To achieve that combination of dot and contour mentioned
below you can use the map function in WinMDI or make very
nice overlay graphs using WinMDI and paintbrush. I tend to
use the old 1.3.4 version as it does true colour gating and
paste the plots into paintbrush (remember to set the 2D size
to 256x256). I than use the contour plot and paste it to
the same picture. When you then click the frame it produces
a nice overlay of contours on dots. The colourgating also
reveals that what you see as a single cluster is made up
from dots of different colour which immediately tells you
that there is no natural preference of dot density or
contour but a selection of display fit for the purpose that
demonstrates the point to be made. But if the articles get
judged by their artistic value instead of their scientific
content.....
The Deskjet 1600CM on our XL can also translate the colours
in distinguishable gray scale which is very nice for b&w
reproduction.
If you want to get rid of warts you have endless ways of
data manipulation of which gating has probably the biggest
impact, pixel resolution smoothing following. A lot of
people do not show any plots in their publications so you
can not even guess on the underlying data. If they do,
the manipulations are usually obvious to the skilled
cytometrist but could do with some explanation for the
average reader.
Happily plodding along
Gerhard.Nebe-von-Caron@unilever.com
______________________________ Reply Separator _________________________________
Subject: Contour plots & smoothing: rights and wrongs
Author: Roederer@Beadle.Stanford.EDU at INTERNET
Date: 24/09/97 22:55
> Since I believe in showing the real data, warts and all,
> I avoid using contour plots
Howard,
I must take issue with this. Contour plots (when properly computed) do not
inaccurately display bivariate data. In fact, they can be much more informative
than even color (or gray-scale) dot plots, which are much more difficult for
most people to readily interpret. It's not difficult to make up a series of
test plots, shown in both formats, and demonstrate that the inexperienced person
will more readily estimate the population frequency in a contour plot than a
color-dot plot. This, ultimately, is the goal in graphical presentation of
data.
Of course, you will agree that dot plots are completely inappropriate.
(Everyone: please stop publishing data with single-color dot plots!)
You also stated that "smoothing" makes the data "look better" than it is. This
is also not entirely correct--proper smoothing algorithms simply make the
contour plot look like it would if you were to collect a huge number of events.
In other words, proper density estimation algorithms, which are those that
employ a variable kernel-width smoothing algorithm, do not distort the data
presentation, and, in general, make it easier to interpret by mere humans. Dave
Parks and Marty Bigos have discussed these issues at length in various chapters
on data analysis (for example, in the Handbook of Experimental Immunology).
The main downside of contour plots is that data outside the last contour is
generally not shown. This problem has a simple solution: by showing outlying
events together with contour plots: thus, the contours give you the frequency
estimation that they are so good at, but the outliers will shown the low
frequency events. This format combines the best qualities of both presentation
styles.
mr
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