Clustering Your Data

Protocol Developed by Hilary Clay (class of 2001) Swarthmore College (with Dr. Liz Vallen)

Assorted Advice collected from GCAT-L

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What clustering means: This program will look at two or more sets of experiments and find those spots and ORFs which have a high correlation. This means it looks for sets of genes which both have bright red spots, or sets which both have bright green spots, or sets where one experiment has bright red and another has bright green, etc. For example: in our experiment, we used the Red color to indicate our experimental conditions, and the Green to indicate control. But the experiment we clustered with did just the opposite: they used Green for experimental and Red for control. Therefore, if I wanted to find those genes which were overexpressed in experimental conditions, I would need to find areas where my slide was bright Red and the other slide was bright Green. What the clustering program does is put all of these color sets next to each other, so that by clicking on a node I can view at once all genes which are bright Red on my slide and bright Green on the other slide. If all of your experiments used Green for experimental conditions, you would want to find all those spots where the green color was bright for all of the experiments, and the clustering program would put all of these genes next to each other for easy viewing.

To Analyze data on SMD: (http://genome-www4.stanford.edu/MicroArray/SMD/)

The first thing to do is download Michael Eisen's data analysis software (http://rana.lbl.gov/EisenSoftware.htm). There is a link to his page on the SMD web page under software. You will have to register on his site, and this process is free for anyone using the software for non-profit purposes. The two programs you need to get are Cluster and Treeview, for Windows.

GETTING YOUR DATA:

Your data should be posted on the SMD home page, and you will need to find out where it is located. The easiest way to do this is to go to the public search button.

If you want to find your experiment alone:

Go to search by List Data and pick some parameter you can recognize. For example, I know my slide number is yO1n010, so I can go to List by Experiment ID and change the "sorted by header" category to "SlideName."

From there I can peruse the data and find my experiment.

Once I locate my slide I click on the Experiment ID link and it will bring me to a page where I set up my data retrieval parameters. Because my experiment used the Red color for the experimental conditions, I chose to sort by R/G Normalized Mean, descending. This means I will get a list of genes which have the brightest red color (i.e., the experimental conditions caused these genes to be expressed over the control) and the list will show this data from the brightest (most expressed) to the least bright (least expressed).

Under display, you can pick those elements you want to find in your spreadsheet, like gene name, process, and function. You can highlight those categories which are not adjacent by holding down the control key. You can also filter your data if you want, and adjust it to, say, only show spots that were flagged, or to only show numbers if they are above a certain value.

From here you click Submit and a web page will come up with your assorted data. This page can also be downloaded if you click on "make downloadable file of all data" before hitting submit, and can be saved as an Excel file.

If you want to cluster your data with other experiments:

Go back to public search and click on Basic Search. Pick a results type to search in, for example, Experiment.

From here you can find your organism (in my case, S. cerevisiae) and find your group where your data has been posted (in my case, GCAT).

To cluster, you will need to hit "Data Retrieval and Analysis" NOT "Display data." This will come up with the individual names of experiments, and you can chose which experiments you wish to cluster together (in my case, I would highlight the two Swarthmore college experiments).

Once you have selected the experiments you wish to cluster, you hit the Data Retrieval and Analysis button again.

This will bring you to a page where you set your data retrieval parameters, which you should fill out as follows:

• Gene selection options: If you are assessing all of your results, leave it on the default (Use all genes/clones on arrays). Retrieve data by SUID.
• Gene filtering: I did not filter my data at this point, so I unclick the "use one of" box. However, you can filter your data here and play around with these parameters.
• Biological data to select: Leave it on the default (Select data from Oracle) and set those parameters which you wish to look at, like Gene name, process, and function. IMPORTANT: if your experiment name is lengthy and your individual experiment names are similar, you might not be able to tell which band corresponds to which experiment later in Treeview. To avoid this problem, you should find out which slide goes with which experiment and click the "use slidename" box in this window.
• Data selection options: Leave the default as "Log(base2) of R/G Normalized Ratio (mean)" and filter the data as follows. Under the first box, make sure "active" is checked, and set the scrollbar to CH2IN_MEAN/CH2BN_MEDIAN. on the next scrollbar under that, select "gt" and set the number to 1.2. You can set this number higher (like 1.3 or 1.5) if you wish to filter out more genes, but it isn't necessary at this point. Then go to the next box over and make sure "active" is checked, and set this scrollbar to CH1I_MEAN/CH1B_MEDIAN, and set it to "gt" and to "1.2" or whatever number you settled on.

From there, submit your query. It may take some time to load.

When its done, click on "Download Preclustering File." This will take you to your data page, and you will need to save this page as a text file.

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CLUSTERING YOUR DATA:

Open the cluster program.

Click on Load File and find the text document you just saved. It will tell you that it cant be opened; ignore it.

Under filter genes, click on the % Present box, and decide what percent of data you want to look at. The higher your %, the more genes will get cut out, and they are eliminated by lack of brightness. 80% is a good place to start.

Then click on the Filter button. The program will then tell you how many genes passed through your filter. In my case, I used 80% as my filter, and it showed about 1200 out of 6000 genes. Once you have decided on the number of genes you want to look at, hit the accept button. If you don't hit the accept button, it will try to do all your data and will take much more time.

Next click on the Hierarchical Clustering Tab. Here you can decide whether or not to cluster your arrays as well as your genes. The cluster box under genes should be checked, but NOT the Calculate weights box. If you want to cluster your arrays (I didn't) then you should make sure the Cluster box under Array is checked, otherwise make sure it is unchecked, again, don't click on the Calculate weights box.

When you are all set hit the Average Linkage Clustering button and the program will cluster your data. This can take a long time. When it is done, it will create two files, a cdt file (this is for Treeview) and a gtr file.

VIEWING YOUR DATA:

Open the Treeview program. Under File, select Load and find your cdt file.

When it opens, you will be presented with a picture view of your clustered data on the left. If you click on the lines in the pictures, all genes past that node will appear on the right side of the screen with the gene name, function, and process, as well as a close-up view of your experiments side by side. In addition, you can drag over genes with your mouse and they will appear on the right, or you can click on individual spots (genes) and look at their information.

From here, you just peruse your data and see what you find!

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Advice from Barbara Dunn at SMD (Standford Microarray Database)

Clustering Directions

Data Retrieval Parameters