The use of random projections for the analysis of mass spectrometry imaging data.

Josephine Bunch,Andrew D Palmer,Iain B Styles

doi:10.1007/s13361-014-1024-7

Abstract

The ‘curse of dimensionality’ imposes fundamental limits on the analysis of the large, information rich datasets that are produced by mass spectrometry imaging. Additionally, such datasets are often too large to be analyzed as a whole and so dimensionality reduction is required before further analysis can be performed. We investigate the use of simple random projections for the dimensionality reduction of mass spectrometry imaging data and examine how they enable efficient and fast segmentation using k-means clustering. The method is computationally efficient and can be implemented such that only one spectrum is needed in memory at any time. We use this technique to reveal histologically significant regions within MALDI images of diseased human liver. Segmentation results achieved following a reduction in the dimensionality of the data by more than 99% (without peak picking) showed that histologic changes due to disease can be automatically visualized from molecular images. Graphical ᅟ Electronic supplementary materialThe online version of this article (doi:10.1007/s13361-014-1024-7) contains supplementary material, which is available to authorized users.

Highlights

We investigate the use of simple random projections for the dimensionality reduction of mass spectrometry imaging data and examine how they enable efficient and fast segmentation using k-means clustering
The determination of molecular profiles from individual tissue types is central to the understanding of their biological function, and direct chemical analysis of tissue using mass spectrometry imaging (MSI) is an established tool for determining profiles encompassing a broad range of molecules within a single imaging experiment [7, 29]
We have evaluated the use of random projections for dimensionality reduction in MSI on a benchmark dataset whose histologic features have previously been identified using several approaches to MSI visualization[14]

Summary

Introduction

The determination of molecular profiles from individual tissue types is central to the understanding of their biological function, and direct chemical analysis of tissue using mass spectrometry imaging (MSI) is an established tool for determining profiles encompassing a broad range of molecules within a single imaging experiment [7, 29]. We investigate the use of simple random projections for the dimensionality reduction of mass spectrometry imaging data and examine how they enable efficient and fast segmentation using k-means clustering.

Results

Conclusion