#
Interpreting Results

QTube presents you with a color-coded table of values for the metrics
for each tube. Let's look at how it gets from your FCS files to the
final result.

### Example: samp1

The example we'll use is taken from the Example Data. The
dotplots for CD45 vs Side Scatter for Tubes 1 and 6 for the "samp1"
sample are shown
referenced to contours from the aggregate of the first 6 tubes in the
panel (tubes 7-10 were permealized for intracellular labels, so we
won't consider them for now):

Notice that Tube 1 (which is closely representative of all but Tube 6)
lines up
nicely with the contours, while Tube 6 shows a subtle shift
towards the origin. *This is the type of defect
we hope QTube will
notice. *Depending
on your particular analysis, such a shift may or may not be
problematic. You can tailor QTube's sensitivity using the
controls in the Preferences [ ] panel.

#### Low Resolution

We've run QTube on this example (tubes 1-6), at a Low resolution and
using Side Scatter and CD45. The results are
shown as follows:

Note that Tube 6 looks a bit worse than the others - as expected,
knowing in advance
what the dotplots look like. Let's examine how this result
is arrived at. Take a look at the Max Metric first. Remember
that this metric is the maximum absolute value of the base-2 logarithm
of the fingprint. Let's plot the fingerprints on a base-2 log y
axis:

The Tube 6 fingerprint is shown in blue, the others in black.
Note that the Tube 6 fingerprint deviates significantly from 0. The
other
fingerprints are all hovering pretty closely around 0 (which is the log
of 1.0, so the ratio of the densities in these bins is close to the
expected density for the aggregate) and none of them gets outside of
the
green.

#### High Resolution

Now, we've used the same six tubes, this time using 'High' resolution.
Here's what the results look like:

Note that now the Max Metric looks a little worse. Why is that?
To answer this question we'll again look under the hood at the
fingerprints themselves:

Notice that the fingerprint is "denser". That's because the
bins
into which the data have been divided are smaller, so it takes more of
them to cover the space.

The Log2 fingerprint for Tube 6 is again drawn in blue, the others in
black.
Notice that the Tube 6 fingerprint wiggles around a bit more - we're
measuring density in smaller regions, so local fluctations aren't
averaged out as much as at lower resolution. Also note that where the
Tube 6 fingerprint is very high,
the others are a bit low (and vice versa). This is because the higher
density that
Tube 6 contributes to the aggregate in
that subregion has "pulled" bins towards it, making them
smaller, and leaving less volume for events in the other tubes.
Therefore, the other tubes extend slightly into the yellow
region. Another way of looking at this is that the average value
of a fingerprint element across the tubes will be 1.0, so if one of
them is anomalously high, the others will be slightly lower such that
their average remains 1.0.

#### The Standard Deviation Metric

If we collect all of the fingerprint values together for each tube, we
can calculate their standard deviation. That's what this
metric
is all about. Looking at the boxplot below we can see that
the
variance for Tube 6 is much higher than the other tubes.
Given
our choice for the colors ranges, this lands Tube 6 in the yellow.
This metric is useful for looking across the entire
fingerprint
and not zeroing in on a single anomalous bin.

####
Some final thoughts

It's important that you develop your own intuition using your data in
QTube. Experiment around with data you know to be good, and
other
data that you think have some inconsistencies such as the ones we've
illustrated. By going back and forth between QTube results
and
your favorite way of looking at your data you will begin to understand
where you should set resolution and color values for your application.