Assists users to analyze mass spectrometry imaging data. SCILS Lab includes a lot of functionalities allowing researchers to display multiple samples in both 2D and 3D and permitting a multitude of applications in pharmaceutical, medical, and industrial research. This software is composed of several features among which comparative analysis for uncovering discriminative m/z-markers, classification model calculation based on training data and classification of new samples, or extraction of underlying trends.
Organizes and processes multimodal mass spectrometry imaging data. HDI Software allows users to interrogate complex imaging data. The software enables the integration of multimodal imaging experiments for SYNAPT G2-Si as well as DESI imaging for Xevo G2-XS Mass Spectrometers. Some of its benefits are the image clarity and integrity, as well as Sequential sample acquisition for accelerated throughput.
MSI has emerged as a technique suited to resolving metabolism within complex cellular systems; where understanding the spatial variation of metabolism is vital for making a transformative impact on science. Unfortunately, the scale of MSI data and complexity of analysis presents an insurmountable barrier to scientists where a single 2D-image may be many gigabytes and comparison of multiple images is beyond the capabilities available to most scientists. The OpenMSI project will overcome these challenges, allowing broad use of MSI to researchers by providing a web-based gateway for management and storage of MSI data, the visualization of the hyper-dimensional contents of the data, and the statistical analysis.
Provides functionalities for statistical analysis. Imaging Mass Solution is a program that can be used for simple comparison analysis or data conversion. It can perform principal component analysis (PCA) analysis, region of interest (ROI) analysis, and hierarchical cluster analysis (HCA). Moreover, it also assists researchers in displaying different types of m/z image.
Consists of an active learning classification method. AL-RF is a program based on an iterative active learning algorithm that utilizes random forests in combination with a training utility value criterion. For instance, this method can be applied for annotating secondary ion mass spectrometry (SIMS) images. Furthermore, for a learning iteration, this tool determines that pixel of the mass spectrometry (MS) image that is deemed most useful for improving and updating the current classifier.