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Data normalization
The GenePix software enabled you to convert your microarray image pixels to an intensity
digital data for each spot. The next step consists in normalizing data within each experiment
to eliminate systematic errors and scaling all of them so that they can be compared.
How to prepare the data file to normalization:
GenePix analysis performs a tabulated result data file (GPR for GenePix Results) containing
all the infomations on spot signals. Open this file with Excel to format it for the normalization
step. To achieve this, complete each of these steps:
1. Open the result file (gpr) with Excel and use tabulation as column separators.
2. Save it under another name to keep your raw data file safe, and work on your file
copy. Open the file copy and duplicate your spreadsheet to keep an eye on the raw
data you are going to work on.
3. The file begins with a header that summarizes image and scan parameters. Filter out
this header, just keep the column header. Pay attention to work on file copies so that
you always keep your raw data safe, just in case...
4. The first handling you have to perform consists in keeping the spots useful to the
analysis and throwing away those that are not compliant. Sort (with the Excel sort
function) the spots that have flag values equal to -50 (absent spots), -75 (empty spots)
and -100 (bad spots you filtered out manually). The conserved spots have a flag value
equal or greater than 0. When the first sort step is completed, note the number of spots
left for analysis. It will give you an appreciation of the quality of your hybridization.
5. The second handling is necessary to format your file to VARAN input file. VARAN is
an on-line normalization tool. It also enables the sorting of significantly expressed
genes. The on-line tutorial describes the input file format needed and its behaviour.
Here is the part dedicated to the input file format:
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6. You have to choose whether you use the mean or the median of the intensities to
estimate the spot general intensity. The median is currently used and advised because
of its capacity to smooth the pixel differences that appear if the hybridization is too
heterogeneous. Keep the columns that correspond to the gene name, the identifier, the
Cy3 signal (F532 median), the Cy3 background (B532 median), the Cy5 signal (F635
median), the Cy5 background (B635 median) and the block identifier. This identifier
is use to keep the spatial information when you perform a Loess normalization.
Remember Cy3 is the green dye and Cy5 the red one.
7. VARAN accepts only one annotation column. As we want to keep the gene name and
its unique identifier, a useful way to discriminate replicated spots on your slide
(Remember the S. cerevisiae slides are double genome microarrays, each spot has a
replicate), we have to concatenate the Name and ID columns. To achieve this, insert
two new columns between the ID and F532 columns. In one of these, apply the
concatenate function (you currently find Excel functions in the “Insertion menu”) to
the Name and ID separated by a semicolon “;”.
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8. Once you have achieved the concatenation, you have to copy the values of the whole
column and paste it with the special paste in the empty column left. This enables you
to paste the values in the columns and not the function. You can now suppress the ID
and Name columns.
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9. The last sorting step consists in deleting all the saturated spots from the file. the
saturated spot in one channel will not have a meaningful ratio. It makes no sense to
keep them. Sort the spot by decreasing Cy3 intensities, then delete all the lines where
intensity values are bigger than a fixed threshold (for example, you can choose
55000). Repeat this for the Cy5 intensities. You have now suppressed all the saturated
10. Delete the header line and save your file as a text file (tabulations as separator). You
are now ready to import your file in VARAN.
VARAN use:
1. Once you have reached the VARAN index page, click on the “VARAN Generator”
link to get the script we will use.
2. The different tool options are described in the on-line help. Here are the main options
we will use: as we want to apply the Loess normalization to our data, we need to fix a
window overlap percentage to calculate the regression curves.
3. Launch VARAN and note your query identifier in case you need to access it later on.
4. The VARAN result page enables you to visualize your experiment MA-plot and
contains a link to your normalised data file. You may also have a significatively
expressed gene list if ever VARAN completed the asymptote design. Report yourself
to the on-line help for further details on the results files.
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