An integrative multi-omics approach identifies microbiome alterations linked to pathological and behavioral features in autism spectrum disorder.

Code ↔ Paper

10 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 10 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Results › Microbial abundance demonstrates predictive capacity for brain structural changes in children with ASD ↔ spfigure6_spfigure7_spfigure8/plot_mri.R, lines 61–121 · score 0.76 · S_suborbital_ThickAvg_lh, temporalpole_MeanCurv_rh, networks lh default, pfc, par, linear
[2] § STAR★Methods › Quantification and statistical analysis › Latent factor extraction and analysis by MOFA2 ↔ figure1E-F/mofa_plot.R, the whole file · a weak match · score 0.71 · plot_variance_explained, correlate factors, sMri, covariates, mofa2, metabolite
[3] § Results › Evaluating discriminative power of multi-omics latent factors for distinguishing ASD from TD ↔ figure2/LF_omics_analysis.R, the whole file · a weak match · score 0.59 · networks rh cont, Clostridioides difficile, sarcosine, PWY, correlate, Figure 2
[4] § STAR★Methods › Quantification and statistical analysis › Statistical analysis and visualization ↔ figure3_spfigure2/cor_with_ADOS_CARS.R, lines 97–181 · score 0.57 · forest_model, cor.test, lm, ADOS, Linear
[5] § STAR★Methods › Quantification and statistical analysis › Microbial ecological imbalance analysis ↔ figure5/PM_Analysis_multiprocessing.py, lines 1–27 · score 0.57 · minimum coexist taxa, kernel, PM
[6] § STAR★Methods › Quantification and statistical analysis › Microbial ecological imbalance analysis ↔ figure5/PM_Analysis.py, lines 1–37 · score 0.57 · minimum coexist taxa, kernel, PM
[7] § STAR★Methods › Quantification and statistical analysis › Statistical analysis and visualization ↔ spfigure4/common_taxon_enzyme.R, lines 1–60 · score 0.56 · forest_model, cor.test, lm, enzyme, Linear
[8] § STAR★Methods › Method details › sMRI acquisition and analysis ↔ R/coregister_volume.R, lines 3–49 · score 0.54 · FreeSurfer, tissue, intensity, atlases, parcellation, Raw
[9] § STAR★Methods › Quantification and statistical analysis › MOFA2 latent factor clustering ↔ spfigure1/cluster_crm.ipynb, lines 82–105 · score 0.54 · UMAP dimensionality, clusters
[10] § Results › Evaluating discriminative power of multi-omics latent factors for distinguishing ASD from TD ↔ figure2/LF_omics_analysis.R, the whole file · a weak match · score 0.50 · Clostridioides difficile, sarcosine, folding, PWY, Pearson, correlation

Paper

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The authors' code

R · 90 lines · 3.2 KB · CC-BY-4.0 · 2 matches

df<- read.table("mofafactor_most_cor_features.tsv", header = TRUE, sep = "\t" )
corr_coef <-cor.test(df$Factor2, df$Sarcosine, method = "pearson")$estimate
corr_p <- cor.test(df$Factor2, df$Sarcosine, method = "pearson")$p.value
p3<-ggplot(df, aes(x = Factor2, y = Sarcosine, color = group)) +
geom_point(size =2, alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE, color = "darkgreen") +
labs(
title = "",
x = "Factor2",
y = "Sarcosine"
) +
theme_classic() +
annotate(
"text",
x = 0.2, y = 0.9,
label = paste("Cor: ", round(corr_coef, 2), ", p-value: ", signif(corr_p, 2)),
size = 4
)
ggsave(p3, filename='Sarcosine_factor2.pdf', width=12, height=10, units=c("cm"))
corr_coef <-cor.test(df$Factor2, df$X7Networks_RH_Cont_PFCv_1_FoldInd_rh.Schaefer2018_400Parcels_7Networks_order, method = "pearson")$estimate
corr_p <- cor.test(df$Factor2, df$X7Networks_RH_Cont_PFCv_1_FoldInd_rh.Schaefer2018_400Parcels_7Networks_order, method = "pearson")$p.value
p3<-ggplot(df, aes(x = Factor2, y = X7Networks_RH_Cont_PFCv_1_FoldInd_rh.Schaefer2018_400Parcels_7Networks_order, color = group)) +
geom_point(size =2, alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE, color = "darkgreen") +
labs(
title = "",
x = "Factor2",
y = "X7Networks_RH_Cont_PFCv_1_FoldInd_rh.Schaefer2018_400Parcels_7Networks_order"
) +
theme_classic() +
annotate(
"text",
x = 0.2, y = 0.9,
label = paste("Cor: ", round(corr_coef, 2), ", p-value: ", signif(corr_p, 2)),
size = 4
)
ggsave(p3, filename='X7Networks_RH_Cont_PFCv_1_FoldInd_rh.Schaefer2018_400Parcels_7Networks_order_factor2.pdf', width=12, height=10, units=c("cm"))
df<- read.table("mofafactor_most_cor_features.tsv", header = TRUE, sep = "\t" )
corr_coef <-cor.test(df$Factor2, df$PWY.5199, method = "pearson")$estimate
corr_p <- cor.test(df$Factor2, df$PWY.5199, method = "pearson")$p.value
p3<-ggplot(df, aes(x = Factor2, y = PWY.5199, color = group)) +
geom_point(size =2, alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE, color = "darkgreen") +
labs(
title = "",
x = "Factor2",
y = "PWY.5199"
) +
theme_classic() +
annotate(
"text",
x = 0.2, y = 0.9,
label = paste("Cor: ", round(corr_coef, 2), ", p-value: ", signif(corr_p, 2)),
size = 4
)
ggsave(p3, filename='PWY.5199_factor2.pdf', width=12, height=10, units=c("cm"))
df<- read.table("mofafactor_most_cor_features.tsv", header = TRUE, sep = "\t" )
corr_coef <-cor.test(df$Factor2, df$X1496, method = "pearson")$estimate
corr_p <- cor.test(df$Factor2, df$X1496, method = "pearson")$p.value
p3<-ggplot(df, aes(x = Factor2, y = X1496, color = group)) +
geom_point(size =2, alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE, color = "darkgreen") +
labs(
title = "",
x = "Factor2",
y = "Clostridioides difficile"
) +
theme_classic() +
annotate(
"text",
x = 0.2, y = 0.9,
label = paste("Cor: ", round(corr_coef, 2), ", p-value: ", signif(corr_p, 2)),
size = 4
)
ggsave(p3, filename='Clostridioides difficile_factor2.pdf', width=12, height=10, units=c("cm"))

LF_omics_analysis.R, under CC-BY-4.0 · at the source

Overview

Authors: Kai Mi¹, Miao Cao^2,3, Lingli Zhang³, Qianlong Zhang³, Wei Zhou³, Chijun Deng², Yue Zhang², Qing Zhao¹, Yusen Wei¹, Xingyin Liu¹, Fei Li³

ORCID iDs: Kai Mi, Xingyin Liu, Fei Li

Institutions: Shanghai Jiao Tong University (China); Fudan University (China); XinHua Hospital (China); Southern University of Science and Technology (China); ShenZhen People’s Hospital (China)

Journal: Cell reports. Medicine, volume 7, issue 3, article 102655

Dates: received 3 October 2025; accepted 6 February 2026; published online 9 March 2026; in print March 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1016/j.xcrm.2026.102655 · PMID 41806837 · PMCID PMC13006428 · OpenAlex W7134238110

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), human (organism), autism (population)

Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging, Preprocessing

Keywords: autism spectrum disorder, multi-omics, neuroimaging, metabolome, gut microbiota

MeSH: Autism Spectrum Disorder*, Gastrointestinal Microbiome*, Brain, Child, Child, Preschool, Female, Humans, Infant, Infant, Newborn, Male, Metabolome, Multiomics (* major topic)

Topic: Gut microbiota and health (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: School of Medicine, Shanghai Jiao Tong University (23XHCR16C, XKPF2024A5001); Science and Technology Commission of Shanghai Municipality (23Y21900500, 23DZ2291100, 2018SHZDZX01); Shanghai Municipal Health Commission (GWVI-11.1-34, GWVI-11.2-YQ30, 2018YJRC03, 2020CXJQ01); National Natural Science Foundation of China (32570064, 82430104, 82172288, 82125032); Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20211100); National Key Research and Development Program of China; Key Technologies Research and Development Program (2022YFA1303900)

Citations: cited by 3 papers (Europe PMC); 118 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

figshare 31143223

License: CC-BY-4.0

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data and code availability”

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

Tools: tidyverse (8 files), ggplot2 (7 files), NumPy (4 files), data.table (3 files), pandas (3 files), scikit-learn (3 files), ggpubr (2 files), SciPy (2 files), Matplotlib (1 file), patchwork (1 file), pROC (1 file), reticulate (1 file), seaborn (1 file), UMAP (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)

30 September 2026: the link answers (HTTP 200)

15 files

figure1A-D/plot.R, R, 95 lines
figure1E-F/mofa_plot.R, R, 88 lines, 1 match
figure1H/linear_reg_onLF_roc.R, R, 29 lines
figure2/LF_omics_analysis.R, R, 90 lines, 2 matches
figure3_spfigure2/cor_with_ADOS_CARS.R, R, 206 lines, 1 match
figure4/taxon_brain_altas.R, R, 295 lines
figure5/PM_Analysis.py, Python, 361 lines, 1 match
figure5/PM_Analysis_multiprocessing.py, Python, 413 lines, 1 match
figure5/fdrcorrection.py, Python, 90 lines
figure5/load_save_project.py, Python, 15 lines
spfigure1/cluster_crm.ipynb, Jupyter, 106 lines, 1 match
spfigure1/subGroup_analysis.R, R, 245 lines
spfigure3/common_taxon.R, R, 232 lines
spfigure4/common_taxon_enzyme.R, R, 421 lines, 1 match
spfigure6_spfigure7_spfigure8/plot_mri.R, R, 184 lines, 1 match

At the source: doi.org/10.6084/m9.figshare.31143223

ggseg/ggsegExtra

License: other

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 82d5c334216fbf15adb9e789e4908c56f784ec9a, 29 September 2026

Languages: R (133), Quarto (11)

Size: 341 files, 144 scripts

Software Heritage: not checked

Found in: the resources table

Holds: README, license file, environment (DESCRIPTION, .github/docker/freesurfer-slim/Dockerfile, inst/templates/atlas-fallback/DESCRIPTION), tests, continuous integration, documentation, 18 notebooks

Not found: CITATION.cff

Tools: tidyverse (21 files), RNifti (20 files), ggseg (12 files), ggplot2 (5 files), FreeSurfer (3 files)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

147 files

.github/scripts/run-freesurfer-tests.R, R, 7 lines
R/arg_groups.R, R, 88 lines
R/atlas_cerebellar.R, R, 1,936 lines
R/atlas_convert.R, R, 5 lines
R/atlas_coords.R, R, 246 lines
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R/atlas_geometry.R, R, 1,309 lines
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R/atlas_subcortical.R, R, 747 lines
R/atlas_tracts.R, R, 683 lines
R/atlas_verbs.R, R, 121 lines
R/atlas_wholebrain.R, R, 2,700 lines
R/cache_version.R, R, 358 lines
R/cerebellar_projection.R, R, 387 lines
R/cifti_subcortical.R, R, 132 lines
R/classify_anatomy.R, R, 636 lines
R/coregister_volume.R, R, 1,058 lines, 1 match
R/cortical_steps.R, R, 74 lines
R/deprecate_post_creation.R, R, 72 lines
R/freesurfer.R, R, 684 lines
R/ggseg.extra_package.R, R, 31 lines
R/github_actions.R, R, 132 lines
R/lut_colors.R, R, 179 lines
R/mesh_projection.R, R, 413 lines
R/prepare_subcortical_mni152.R, R, 219 lines
R/read_write.R, R, 1,342 lines
R/sitrep.R, R, 533 lines
R/snapshots.R, R, 280 lines
R/subcortical_builders.R, R, 404 lines
R/subcortical_geometry.R, R, 390 lines
R/subcortical_steps.R, R, 675 lines
R/tract_from_volume.R, R, 215 lines
R/tract_geometry.R, R, 873 lines
R/tract_io.R, R, 202 lines
R/tract_steps.R, R, 369 lines
R/utils.R, R, 644 lines
R/utils_atlas.R, R, 530 lines
R/utils_snapshot.R, R, 492 lines
README.Rmd, R, 80 lines
data-raw/build_tutorials.R, R, 173 lines
data-raw/test_create_cortical.R, R, 53 lines
data-raw/test_create_from_labels.R, R, 53 lines
data-raw/test_create_neuromaps.R, R, 47 lines
data-raw/test_create_subcortical.R, R, 78 lines
data-raw/test_create_tracts.R, R, 119 lines
data-raw/test_plot_neuromaps.R, R, 18 lines
dev/audit-tutorial-api.R, R, 245 lines
dev/check-like-ci.R, R, 134 lines
inst/templates/atlas-fallback/R/PKGNAME-package.R, R, 3 lines
inst/templates/atlas-fallback/R/data.R, R, 17 lines
inst/templates/atlas-fallback/README.qmd, Quarto, 77 lines
inst/templates/atlas-fallback/data-raw/create-atlas.R, R, 223 lines
inst/templates/atlas-fallback/tests/testthat.R, R, 4 lines
inst/templates/atlas-fallback/tests/testthat/test-data.R, R, 22 lines
man-roxygen/annot_dir.R, R, 1 line
man-roxygen/atlas_name.R, R, 2 lines
man-roxygen/cleanup.R, R, 3 lines
man-roxygen/colours.R, R, 2 lines
man-roxygen/decimate.R, R, 4 lines
man-roxygen/dilate.R, R, 2 lines
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man-roxygen/hemisphere.R, R, 1 line
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man-roxygen/subjects_dir.R, R, 1 line
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man-roxygen/verbose.R, R, 5 lines
man-roxygen/vertex_size_limits.R, R, 3 lines
man-roxygen/views_deprecated.R, R, 1 line
tests/spelling.R, R, 7 lines
tests/testthat.R, R, 12 lines
tests/testthat/helper-cifti.R, R, 41 lines
tests/testthat/helper-fs.R, R, 42 lines
tests/testthat/helper-pipeline.R, R, 104 lines
tests/testthat/helper-snapshot.R, R, 6 lines
tests/testthat/helper.R, R, 49 lines
tests/testthat/setup.R, R, 18 lines
tests/testthat/test-arg_groups.R, R, 102 lines
tests/testthat/test-atlas_cerebellar.R, R, 2,844 lines
tests/testthat/test-atlas_convert.R, R, 8 lines
tests/testthat/test-atlas_coords.R, R, 453 lines
tests/testthat/test-atlas_cortical.R, R, 1,275 lines
tests/testthat/test-atlas_dilate.R, R, 83 lines
tests/testthat/test-atlas_geometry.R, R, 1,817 lines
tests/testthat/test-atlas_repo.R, R, 786 lines
tests/testthat/test-atlas_subcortical.R, R, 1,150 lines
tests/testthat/test-atlas_tracts.R, R, 732 lines
tests/testthat/test-atlas_wholebrain.R, R, 4,302 lines
tests/testthat/test-cache_version.R, R, 278 lines
tests/testthat/test-cerebellar_projection.R, R, 177 lines
tests/testthat/test-cifti_subcortical.R, R, 133 lines
tests/testthat/test-classify_anatomy.R, R, 545 lines
tests/testthat/test-coregister_volume.R, R, 1,211 lines
tests/testthat/test-cortical_steps.R, R, 155 lines
tests/testthat/test-deprecate_post_creation.R, R, 56 lines
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tests/testthat/test-freesurfer.R, R, 789 lines
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tests/testthat/test-ggseg.extra_package.R, R, 20 lines
tests/testthat/test-gifti_cifti.R, R, 778 lines
tests/testthat/test-github_actions.R, R, 117 lines
tests/testthat/test-integration.R, R, 116 lines
tests/testthat/test-lut_colors.R, R, 199 lines
tests/testthat/test-mesh_projection.R, R, 481 lines
tests/testthat/test-neuromaps.R, R, 468 lines
tests/testthat/test-prepare_subcortical_mni152.R, R, 112 lines
tests/testthat/test-read_write.R, R, 1,197 lines
tests/testthat/test-sitrep.R, R, 309 lines
tests/testthat/test-snapshots.R, R, 313 lines
tests/testthat/test-subcortical_builders.R, R, 541 lines
tests/testthat/test-subcortical_geometry.R, R, 530 lines
tests/testthat/test-subcortical_steps.R, R, 1,636 lines
tests/testthat/test-tract_from_volume.R, R, 139 lines
tests/testthat/test-tract_geometry.R, R, 1,072 lines
tests/testthat/test-tract_io.R, R, 299 lines
tests/testthat/test-tract_steps.R, R, 1,290 lines
tests/testthat/test-utils.R, R, 771 lines
tests/testthat/test-utils_atlas.R, R, 498 lines
tests/testthat/test-utils_snapshot.R, R, 677 lines
vignettes/articles/tutorial-cerebellar-atlas.qmd, Quarto, 276 lines
vignettes/articles/tutorial-cortical-atlas.qmd, Quarto, 341 lines
vignettes/articles/tutorial-label-atlas.qmd, Quarto, 255 lines
vignettes/articles/tutorial-lookup-tables.qmd, Quarto, 247 lines
vignettes/articles/tutorial-neuromaps-atlas.qmd, Quarto, 446 lines
vignettes/articles/tutorial-publishing-an-atlas.qmd, Quarto, 344 lines
vignettes/articles/tutorial-subcortical-atlas.qmd, Quarto, 377 lines
vignettes/articles/tutorial-tract-atlas.qmd, Quarto, 377 lines
vignettes/articles/tutorial-wholebrain-atlas.qmd, Quarto, 376 lines
vignettes/atlas-workflows.qmd, Quarto, 219 lines
vignettes/contributing.Rmd, R, 265 lines
vignettes/ggseg.extra.Rmd, R, 106 lines
vignettes/legacy-conversion.Rmd, R, 214 lines
vignettes/pipeline-configuration.Rmd, R, 192 lines
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LICENSE, License, 2 lines
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README.md, Text, 85 lines

At the source: github.com/ggseg/ggsegExtra

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
159 scripts, each with its path and the digest of its content;
10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to the authors' code: figshare 31143223
it says that the data are available on request

Read it in the paper: doi.org/10.1016/j.xcrm.2026.102655.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 keywords, 12 MeSH terms, 7 funders, 110 references.

Cite

This paper

Mi, K., Cao, M., Zhang, L., Zhang, Q., Zhou, W., Deng, C., Zhang, Y., Zhao, Q., Wei, Y., Liu, X., & Li, F. (2026). An integrative multi-omics approach identifies microbiome alterations linked to pathological and behavioral features in autism spectrum disorder. Cell reports. Medicine, 7(3), 102655. https://doi.org/10.1016/j.xcrm.2026.102655

BibTeX

@article{mi2026integrative, author = {Mi, Kai and Cao, Miao and Zhang, Lingli and Zhang, Qianlong and Zhou, Wei and Deng, Chijun and Zhang, Yue and Zhao, Qing and Wei, Yusen and Liu, Xingyin and Li, Fei}, title = {{An integrative multi-omics approach identifies microbiome alterations linked to pathological and behavioral features in autism spectrum disorder}}, journal = {Cell reports. Medicine}, year = {2026}, month = mar, volume = {7}, number = {3}, pages = {102655}, publisher = {Elsevier}, issn = {2666-3791}, doi = {10.1016/j.xcrm.2026.102655}, url = {https://doi.org/10.1016/j.xcrm.2026.102655}, pmid = {41806837}, pmcid = {PMC13006428} }

RIS

TY - JOUR AU - Mi, Kai AU - Cao, Miao AU - Zhang, Lingli AU - Zhang, Qianlong AU - Zhou, Wei AU - Deng, Chijun AU - Zhang, Yue AU - Zhao, Qing AU - Wei, Yusen AU - Liu, Xingyin AU - Li, Fei TI - An integrative multi-omics approach identifies microbiome alterations linked to pathological and behavioral features in autism spectrum disorder T2 - Cell reports. Medicine J2 - Cell Rep Med PY - 2026 DA - 2026/03/09 VL - 7 IS - 3 SP - 102655 SN - 2666-3791 PB - Elsevier DO - 10.1016/j.xcrm.2026.102655 UR - https://doi.org/10.1016/j.xcrm.2026.102655 LA - en ER -

CSL-JSON

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Journal: Nature communications
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Journal: Nature
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