Spatial self-supervised Peak Learning and correlation-based Evaluation of peak picking in Mass Spectrometry Imaging

TL;DR

An autoencoder-based spatial self-supervised peak learning neural network that selects spatially structured peaks by learning an attention mask leveraging both spatial and spectral information is proposed that consistently outperforms state-of-the-art peak picking methods by selecting spatially structured peaks.

Abstract

Mass spectrometry imaging (MSI) enables label-free visualization of molecular distributions across tissue samples but generates large and complex datasets that require effective peak picking to reduce data size while preserving meaningful biological information. Existing peak picking approaches perform inconsistently across heterogeneous datasets, and their evaluation is often limited to synthetic data or manually selected ion images that do not fully represent real-world challenges in MSI. To address these limitations, we propose an autoencoder-based spatial self-supervised peak learning neural network that selects spatially structured peaks by learning an attention mask leveraging both spatial and spectral information. We further introduce an evaluation procedure based on expert-annotated segmentation masks, allowing a more representative and spatially grounded assessment of peak picking performance. We evaluate our approach on four diverse public MSI datasets using our proposed evaluation procedure. Our approach consistently outperforms state-of-the-art peak picking methods by selecting spatially structured peaks, thus demonstrating its efficacy. These results highlight the value of our spatial self-supervised network in comparison to contemporary state-of-the-art methods. The evaluation procedure can be readily applied to new MSI datasets, thereby providing a consistent and robust framework for the comparison of spatially structured peak picking methods across different datasets.

Figures (8)

• Figure 1: Our proposed spatial self-supervised peak learning pipeline, S3PL, for spatially structured peak picking. The network receives an input comprising a spectral patch $x$ of a complete spectrum and its neighboring spectra. During training, the network learns to identify the most informative peaks by learning an attention mask. In order to identify the peaks, the frozen 3D convolution is applied to the entire dataset, resulting in the filtering of $z$ peaks per spectral patch $x$. In the final selection, the $n$ most frequent peaks of the peak selection yield the final peak list.
• Figure 2: Our proposed evaluation procedure for peak picking of spatially structured peaks. (a) PCC values of all ion images and a corresponding binary mask are computed and sorted. (b) All ion images with a PCC value equal to or greater than $T_{PCC}$ for any annotated class are classified as positive. Steps (a) and (b) are repeated for every class. (c) We calculate F1-scores using thresholds $T_{PCC} \in \{0.3, 0.4, 0.5, 0.6\}$ and report the mean of these four F1-scores, $mSCF1$, as the final metric.
• Figure 3: Detailed comparison of our spatial self-supervised peak learning autoencoder, S3PL, with state-of-the-art peak picking methods gibb2012maldiquantlieb2020peakinglese2019sputnikabdelmoula2021peak using our proposed evaluation procedure. The figures show the $mSCF1$ scores for each tissue section within each dataset separately.
• Figure 4: Visualization of selected peaks by our S3PL method on the GIST dataset abu2019quantitative in the representative mass range $m/z$$563$ to $573$. The depicted mean spectrum contains many distinct peaks, which do not necessarily correspond to spatially structured ion images. S3PL only selects peaks with spatial structure.
• Figure 5: Ablation study on the spectral patch size $p$ across three public datasets abdelmoula2022massnetbemis2019cardinalworkflowsinglese2017deep. The $mSCF1$ score is determined by averaging the results of all tissue sections in a dataset. The best patch size highly depends on the dataset abdelmoula2022massnetbemis2019cardinalworkflowsinglese2017deep.