PUMA with Agilent arrays
Maitreya Dunham September
2007
Open the Tiff-splitter
program from the Agilent directory in the start menu.
Split all the arrays.
Using SFTP, upload the
following files for each array into your incoming directory on loader: red
tiff, green tiff, shp file, txt file
Log in to the
database. Under Enter Data, click
Experiments and Results.
Click Enter a New
Experiment into the Database.
Select Agilent, Agilent,
and Saccharomyces cerevisiae from the 3 pulldown menus. Click Enter a New Experiment.
Fill out the experiment
description form. Choose the print
from the menu. Make sure all the
files match. Pick a logical
category and subcategory. If the
reference was in the red channel, click the dye swap box. Select your collaborators from the list
to give them access.
Put in as much information
as possible. You will probably not
go back and add in extra documentation later. Do it now.
Submit the form to the
database.
You will get an email
confirming that the files have been submitted to the database, then another one
when they have been actually placed in the database. Go to the URL in the second email and look at the submission
data. Make sure everything looks
OK.
To look at the data, go to
the search section, select any subcategory or user filters, and hit data
retrieval and analysis.
Select the experiments you
want to analyze from the list.
Choose Data Retrieval and
Analysis.
I typically retrieve data
by SUID, which averages all the spots that are for the same gene, as long as
they pass your filters. On
occasion, you may want to retrieve by spot to make sure your replicates are
actually correlating.
Select any biological
annotations you want.
Choose what label you want
on each experiment.
Proceed to Data Filtering.
I usually retrieve the Log
(base2)(REDsignal/GREENsignal).
Invert any dye swaps.
Choose your filtering
criteria. I usually just use
significantly above background in either channel (i.e., 1 OR 2)
If you would like to
actually inspect the spots, click the box next to retrieve spot coordinates.
Download the resulting .pcl file, or proceed to filtering/clustering as desired. Or add it to your repository.