Correlations: P53 Isoform Biosignatures vs Biomedical Parameters
Werner Van Belle1* - firstname.lastname@example.org, email@example.com
1- Bioinformatics Group
Norut IT; Research Park; 9294 Tromsø; Norway
This deliverable compares 132 various biological parameters agains P53 ISoforms on 2D Gels of AML and ALL Patients.
P53 AML ALL 2DGel Correlation
Werner Van Belle; Correlations: P53 Isoform Biosignatures vs Biomedical Parameters; 93 Gb; February 2007
This is a
report I wrote on the analysis I performed
correlations against biomedical parameters. The report is presented
here to show what can be achieved with this
technique and how a typical
like. Regardless of the realisation that money in
general comes directly/indirectly from the taxpayer, we honour the
current practice in biomedical science and do not present the data
online. Except for the FAB classifications which have been previously
published. If you have inquiries regarding the technique please contact
you have inquiries regarding P53 itself, AML or ALL then please contact
Correlation can be measured between any two numerical data sets.
case, the first data set is a set of images, and the second data set is
a set of biomedical parameters. We used 8 different image-sets.
: is an image set
containing the first gels made by Ingvild
: is an image set
containing the second gels made by Ingvild
: are all the
images made by Ingvild
: are all the images
made by Nina, excluding the X-ray images.
: are all the X-ray
images made by Nina
: is an
image set containing all of Ingvilds and
Ninas gels, excluding the X-ray images.
: is an
image set containing all of Ingvilds and Ninas
gels, including the X-ray images. I believe that since the acquisition
process is different between gels, these should be treated as
: is an
image set containing all of Ninas gels,
including the X-ray images. I believe that since the acquisition
process is different between gels, these should be treated as
unreliable. This is the analysis as done before.
to this collection of image sets,
we also have a collection of
parameter-sets. Each parameter-set is correlated to each of the 8
image sets. Of course, the limited overlap between the new and the old
parameters limits the
usability of the combined sets. And in cases that a parameter is
available for both
sets, it must be ensured that the new and old images can be pooled
together (meaning that Ingvild and Nina must have followed exactly the
same procedures). The image sets and parameters can be found in
(excel format), Report/imagesets-and-parameters.ods
(OpenOffice format) and Report/imagesets-and-parameters.html.
Those files are based on the spreadsheets as provided by Nina
data_Master Sheet.xls, Mastersheet_Nina.xls)
and Ingvild (Patient numbers.doc).
is a list of parameters which link
to html files. Each
parameter contains the image count, whether it is based on normal or
normalized images and the actual image. PDF files are also
available. In the PDF files, each image will list the number of
gel-images used in the top left corner. The image count for all
correlation-images is informative. Results produced with
few images might be biased by the little information available and must
be treated with caution.
full movies and images are direct
available in the directories
<parameter>/<parameter>-vs-old[-ns]. For instance
age/age-vs-old will contain the correlations between the age and the
old image set. The directory age/age-vs-old-ns, will contain the same
but will be based on normalized images.
the color reflects the
correlation as presented in the key next to the image. The
of an area reflects the joined mask based on the
standard deviation and the significance of that area. This is the same
mask as used in the Bioinformatics paper. A total overview of all
images is available as a PDF in everything.pdf.
have the same
color code as reflected in the key
contained in the 2D image. The height of a position reflects the
combined mask of standard deviation and significance, but in
addition to the 2D images the correlation strength itself is
integrated as well.
correlation measure used is the
spearman rank order correlation.
They were calculated for the original images and the scale-normalized
images. This process involves translating the median of the grey
values to 0 and scaling the standard deviation of the area to become 1.
The normalisation factors are determined based on
the area contained within the white rectangle. All measures outside the
white rectangle have a correct correlation but the significance might
be too low. We filtered the data in such a manner to ensure that no
outliers outside the white boundary would remove the information
contained within the white rectangle. In the 3D images these areas will
overlay images are provided for
your information. The overlay was
based on input given by nina regarding the alfa and delta positions. We
further added the antibody spot and information based on the ladder.
All these pseudo-calibration points were used in combination with a
pair-wise alignment algorithm to maximize the overlap between any two
gels. Every overlay image contains a red and a blue component. The
overlay of all gel images is in Overlays/overlay.png
The following table contains in the red channel the image and in the
blue channel the overlay. Note:
Due to technical reasons the images are flipped over the y-axis
available. See disclaimer above
gelimages below are in 16 bit .TIF
format as we received them for
See disclaimer above
Raw 24 bit
data produced by the correlation
program is saved to disk using
three files. Those are the correlation image, the standard deviation
image and the significance image. These can be combined as necessary to
have a proper visualisation (as done above). The data format for those
images is a lossless PNG compression in which the RGB bytes are used to
express one quantity ranging from the minimal value to the maximal
value. For instance, if we have a correlation ranging from -0.4 to 0.3
then this range will be linearly mapped to numbers ranging from 0 to
16777215. Those values are listed in a separate file
(Report/minmaxcor.* (.html, .ods, .xls,
.csv) and must accompany the image
before it is useful. The values listed in this file are extrema,
meaning that only 1 pixel
in the entire image might have this value, therefore we caution to use
this file for any interpretation of the results. The directory
raw24bit-cors contains the images for all of the calculated
Potential areas of
correlation process was repeated for
each image set with the
images 'as they are', and once more for each image set after
normalizing the image. This normalisation process has been described in
detail in the BMC Bioinformatics paper and seems to work relatively
well. However, a scientific approach should aim for the best techniques
available and normalisation does not necesarily outclass actual
could for instance have been very useful to improve on the following
we were unable to investigate the influence of the capturing
process (exposure time, gain etc..) to the correlations. Kodak did not
cooperate on this matter and our inquiry regarding their data-format
we were unable to assess the influence of the gel running time
since this parameter was not recorded.
gels were not always positioned at the same place, which is bad
know that the position on the plate has a strong impact on the results.
the camera seems unfocused and filthy. This should probably
been have cleaned
before acquiring the images.
calibration points were insufficient. The
ladder to the left is too far out of the p53 scope to be useful. The
actin spot could have been a useful position, if it was somehow related
to the 2D gel images.
of the reason we redid these
experiments was to have a better
understanding of these factors. It is a pity to notice that little
effort in that
direction has realized. Aside from this, there remain 3 other
points that might be of interest:
Bias Through Gel Removal
gels that are 'bad' is not
good. Typically those gels
have a very low signal or no signal at all. By removing them, one
the resulting correlations.
Measurements (still living is not dead)
is scientifically unsound to include
'clipped' parameters into
the set. For instance, the 'age' parameter includes still living
patients. These are marked with >48. It is incorrect to include this
value as 48. Instead this value should be removed since we do not
yet have full information on the variable. Where we were aware of such
conduct we included both the set as we received it and one with only
those fully known ages.
is not zero
is scientifically unsound to replace
an 'unknown' with 0.
This seem to have occurred in numerous places. Where we were aware of
cloned the parameter set and filtered out the zeros. This has happened
the survival parameter for instance.
computer resources necessary for
this analysis were slightly larger
than anticipated and it
might be interesting to shed some light on execution times. All times
are expressed on a 2.8 GHz Intel Pentium 4 processor with 1Gb of memory.
- creating the fine-tuned overlay alignment: 72h
- computing all the correlations: 85.55h, which produced 5.8 Gb of
- rendering of the images: at 5 hours per image, with 1416 images:
one machine this would take
approximately 300 days to finish.
Luckily we had the following hardware available:
- A PowerEdge Dell workhorse with 4 Dual-Core AMD Opteron(tm)
Processors 8218; Cache: 1024 Kb;
- 1 Intel(R) Pentium(R) 4 CPU 2.80GHz; Cache: 512 KB; Memory: 1004
- 2 Intel(R) Pentium(R) D CPU 3.00GHz; Cache: 1024 KB; Memory: 1006
- 2 AMD Athlon(tm) 64 Processors 3200+; Cache: 512 Kb; Memory: 1012
- 1 Intel(R) Pentium(R) 4 CPU 2.00GHz; Cache: 512 Kb; Memory: 1011
a joined effort, these
machines have been rendering for