Difference between revisions of "Bioinformatics"

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(Gene Expression)
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Potentially good things to read / tutorials, etc. ...
Potentially good things to read / tutorials, etc. ...
* R: [[R Programming - Getting Started]] - programs to install, reading, etc.
* R: [[R Programming - Getting Started]] - programs to install, reading, etc.
* Other courses like this one - [https://microbiology.columbia.edu/icqb Introduction to Computational & Quantitative Biology - Columbia Dept Microbiology & Immunology]
=Gene Expression=
=Gene Expression=

Revision as of 01:08, 21 January 2022

Additional Programs to Install

  • Compression - for those using Windows, install 7-Zip. MacOS and Linux natively support most compression formats that we will need.
  • R - first install R and then install RStudio Desktop (free).
  • Teams - ISU faculty/staff/students can install MS Teams for free along with other parts of Office 365. Start by logging into https://portal.office.com with your ISU credentials, click around looking for Teams (may have to click on "All Apps" or something like that), and once you get to Teams look for a link to download the desktop application (for Windows and Mac OS, not available for Linux). You can also use Teams in the browser.


Potentially good things to read / tutorials, etc. ...

Gene Expression

We start by getting into this GSE85331 dataset, described in this publication (and see supplementary information for how they processed/analyzed the data).

On your own computer, download the dataset and extract (uncompress) the file (on MacOS or Linux just double click it, on Windows use 7-Zip or something similar).


After extracting you can open the file in Excel, Sheets, or LibreOffice. Note that it is a tsv file. If you double click, your OS may not know what program to use to open it. So start your spreadsheet program and then open the file. Some things are not too painful to do in your spreadsheet program. For example, you should verify that the following are all correct...

  • Genes with highest H1_day0_0 values: SNORD97, SNHG25, EEF1A1, RPL38, RPS27.
  • Genes with highest H1_CM_0 values: H19, MYL7, RPL31, SNORD9, RPS27.
  • Number of genes (#rows - 1): 26257
  • Median value for H1_day0_0: 0.539942
  • Median value for H1_CM_0: 1.246015
  • Average value for H1_day0_0: 15.86772859
  • Average value for H1_CM_0: 16.4574767

It seems that this dataset might be normalized so that the average values for each column (sample) are similar.

And that is about all we want to do in the spreadsheet right now. You can save it as an xlsx or import into Google Sheets in case we want to do anything else manually with it.

R and R Studio

Let's see what we can do with the same file in R and R Studio. First you should install R and R Studio on your computer. See links above.