SMS scnews item created by John Ormerod at Sun 31 Aug 2014 2137
Type: Seminar
Distribution: World
Expiry: 6 Sep 2014
Calendar1: 5 Sep 2014 1400-1500
CalLoc1: Carslaw 173
Auth: jormerod@124-169-149-136.dyn.iinet.net.au (jormerod) in SMS-WASM

# Statistics Seminar: Inge Koch (Adelaide) -- Analysis of Spatial Data from Proteomics Imaging Mass Spectrometry

Mass spectrometry (MS) has become a versatile and powerful tool in
proteomics for the analysis of complex biological systems. Unlike
the common MS techniques the more recent imaging mass spectrometry
(IMS)  preserves the spatial distribution inherent in tissue samples.
IMS data consist of tens of thousands of spectra measured over a
large range of masses, the variables.  Each spectrum arises from a
grid point on the surface of a tissue section. Motivated by the
requirements in cancer research to differentiate cell populations
and tissue types of such data accurately and efficiently, we
consider two approaches  -- normalisation and feature extraction --
and we illustrate these approaches on IMS data obtained from tissue
sections of patients with ovarian cancer.

In proteomics, normalisation refers to the process of scaling spectra
in order to correct for artefacts occurring during  data acquisition.
Normalising the mass spectra is essential in IMS for an interpretation
of the data.  We propose a new and efficient normalisation of the mass
spectra, based on peak intensity correction (PIC), and illustrate its
effect for individual mass images and cluster maps.

The selection of mass variables which distinguish cancer tissue from
non-cancerous tissue regions -- or responders from non-responders --
is an important step towards identification of biomarkers.  We
consider a combined cluster analysis and feature extraction approach
for derived binary mass data.  This approach exploits the difference
in proportions of occurrence (DIPPS) statistic of subsets of data in
the selection and ranking of variables.  We apply these ideas to the
cancer and non-cancerous regions of the tissue sections, and  we
summarise the best’ variables in a single image which has a natural
interpretation.
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