Biology Reference
In-Depth Information
parts. One part will be mixed with aliquots from all other samples
and then used as an internal standard on each gel. The remaining
part has to be analyzed individually in the DIGE experiment. On
each gel, two samples (ideally one per experimental group) and one
internal standard are applied. From the statistical point of view, it is
disadvantageous to pool the samples within the experimental groups
for diminution of the number of gels (leaving the overall patient-
sample size unchanged). Such pooling strategy does not take into
account the biological variation within the experimental groups. It
leads to a dramatic reduction of the power. After the gel run, the gel
images are loaded into the DeCyder software package. Then spots
are detected in the differential in-gel analysis (DIA) module and
matched in the biological variation analysis (BVA) module.
A Student's
t
test-based statistical evaluation of the DIGE experiment
can be done in the BVA module or in the extended data analysis
(EDA) module of the DeCyder software package (see Note 6).
The software calculates a list with the FDR-adjusted
p
values and
the
average ratio
for each spot. The
average ratio
is a measure
for the ratio of the two mean standardized abundances of the
two experimental groups (see Note 7). To determine those
spots which can be regarded as potential biomarkers, fi rst, sort the
spot list according to the
p
value of the
t
test. Then, exclude all
spots with a
p
value above the fi xed FDR level (usually 0.05). The
expression levels of all remaining spots differ signifi cantly between
the experimental groups. However, most of them probably show a
very small difference in expression. To exclude such spots, sort the
list of the signifi cant spots according to the
average ratio.
Then
eliminate all spots with a small
average ratio
(e.g., between −1.30
and +1.30; see Note 8). The remaining spots may be regarded as
potential biomarkers. The list of potential biomarkers might be
considered too long to include them all in the validation experiment.
Then the researcher can apply more stringent selection criteria
increasing the
average ratio
(i.e., ±1.5 or ±2.0) or alternatively
select the proteins for the validation experiment due to biological
reasons. For practical reasons, usually not more than 10-15 potential
biomarkers are selected for validation.
3.1.3. Statistical Evaluation
of the Results from
the DIGE Experiment
A DIGE experiment is very time intensive and not suited for
routine diagnostic analysis. It is therefore advisable to change the
analytical method before starting the validation experiment. Ideally,
the analytical method used in the validation experiment should
allow high-throughput analysis at low costs. Such methods
include ELISA, Western blotting, and protein biochip. But in
principle, a validation experiment may also be performed using a
DIGE platform. However, it should be taken into account that the
validation experiment is only applicable to confi rm biomarkers
found in the discovery experiment and it is not eligible to fi nd and
validate new biomarkers.
3.2. Validation
Experiment
3.2.1. Type of Assay
Search WWH ::
Custom Search