAgenomics: an R package for multi-patient analysis of genomic markers.\n \n \n \n \n\n\n \n Grimonprez, Q.; Celisse, A.; Blanck, S.; Cheok, M.; Figeac, M.; and Marot, G.\n\n\n \n\n\n\n BMC Bioinformatics, 15(1). December 2014.\n \n\n\n\n
\n\n\n\n \n \n \"MPAgenomics:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{grimonprez_mpagenomics:_2014,\n\ttitle = {{MPAgenomics}: an {R} package for multi-patient analysis of genomic markers},\n\tvolume = {15},\n\tcopyright = {All rights reserved},\n\tissn = {1471-2105},\n\tshorttitle = {{MPAgenomics}},\n\turl = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-014-0394-y},\n\tdoi = {10.1186/s12859-014-0394-y},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2019-03-07},\n\tjournal = {BMC Bioinformatics},\n\tauthor = {Grimonprez, Quentin and Celisse, Alain and Blanck, Samuel and Cheok, Meyling and Figeac, Martin and Marot, Guillemette},\n\tmonth = dec,\n\tyear = {2014},\n}\n\n
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