The first step in working with microarray data, in general, is to gather and format all of your data files. In most cases, this can be a tedious task. In R, the first step is to import your data.
1. Create a Microsoft Excel spreadsheet with the names of all raw data files in the experiment. In this particular experiment with sponge samples, we had 9 chips (8 samples per chip). Assign each chip/slide its own unique number 1-x (where x is the total number of chips in the experiment). These raw intensity data files can be .gpr or .txt files exported from Gene Pix Pro image processing software. Gene Pix Pro creates and reads .tiff files from the microarray laser scanner (ours is an Axon 4200). We use single-channel microarrays (Cy3). This is what the Excel file will look like:
2. Save As Type Text (tab delimited) (*.txt) with the filename “targets.txt” (or whatever you want to name your file).
3. In the Linux Terminal, start R.
4. Use the following R commands to import the linear models for microarray data (limma) library, see which working directory you’re in, if you’re not in the one you want to be in, change the working director to let the R environment know where to look for your files, and then read the “targets.txt” file.
1 1 253295110067_2012-08-01.txt
2 2 253295110050_2012-07-25.txt
3 3 253295110075_2012-09-20.txt
4 4 253295110077_2012-09-13.txt
5 5 253295110079_2012-09-06.txt
6 6 253295110065_2012-08-09.txt
7 7 253295110035_2012-08-23.txt
8 8 253295110062_2012-08-16.txt
9 9 253295110059_2012-08-30.txt
This targets file, now that it has been read by the R environment, can be used to further process the raw intensity data.