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) 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 

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