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Difference in decade in exported .csv


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Hi,

I am new to Kaluza and trying to explore the various options in this s/w. What I noticed is there is some difference in decade in exported compansated .csv. The fluorescence intensities don't seem to match as in kaluza dot plot

In Kaluza plot, if population starts at 10^0 and in exported .csv, population starts at 10^3.

It would be great if someone could explain it in detail.

 

Thanks,

Nagaraj

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Hello Nagaraj,

 

Kaluza Analysis has a feature to enable comparison of data from different instruments. All data is processed using the full range from the instrument, but prior to display it is scaled to a 0 to 1024 range. When you choose to display this data as log, it is displayed on 4 decades by default. Because the maximum value of the scaled data is 1024, the highest displayed decade value is 10^3. When you add more decades it shows you additional decades closer to zero. (On a log plot you never actually see zero.) As you add decades you will eventually encounter the picket-fencing artifact. When you see this will depend on how much resolution your source data has. If you don't know the range of your data, use the information plot to display the $PnR keyword, where n is the number of the parameter you're interested in.

 

The Export Compensated Event Data (which I assume is what you're talking about) provides data at the full range in the file. The goal was to give you the data with as little processing as possible: Only compensation is applied, and if you don't want that just blank out the comp matrix before you export. One side effect of this minimal processing is that you don't see the same ranges in the exported data as you see in Kaluza. Of course, you can do your own scaling if you want it to match.

 

Here's an example:

 

The Gallios flow cytometer returns a 20-bit value for the area measurement. Kaluza is displaying all data on a 1024 scale, 1024 is 2^10, so it is scaling all Gallios area values by 1024. No information is being lost; it's just a simple scaling factor. So if the data was not scaled, the highest value on a log plot would be 2^20, which is 1,048,576, which would be slightly greater than 10^6. Displaying that at the default 4 decade scale, the left-most edge of the plot would be 10^2.

 

So if you have Gallios data that Kaluza shows a arithmetic mean of 7.34 for, and you export that data to CSV and load it into Excel, you can use the AVERAGE function to calculate the arithmetic mean. You'll get a value that's approximately 1024 times what Kaluza displays, something around 7,500. If you then divide that value by 1024 you'll get back to the original Kaluza value.

 

I've attached an XLS to this post to illustrate the example I've given.

 

Ernie

Scaling Example.xls

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