Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues

Abstract

Recently developed methods of ambient ionization allow the collection of mass spectrometric datasets for biological and medical applications at an unprecedented pace. One of the areas that could employ such analysis is neurosurgery. The fast in situ identification of dissected tissues could assist the neurosurgery procedure. In this paper tumor tissues of astrocytoma and glioblastoma are compared. The vast majority of the data representation methods are hard to use, as the number of features is high and the amount of samples is limited. Furthermore, the ratio of features and samples number restricts the use of many machine learning methods. The number of features could be reduced through feature selection algorithms or dimensionality reduction methods. Different algorithms of dimensionality reduction are considered along with the traditional noise thresholding for the mass spectra. From our analysis, the Isomap algorithm appears to be the most effective dimensionality reduction algorithm for negative mode, whereas the positive mode could be processed with a simple noise reduction by a threshold. Also, negative and positive mode correspond to different sample properties: negative mode is responsible for the inner variability and the details of the sample, whereas positive mode describes measurement in general.

State-of-the-art mass spectrometry imaging applications in biomedical research

“…One of the biggest challenges in clinical MSI concerns the validation of research outcomes due to the mismatch between the high number of dimensions as provided by omics technologies (“curse of dimensionality”) and the low number of samples. 29 In MSI, the number of detected ion signals defines the dimensionality and ranges from a few hundreds to several thousands. In contrast to this, the number of human samples for clinically motivated studies rarely surpasses 100 (not considering tissue microarray-based studies).…”

Lipid Profiles of Human Brain Tumors Obtained by High-Resolution Negative Mode Ambient Mass Spectrometry

“…It was shown that the molecular profiles of unmodified and damaged brain tissue are separable-various necrotized (necrotized tumor, necrotic tissue with necrotized vessels, necrotic tissue with tumor strain) and tumor (histologically pure tumor, tumor with necrosis, tumor lesions) tissues could be differentiated from each other as well as from the tumor boundary tissues [25]. The same data was further implemented to create classifiers for rapid identification of various tumors (glioblastoma, astrocytoma, meningioma) based on ambient mass spectrometry [34][35][36], which has become the basis for developing new ambient ionization techniques designed for clinical application [10]. On the other hand, the presented dataset, as it is an example of data representing actual data obtained in a clinic, was used as a model for developing an instrument for an interactive and automated tool for evaluating the stability and reproducibility of mass spectra [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], and for the unification of representations of high-and low-resolution mass spectra for further clinical implementation [23].…”

“…The same data was further implemented to create classifiers for rapid identification of various tumors (glioblastoma, astrocytoma, meningioma) based on ambient mass spectrometry [34][35][36], which has become the basis for developing new ambient ionization techniques designed for clinical application [10]. On the other hand, the presented dataset, as it is an example of data representing actual data obtained in a clinic, was used as a model for developing an instrument for an interactive and automated tool for evaluating the stability and reproducibility of mass spectra [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], and for the unification of representations of high-and low-resolution mass spectra for further clinical implementation [23].…”