tofsims

tofsims analyzes Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) 2D image data to perform mass calibration, peak picking and integration, multivariate analyses, and quantitative measurements such as cell wall thickness from ToF-SIMS datasets.

Key Features:

• Data Import and Preprocessing: Supports import of ToF-SIMS 2D image data from Iontof and Ulvac-Phi systems and both raw and preprocessed datasets, and performs mass calibration, peak picking, and peak integration.
• Advanced Data Processing: Provides data binning, scaling, image subsetting, and visualization for handling and inspecting large ToF-SIMS images.
• Multivariate Analysis Tools: Implements Principal Component Analysis (PCA), Multivariate Curve Resolution (MCR), Mean Centered Factor analysis (MAF), and Multi-way N-Fold (MNF) for exploratory and pattern-detection analyses.
• Cell Wall Thickness Estimation: Implements a computational method for estimating cell wall thickness from ToF-SIMS images by analyzing intersection lengths of random lines on binary masks, with an R implementation.

Scientific Applications:

• Chemical imaging of heterogeneous materials: Enables detailed chemical imaging and spatially resolved analysis of heterogeneous samples using ToF-SIMS datasets.
• Cell wall thickness estimation in wood: Applies random-line analysis of ToF-SIMS images to estimate cell wall thickness in wood samples (Surface and Interface Analysis, DOI: 10.1002/sia.5661), relevant to forest biotechnology and assessment of transgenic trees.

Methodology:

Project random lines over a binary mask of a ToF-SIMS image, collect length values where lines intersect cell walls, and iteratively analyze the shortest intersection lengths (sections normal to cell walls) to estimate cell wall thickness; method implemented in R.