New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...

8 CHAPTER 1. INTRODUCTION AND SURVEY
1. Detect contained signals (and filter out noise)
2. Evaluate the signals
3. Identify biomarkers (that is statistically significant differences between
the groups)
4. Build fingerprints and train classifiers using these fingerprints
5. Test performance (that is classification power) of the resulting classifiers
in independent clinical studies
A follow-up study then often tries to determine the underlying molecules to
link the fingerprints to e.g. metabolic pathways.
Part I: Detecting Fingerprints
There are many (still unsolved) problems associated with each of these pipeline
steps. For example, detecting even smallest but relevant signals in the raw
data - which is a complex mix of the real biological signals and (random and
systematic) noise introduced by the high throughput MS machines. In the
first part of this thesis we propose a solution to this problem: new statistic
driven approach that allows to analyze noise and to identify signals below the
commonly used signal-to-noise threshold 2 (chapter 3).
Additionall signals identified can be used in subsequent steps to build
better patterns for proteomic fingerprinting analysis. We believe that this will
foster identification of new biomarkers having not been detectable by most
algorithms currently available.
Other very important issues are also addressed, such as preprocessing the
raw signals (e.g. to reduce systematic noise, see section 3.3), reliable mapping
of detected signals across different spectra to allow comparison (section 3.6),
building robust and compact fingerprints (section 3.8) and finally using these
fingerprints to classify unknown spectra (section 3.8.5).
Part II: Medical Application
The algorithms and methods developed in this thesis can be combined to
an analysis pipeline for automated fingerprint detection from and analysis of
mass spectrometry data. This pipeline has been set up and equipped with a
web-frontend to allow access for remote scientists (for example in hospitals).
To prove the platform’s practical relevance it has been utilized in several
clinical studies. We could successfully detect fingerprints for different cancer
types (bladder, kidney, testicle, pancreas, colon and thyroid). Two of these
studies are presented in chapter 4.
Experiments have shown, that the fingerprints found by our algorithms
are missed by commercially available systems that are less sensitive than our
approach.
2 The thresholding method only regards signals if their height is above a certain value
determined by a noise-estimation step. A common setting for the minimum signal height is
three times the estimated noise level.