Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactive m/z mapping and exploration.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches

[1] § MATERIALS AND METHODS › Quantitative evaluation of the MSI visualizations › Evaluation metrics ↔ msi_visual/metrics.py, lines 130–257 · score 0.56 · distance correlation, Pearson, MSI visualization, Euclidean, metric, cosine
[2] § MATERIALS AND METHODS › Visualization methods › Saliency optimization ↔ msi_visual/saliency_opt.py, lines 133–181 · score 0.51 · soft ranking, PyTorch, saliency, optimization

Paper

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The authors' code

Python · 268 lines · 11 KB · MIT · 1 match

import scipy
from msi_visual.normalization import spatial_total_ion_count, total_ion_count, median_ion
from sklearn.metrics.pairwise import euclidean_distances, cosine_similarity
from scipy.stats import entropy
import random
import time
import sys
import numpy as np
import matplotlib
from PIL import Image
import matplotlib.pyplot as plt
import cv2
from zadu import zadu
from sklearn.manifold import trustworthiness
def smoothness_saliency_metrics(cosine, maxabs, outputs):
max_rank = np.maximum(
cosine.argsort().argsort(),
maxabs.argsort().argsort())
cosine_rank = cosine.argsort().argsort()
result_rank = outputs.argsort().argsort()
result = {}
corr_cosine = scipy.stats.pearsonr(cosine, outputs).statistic
corr_maxabs = scipy.stats.pearsonr(maxabs, outputs).statistic
spearman_cosine = scipy.stats.spearmanr(cosine, outputs).statistic
spearman_maxabs = scipy.stats.spearmanr(maxabs, outputs).statistic
N = len(outputs)
for gamma in [0, 2.0]:
#max_rank = max_rank + 1e-6
saliency_30 = (((result_rank - max_rank + N * 0.3) > 0)
* max_rank**gamma).sum() / sum(max_rank**gamma)
saliency_20 = (((result_rank - max_rank + N * 0.2) > 0)
* max_rank**gamma).sum() / sum(max_rank**gamma)
saliency_10 = (((result_rank - max_rank + N * 0.1) > 0)
* max_rank**gamma).sum() / sum(max_rank**gamma)
smoothness_30 = (((cosine_rank - result_rank - N * 0.3) < 0)
* (N - cosine_rank)**gamma).sum() / sum((N - cosine_rank)**gamma)
smoothness_20 = (((cosine_rank - result_rank - N * 0.2) < 0)
* (N - cosine_rank)**gamma).sum() / sum((N - cosine_rank)**gamma)
smoothness_10 = (((cosine_rank - result_rank - N * 0.1) < 0)
* (N - cosine_rank)**gamma).sum() / sum((N - cosine_rank)**gamma)
saliency_20 = (((result_rank - max_rank + N * 0.2) > 0)
* max_rank**gamma).sum() / sum(max_rank**gamma)
result[f"Saliency Δ=20% gamma={gamma}"] = saliency_20
result[f"Smoothness Δ=20% gamma={gamma}"] = smoothness_20
result[f"Saliency Δ=30% gamma={gamma}"] = saliency_30
result[f"Smoothness Δ=30% gamma={gamma}"] = smoothness_30
result[f"Saliency Δ=10% gamma={gamma}"] = saliency_10
result[f"Smoothness Δ=10% gamma={gamma}"] = smoothness_10
result[f"Avg. Saliency gamma={gamma}"] = (saliency_10 + saliency_20 + saliency_30) / 3
result[f"Avg. Smoothness gamma={gamma}"] = (smoothness_10 + smoothness_20 + smoothness_30) / 3
result["Correlation Cosine"] = corr_cosine
result["Correlation L-∞"] = corr_maxabs
result["Spearman Cosine"] = spearman_cosine
result["Spearman L-∞"] = spearman_maxabs
return result
class RandomPairSampler:
def __init__(self, img, num_samples, mask):
self.pairs = self.generate_pairs(img, num_samples, mask)
def generate_pairs(self, img, num_samples, mask):
nrow, ncol = img.shape[0], img.shape[1]
points = np.mgrid[:nrow, :ncol].reshape(2, -1).T
points = list(points)
img_mask = np.uint8(img.max(axis=-1) > 0) * 255
if mask is not None:
img_mask[mask == 0] = 0
points = [(a, b) for a, b in points if img_mask[a, b] > 0]
pairs = []
for _ in range(min(len(points), num_samples)):
point_a = random.choice(points)
point_b = random.choice(points)
pairs.append((point_a, point_b))
return pairs
def get_input_distances(self, img):
data = {}
distances = ["L-2", "Cosine", "L-∞", "Output"]
for distance in distances:
data[distance] = []
for index, (point_a, point_b) in enumerate(self.pairs):
a, b = img[point_a[0], point_a[1]], img[point_b[0], point_b[1]]
data["L-2"].append(np.linalg.norm(a - b, 2))
data['Cosine'].append(
1 -
cosine_similarity(
np.array(
[a]),
np.array(
[b]))[
0,
0])
data['L-∞'].append(np.abs(a - b).max())
for key in data:
data[key] = np.float32(data[key])
return data
def get_output_distance(self, visualization):
result = []
for point_a, point_b in self.pairs:
a, b = visualization[point_a[0], point_a[1]
], visualization[point_b[0], point_b[1]]
a = np.float32(a)
b = np.float32(b)
d = np.linalg.norm(a - b, 2)
result.append(d)
return np.float32(result)
class MSIVisualizationMetrics:
def __init__(self, normalized, visualization, mask=None, num_samples=30000):
self.img_mask = np.uint8(normalized.max(axis=-1) > 0) * 255
self.img_mask_reshaped = self.img_mask.reshape(self.img_mask.shape[0] * self.img_mask.shape[1])
indices = [i for i in range(len(self.img_mask_reshaped)) if self.img_mask_reshaped[i] > 0]
if len(indices) <= num_samples:
self.random_indices = indices
else:
self.random_indices = random.sample(indices, num_samples // 5)
self.data_subset = normalized.reshape(-1, normalized.shape[-1])
self.visualization_subset = visualization.reshape((self.data_subset.shape[0], -1))
self.data_subset = self.data_subset[self.random_indices]
self.visualization_subset = self.visualization_subset[self.random_indices]
self.sampler = RandomPairSampler(normalized, num_samples, mask)
self.data = self.generate_input_output_samples(
normalized, cv2.cvtColor(
visualization, cv2.COLOR_RGB2LAB), self.sampler)
def generate_input_output_samples(
self, normalized, visualization, sampler):
data = sampler.get_input_distances(normalized)
data["Output"] = sampler.get_output_distance(visualization)
return data
def get_metrics(self):
cosine = self.data["Cosine"]
maxabs = self.data["L-∞"]
outputs = self.data["Output"]
metrics = smoothness_saliency_metrics(cosine, maxabs, outputs)
spec = [{"id": "mrre", "params": { "k": 100 },}, {"id": "lcmc", "params": { "k": 100 }}]
t0 = time.time()
scores = zadu.ZADU(spec, self.data_subset).measure(self.visualization_subset)
for zadu_metric in scores:
for m in zadu_metric:
metrics[m] = zadu_metric[m]
print("zadu took", time.time() - t0)
t0 = time.time()
trustworthiness_score = trustworthiness(self.data_subset, self.visualization_subset, metric='euclidean', n_neighbors=100)
trustworthiness_score_chevbyshev = trustworthiness(self.data_subset, self.visualization_subset, metric='chebyshev', n_neighbors=100)
metrics["Trustworthiness"] = trustworthiness_score
metrics["Trustworthiness L-∞"] = trustworthiness_score_chevbyshev
print(time.time() - t0, "trustwo")
return metrics
def get_correlation_scatter_plot(self, title=None):
cosine = self.data["Cosine"]
maxabs = self.data["L-∞"]
outputs = self.data["Output"]
metrics = self.get_metrics()
fig = plt.figure()
ax = fig.add_subplot()
max_rank = np.maximum(
cosine.argsort().argsort(),
maxabs.argsort().argsort())
# corr = scipy.stats.pearsonr(max_rank, outputs).statistic
expected_rank = max_rank
result_rank = outputs.argsort().argsort()
diff_rank = np.abs(result_rank - expected_rank)
cm = matplotlib.pyplot.get_cmap("RdYlGn_r")
colors = cm(np.linspace(0, 1, len(outputs)))
N = len(diff_rank) // 2
cs = [colors[diff_rank[i]]
for i in range(len(outputs))] # could be done with numpy's repmat
matplotlib.pyplot.scatter(cosine, maxabs, color=cs, s=10, alpha=0.5)
sm = plt.cm.ScalarMappable(cmap=cm)
sm.set_clim(vmin=0, vmax=1)
plt.colorbar(sm, ax=plt.gca())
ax.set_xlabel('m/z values Cosine distance')
ax.set_ylabel('m/z values L-∞ distance')
if title:
ax.set_title(title)
for name, value in metrics.items():
plt.plot([], [], ' ', label=f"{name} {value:.3f}")
plt.legend()
return fig
def get_correlation_plot(self, title=None):
cosine = self.data["Cosine"]
maxabs = self.data["L-∞"]
outputs = self.data["Output"]
fig, ax = plt.subplots()
for inputs, name in zip([cosine, maxabs], ["Cosine", "L-∞"]):
sorted_distances = sorted(inputs)
indices = list(
range(
len(sorted_distances) // 10,
len(sorted_distances),
len(sorted_distances) // 10))
indices.append(len(sorted_distances) - 1)
graph_distances, graph_corrs = [], []
for index in indices:
d = sorted_distances[index]
relevant_indices = [
i for i in range(
len(inputs)) if inputs[i] < d]
corr = scipy.stats.pearsonr(
inputs[relevant_indices],
outputs[relevant_indices]).statistic
graph_distances.append(
int(100 * index / len(sorted_distances)))
graph_corrs.append(corr)
plt.plot(graph_distances, graph_corrs, label=name)
plt.legend()
if title:
plt.title(
'Visualization distance correlation with spectra\n' +
title)
else:
plt.title('Visualization distance correlation with spectra')
plt.xlabel('Maximum input distance percentile')
plt.ylabel('Pearson rank correlation')
ax.set_yticks(list(np.arange(0, np.max(corr) + 0.05, 0.05)))
return fig
if __name__ == "__main__":
img = np.load(sys.argv[1])
visualization = np.array(Image.open(sys.argv[2]))
normalized = total_ion_count(img)
random.seed(0)
metrics = MSIVisualizationMetrics(normalized, visualization)
print(metrics.get_metrics())
metrics.get_correlation_scatter_plot()
plt.show()

metrics.py, under MIT · at the source

Overview

Authors: Jacob Gildenblat¹, Jens Pahnke^1,2,3,4,5,6

ORCID iDs: Jacob Gildenblat, Jens Pahnke

Pahnke Lab, Oslo, Norway
Translational Neurodegeneration Research and Neuropathology Lab, Department of Clinical Medicine, Medical Faculty, University of Oslo, Sognsvannsveien 20, Oslo NO-0372, Norway
Section of Neuropathology Research, Department of Pathology, Division of Laboratory Medicine, Oslo University Hospital, Sognsvannsveien 20, Oslo NO-0372, Norway
Institute of Nutritional Medicine, University of Lübeck and University Medical Center Schleswig-Holstein, Ratzeburger Allee 160, Lübeck D-23538, Germany
Department of Neuromedicine and Neuroscience, The Faculty of Medicine and Life Sciences, University of Latvia, Jelgavas iela 3, Rīga LV-1004, Latvia
Department of Neurobiology, School of Neurobiology, Biochemistry and Biophysics, The Georg S. Wise Faculty of Life Sciences, Tel Aviv University, G. S. Wise Street, Ramat Aviv IL-6997801, Israel

Institutions: Oslo University Hospital (Norway); Tel Aviv University (Israel); University of Oslo (Norway); University of Latvia (Latvia); University of Lübeck (Germany)

Journal: Science advances, volume 12, issue 35, article eaed3650

Dates: received 24 October 2025; accepted 17 July 2026; published online 26 August 2026; in print August 2026

Type: Research article · Language: English

License: CC BY-NC

Identifiers: DOI 10.1126/sciadv.aed3650 · PMID 42647616 · PMCID PMC13510675 · OpenAlex W7204261474

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), other (modality), mouse (organism), methods / tools (subfield)

Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging

MeSH: Image Processing, Computer-Assisted*, Mass Spectrometry*, Animals, Brain, Kidney, Lipidomics, Mice, Software (* major topic)

Topic: Mass Spectrometry Techniques and Applications (Spectroscopy, Chemistry), according to OpenAlex

Funding: Helse Sør-Øst RHF (2022046); Norges Forskningsrådet (327571, 295910); Nationalforeningen for folkehelse (Demenspris 2025, Norway); European Innovation Council (OPTIPATH 7D, 101185769)

Citations: not cited yet (Europe PMC); 58 references in the paper

Abstract

Mass spectrometry imaging (MSI) produces high-dimensional molecular data, but practical interpretation remains limited by visualizations that incompletely preserve global structure. We present MSI-VISUAL, an open-source framework for interactive MSI analysis that integrates truthful dimensionality reduction visualizations with region-of-interest selection, statistical comparison, and direct mass/charge ratio (m/z) mapping. MSI-VISUAL introduces four visualization strategies: SALO and SPEAR (optimization-based methods designed to improve global structure preservation across distance metrics) and TOP3 and PR3D (lightweight approaches for memory-efficient, rapid visualization of large datasets). Across benchmarks and lipidomics case studies, including mouse brain and kidney pathology examples, the proposed methods outperform commonly used alternatives in our benchmarks, improve detection of subtle tissue differences, and reveal fine molecular-anatomical patterns that support biological insights. These results establish MSI-VISUAL as a scalable framework for discovery-oriented and diagnostic MSI workflows.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

Zenodo 19465293

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data, code, and materials availability:”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (70 files), Pillow (44 files), OpenCV (43 files), scikit-learn (30 files), Matplotlib (20 files), PyTorch (10 files), SciPy (10 files), pandas (6 files), TensorFlow (5 files), XGBoost (4 files), UMAP (3 files), Keras (2 files), SHAP (2 files), NetworkX (1 file), Numba (1 file), scikit-image (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

96 files

app/main.py — Python, 26 lines
app/pages/dimensionality_reduction_train.py — Python, 128 lines
app/pages/explore.py — Python, 217 lines
app/pages/extract.py — Python, 33 lines
app/pages/help.py — Python, 16 lines
app/pages/pipeline.py — Python, 119 lines
app/pages/saliency_optimization.py — Python, 135 lines
app/pages/seg_train.py — Python, 158 lines
app/pages/show_ion_images.py — Python, 84 lines
app/pages/viewer.py — Python, 88 lines
compare.ipynb — Jupyter, 58 lines
compare_extractions.ipynb — Jupyter, 52 lines
msi_visual/__init__.py — Python, 1 line
msi_visual/app/utils/extraction.py — Python, 53 lines
msi_visual/app/utils/pipeline.py — Python, 104 lines
msi_visual/app/utils/viewer.py — Python, 661 lines
msi_visual/app_utils/__init__.py — Python, 1 line
msi_visual/app_utils/extraction_info.py — Python, 41 lines
msi_visual/auto_colorization.py — Python, 32 lines
msi_visual/avgmz.py — Python, 45 lines
msi_visual/avgmz_nmf_segmentation.py — Python, 104 lines
msi_visual/base_dim_reduction.py — Python, 57 lines
msi_visual/blend.py — Python, 31 lines
msi_visual/component_umap.py — Python, 62 lines
msi_visual/extract/base_msi_to_numpy.py — Python, 125 lines
msi_visual/extract/bruker_tims_to_numpy.py — Python, 87 lines
msi_visual/extract/bruker_tsf_to_numpy.py — Python, 45 lines
msi_visual/extract/pymzml_to_numpy.py — Python, 25 lines
msi_visual/extract/timsdata.py — Python, 415 lines
msi_visual/extract/tsfdata.py — Python, 189 lines
msi_visual/extraction.py — Python, 23 lines
msi_visual/fastica_3d.py — Python, 6 lines
msi_visual/isomap_3d.py — Python, 6 lines
msi_visual/kmeans_segmentation.py — Python, 132 lines
msi_visual/lda_3d.py — Python, 5 lines
msi_visual/lle_3d.py — Python, 6 lines
msi_visual/mds_3d.py — Python, 6 lines
msi_visual/metrics.py — Python, 268 lines, 1 match
msi_visual/nmf_3d.py — Python, 70 lines
msi_visual/nmf_segmentation.py — Python, 111 lines
msi_visual/nonparametric_umap.ipynb — Jupyter, 40 lines
msi_visual/nonparametric_umap.py — Python, 61 lines
msi_visual/normalization.py — Python, 21 lines
msi_visual/objects.py — Python, 37 lines
msi_visual/outliers.py — Python, 73 lines
msi_visual/pacmac_3d.py — Python, 24 lines
msi_visual/parametric_umap.py — Python, 98 lines
msi_visual/pca_3d.py — Python, 70 lines
msi_visual/percentile_ratio.py — Python, 111 lines
msi_visual/phate3d.py — Python, 28 lines
msi_visual/rare_nmf_segmentation.py — Python, 109 lines
msi_visual/saliency_clustering_opt.py — Python, 86 lines
msi_visual/saliency_opt.py — Python, 181 lines, 1 match
msi_visual/segmentation.py — Python, 98 lines
msi_visual/segmentation_visualization_comb.py — Python, 61 lines
msi_visual/spearman_opt.py — Python, 172 lines
msi_visual/spectral_3d.py — Python, 6 lines
msi_visual/stdmz.py — Python, 45 lines
msi_visual/supervised/annotations.py — Python, 149 lines
msi_visual/trimap_3d.py — Python, 7 lines
msi_visual/tsne_3d.py — Python, 6 lines
msi_visual/umap_nmf_segmentation.py — Python, 100 lines
msi_visual/utils.py — Python, 76 lines
msi_visual/visualizations.py — Python, 310 lines
notebooks/CRF.ipynb — Jupyter, 202 lines
notebooks/NMF.ipynb — Jupyter, 203 lines
notebooks/UMAP.ipynb — Jupyter, 311 lines
notebooks/backup_umap.ipynb — Jupyter, 233 lines
notebooks/domain_adaptation.ipynb — Jupyter, 1 line
notebooks/extract_notebook.ipynb — Jupyter, 39 lines
notebooks/hdbscan.ipynb — Jupyter, 27 lines
notebooks/heatmap.ipynb — Jupyter, 81 lines
notebooks/mz.ipynb — Jupyter, 44 lines
notebooks/nmf_segmentation.ipynb — Jupyter, 544 lines
notebooks/seg.ipynb — Jupyter, 672 lines
notebooks/selection.ipynb — Jupyter, 1 line
notebooks/train_seg.ipynb — Jupyter, 134 lines
notebooks/umap_train.ipynb — Jupyter, 67 lines
scripts/benchmark.py — Python, 128 lines
scripts/benchmark_comprehensive.py — Python, 174 lines
scripts/create_graph_data.py — Python, 100 lines
scripts/extraction/extract.py — Python, 145 lines
scripts/extraction/extract_bruker_tims.py — Python, 37 lines
scripts/extraction/extract_bruker_tsf.py — Python, 28 lines
scripts/extraction/extract_pymzml.py — Python, 28 lines
scripts/extraction/extract_tims.py — Python, 155 lines
scripts/extraction/timsdata.py — Python, 415 lines
scripts/extraction/tsfdata.py — Python, 189 lines
scripts/maldi_diff.py — Python, 28 lines
scripts/report.py — Python, 203 lines
scripts/train_kmeans.py — Python, 61 lines
scripts/train_nmf.py — Python, 61 lines
scripts/train_segmentation.py — Python, 31 lines
setup.py — Python, 30 lines
LICENSE — License, 21 lines
README.md — Text, 188 lines

At the source: doi.org/10.5281/zenodo.19465293

The paper's code and data availability statement is in the Data section.

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1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. The mouse dataset files are available in PRIDE (https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD056609). We release the code as an open-source package on Zenodo (https://doi.org/10.5281/zenodo.19465293). This study did not generate new materials.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 8 MeSH terms, 4 funders, 43 references.

Cite

This paper

Gildenblat, J., & Pahnke, J. (2026). Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactive m/z mapping and exploration. Science advances, 12(35), eaed3650. https://doi.org/10.1126/sciadv.aed3650

BibTeX

@article{gildenblat2026truthful, author = {Gildenblat, Jacob and Pahnke, Jens}, title = {{Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactive \<i\>m/z\</i\> mapping and exploration}}, journal = {Science advances}, year = {2026}, month = aug, volume = {12}, number = {35}, pages = {eaed3650}, publisher = {American Association for the Advancement of Science}, issn = {2375-2548}, doi = {10.1126/sciadv.aed3650}, url = {https://doi.org/10.1126/sciadv.aed3650}, pmid = {42647616}, pmcid = {PMC13510675} }

RIS

TY - JOUR AU - Gildenblat, Jacob AU - Pahnke, Jens TI - Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactive m/z mapping and exploration T2 - Science advances J2 - Sci Adv PY - 2026 DA - 2026/08/26 VL - 12 IS - 35 SP - eaed3650 SN - 2375-2548 PB - American Association for the Advancement of Science DO - 10.1126/sciadv.aed3650 UR - https://doi.org/10.1126/sciadv.aed3650 LA - en ER -

CSL-JSON

{ "id": "10.1126/sciadv.aed3650", "type": "article-journal", "title": "Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactive m/z mapping and exploration", "container-title": "Science advances", "author": [ { "family": "Gildenblat", "given": "Jacob" }, { "family": "Pahnke", "given": "Jens" } ], "container-title-short": "Sci Adv", "volume": "12", "issue": "35", "page": "eaed3650", "DOI": "10.1126/sciadv.aed3650", "PMID": "42647616", "PMCID": "PMC13510675", "ISSN": "2375-2548", "publisher": "American Association for the Advancement of Science", "URL": "https://doi.org/10.1126/sciadv.aed3650", "language": "en", "issued": { "date-parts": [ [ 2026, 8, 26 ] ] } }

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Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactive <i>m/z</i> mapping and exploration.

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Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactive &lt;i&gt;m/z&lt;/i&gt; mapping and exploration.

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Discussion, reproductions, activity

Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactive <i>m/z</i> mapping and exploration.