{"id":15940,"date":"2017-04-20T17:54:00","date_gmt":"2017-04-21T00:54:00","guid":{"rendered":"https:\/\/devwww.3cloudsolutions.com\/post\/data-visualization-for-bioinformatics-with-r-in-power-bi-2\/"},"modified":"2023-10-04T10:21:55","modified_gmt":"2023-10-04T17:21:55","slug":"data-visualization-for-bioinformatics-with-r-in-power-bi","status":"publish","type":"post","link":"https:\/\/3cloudsolutions.com\/resources\/data-visualization-for-bioinformatics-with-r-in-power-bi\/","title":{"rendered":"Data Visualization for Bioinformatics with R in Power BI"},"content":{"rendered":"

Whether you work in bioinformatics, computational biology, genomics, proteomics, or pharmacology, your data needs often differ vastly from a traditional business user. The file types and volume of data with which you are interacting are typically unique to these industries. The common thread among them is the importance of visualizing the data. With Microsoft Power BI<\/a> and its ability to integrate with R, you can easily see your data as well as the results of your analyses. Here, we will look at a few examples of visualizing data from bioinformatics-related areas using only R and Power BI.<\/p>\n

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\"bioinformatics-1.png\"<\/p>\n

As you may know, in bioinformatics, we\u00a0encounter many odd file types. Often,\u00a0these files can’t be opened in a\u00a0tabular viewer such as Excel or put into a traditional database table because their structure just doesn’t fit. However, using R, we can use parsers to\u00a0analyze and display our data. We can even blend this data with traditional data sources to enhance filtering capabilities and more.<\/p>\n

Survival Analysis<\/h2>\n

In our first example, we will implement a survival analysis of tumor DNA profiles. Common industries and uses for survival analyses include:<\/p>\n