Concise Interpretation of MALDI Imaging Data by Probabilistic Latent Semantic Analysis (pLSA)

Application Note

Expression Proteomics

MT-111

MALDI-TOF

2012

ultraflex TOF/TOF flexImaging

Sören-Oliver Deininger, Klaus Meyer, Axel Walch; Bruker Daltonik and Institute of Pathology, Helmholtz-Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.

Bruker Daltonics Application Note MT-111, #284058

Abstract

MALDI imaging datasets can be very information-rich,
containing hundreds of mass signals. If the underlying
tissue sample is also complex – as is typically the case
with cancer samples – the analysis of individual mass
signals in their histological context can become very timeconsuming.
Mathematical ways of representing the data
in a concise fashion therefore become very important.
We describe here the use of probabilistic latent semantic
analysis (pLSA) for this purpose. The main advantage of
this technique is the fact that the components computed
by pLSA can be interpreted as real tissue components,
e.g cell types, even if the spatial resolution is not
sufficient to resolve these cells and their respective mass
spectrometric profiles.

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