1. High Throughput Data Analysis Karin Leiderman ViaLogy Southern California Bioinformatics Summer Institute at California State University, Los Angeles

2. Need for high throughput data analysis  Example: Have 150 genes each with a replicate on each microarray (300 spots) Need to monitor the red and green intensity levels across 20 microarrays, now there is a total of 6000 spots Would like to see if there is a pattern across genes or across arrays when the intensity is inspected in a decreasing order per gene Need to make sense of the data Will have to generate at least 150 graphs to look at how the arrays group, for each color, red and green and 150 more to look at the two intensities together Do you really want to make all of these graphs one by one in Excel? NO! So now, we code, code, code!!!

3. Things to think about first…  What format will the data come in? Need to be able to handle many formats, so the program should be easily changeable or ready for anything Are there any further calculations or manipulations of the data that need to take place? How can these be executed?  What type of graph will be the most efficient and understandable? First, what has your mentor asked for in the graph!? Second, what colors and formats will make it easily readable? Also, what kind of file should the graph come out as?.emf,.tiff,.gif?

4. Example data format: This is data for one gene…the format will be either: -This file extends 149*20 more lines: How do we parse out the data? -There will be 149 more files to read separately: How do we read in multiple files?

5. Let’s use Karin’s favorite: MATLAB!!!  Program can use one-file input or multiple-file input: For one file, need to create a list of the filenames Use C:\, as I call it “C prompt”, to create a text file of filenames from the directory where the files are located Figure out how to read these and then open them in MATLAB Want to iterate over the text list What function to use in MATLAB to read the files?  Turns out, ‘textread’ is the easiest one!

6. The text file of filenames might look something like this:

7. Special Thanks To:  The awesome team at ViaLogy  Everyone involved in the Southern California Bioinformatics Summer Institute  NIH and NSF