Data collections over individual studies

Collections of medical data from many studies are better for understanding disease than single studies. Conclusions linking genetic mutations to diseases in individual studies have often been wrong due to small sample sizes. By publicly aggregating the data from many papers together the data becomes larger and more powerful. Merging and comparing cross study data takes powerful normalization tools. Luckily our data normalization tools are getting more and more powerful. One tool is ComBat to remove batch effects in studies: https://www.bu.edu/jlab/wp-assets/ComBat/Abstract.html available from Biocondcutor, https://www.bioconductor.org/ for R, https://cran.r-project.org/.

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