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VAMPIRE: Two-channel Microarray Tutorial 2005.02.17 This tutorial will take you through the typical steps needed to interpret a set of two-channel microarray data. The data set used for this tutorial was obtained from an AfCS study on the effects of LPS on a RAW264.7 macrophage cell line. We will use this set to explore the response of these cells after 1 hour of treatment. Sample Editor The first step will be to load the data set into the VAMPIRE microarray analysis platform through the sample editor. You will load the file “tutorial.2-channel.txt” as a table of measurements on the AfCS16K array. You may enter whatever description you wish. Note the file format of the tutorial file. Lines that are blank or are preceded by the # character are ignored. The first interpreted line contains the titles of each of the samples contained in the file. The first column contains the names of each feature. Each successive column contains gene expression measurements for each sample. Note: Data should not be log-transformed before loading into VAMPIRE. Normalized data from RMA, CORGON, dChip, etc should be used with caution, as these tools have profound effects on the variance structure, and can prevent the variance structure of the data from being adequately modeled. For Affymetrix chips, we recommend MAS 5.0/GCOS scores. For Agilent chips, we recommend the processed signal intensities. Group Editor Next, you will learn how to group related samples. ...
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VAMPIRE: Two-channel Microarray Tutorial
2005.02.17
This tutorial will take you through the typical steps needed to interpret a set of two-channel
microarray data. The data set used for this tutorial was obtained from an AfCS study on the effects of
LPS on a RAW264.7 macrophage cell line. We will use this set to explore the response of these cells
after 1 hour of treatment.
Sample Editor
The first step will be to load the data set into the VAMPIRE microarray analysis platform through the
sample editor. You will load the file “tutorial.2-channel.txt” as a
table
of measurements on the
AfCS16K
array. You may enter whatever description you wish. Note the file format of the tutorial file.
Lines that are blank or are preceded by the # character are ignored. The first interpreted line contains
the titles of each of the samples contained in the file. The first column contains the names of each
feature. Each successive column contains gene expression measurements for each sample.
Note: Data should
not
be log-transformed before loading into VAMPIRE. Normalized data from
RMA, CORGON, dChip, etc should be used with caution, as these tools have profound effects
on the variance structure, and can prevent the variance structure of the data from being
adequately modeled. For Affymetrix chips, we recommend MAS 5.0/GCOS scores. For Agilent
chips, we recommend the processed signal intensities.
Group Editor
Next, you will learn how to group related samples. For the paired analysis that we will be performing,
it is necessary to create not only groups for the two conditions that we wish to compare, but also
groups to pair samples that are measured on the same array.
Select the
AfCS16K
array type and create the following groups:
Group name
Contents
control
con1, con2
lps
lps1, lps2
array1
con1, lps1
array2
con2, lps2
The first two groups, “control” and “lps” are groups for the two treatment conditions that we wish to
compare. We would like to find out whether there is a systematic difference in gene expression
between control-treated macrophages and LPS-treated macrophages. The latter two groups, “array1”
and “array2” will be used to pair these samples together. Since measurements made on the same
microarray are likely to move together, it is important for the statistical method to recognize which
samples were measured off the same array.
Category Editor
Create a new category that contains all of the “pairing” groups. You will see the importance of this
step later on.
Select the
AfCS16K
array type and create the following category:
Category name
Contents
array
array1, array2
Variance Modeler
With all of the necessary groups and categories defined, you will need to create a paired variance
model to estimate the error structure of the data set that you’ve submitted. Create a model named
“conlps”. Select the
AfCS16K
array type, and create a “paired” model.
Select the following:
Sample group 1 – control
Sample group 2 – lps
Pairing category – array
Once you’ve clicked on “Build Model”, a job will be submitted to the database, which will be
processed in the order which it was received. If no other jobs are running, this step will typically take
about 20 minutes. In the meantime, we may go ahead and create the statistical test that we wish to run.
When the variance modeling job is completed, the model itself may be viewed by selecting it from the
navigation bar on the left. Parameter values and their MCMC simulation errors will be reported in the
results section of the page. In this section, there are a number of critical values that must be examined
to ensure the reliability of this analysis.
Check the following:
1. cutoff(quantile) should be a value greater than 0 and less than 0.8.
2. the A parameters should be similar between the two experimental conditions
3. the B parameters should be similar between the two experimental conditions
4. rhoA and rhoB will ideally be values greater than zero and less than 1
If any of these conditions are not true, the results of the significance test should be treated with caution,
because it is likely that the cutoff procedure was not able to identify a stable estimate of the variance
parameters. This can occur for a number of reasons:
1. the raw data was pre-processed with a complex normalization algorithm, introducing
normalization artifact
2. the raw data contains control probes that do not share the variance structure of the rest of the
data set (Agilent arrays)
Significance Tester
Finally, you will create a significance test based on the model that you created to identify which array
features are differentially-expressed between the two experimental conditions. Select the
AfCS16K
array type, and create a “paired” test.
Select the following:
Sample group 1 – control
Sample group 2 – lps
Pairing category – array
Variance model – conlps
After an additional 10 minutes, this job will complete and you will be able to download the results of
the VAMPIRE analysis. Simply select the significance threshold that you desire, and click download.
The results may be downloaded either as a tab-delimited text file or as a VAMPIREResults XML file.
GOby
GOby has two functions that may be useful for the interpretation of microarray data.
1. The “Annotate” function may be used to construct an annotated table of differentially-
expressed genes from multiple statistical tests.
2. Alternatively, you may wish to use GOby to identify functional groups that are
overrepresented in the list of differentially-expressed features. Click on “New” in the
navigation bar on the left. Then, simply select the test you wish to interpret, select a
significance threshold, and click “analyze”. The GOby analysis job will take approximately
30 minutes to complete. The results may subsequently be viewed as a series of HTML web
pages, or downloaded as GObyReport XML documents.
