Convergent transcriptomic and neuroimaging signature of Autism Spectrum Disorder

Alessandra Retico (INFN); Alessandro Cellerino (Scuola Normale Superiore); Davide Bacciu (University of Pisa); Elisa Ferrari (Scuola Normale Superiore)

REVIEW 5 major objections 5 minor 300 references

Convergent transcriptomic and neuroimaging signature of Autism Spectrum Disorder

T0 review · 5 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This thesis claims that the brain regions driving an fMRI-based ASD classifier are the same regions where four of five ASD-associated gene modules are most strongly expressed.

desk verdict Readable, honest thesis front-matter with a potentially important cross-modal claim; the actual evidence is not in the files I was given, so it is unverdictable rather than wrong. read the letter →

arxiv 2505.23776 v1 pith:HXY6B2Z3 submitted 2025-05-15 q-bio.NC

Elisa Ferrari (Scuola Normale Superiore) , Alessandra Retico (INFN) , Alessandro Cellerino (Scuola Normale Superiore) , Davide Bacciu (University of Pisa) This is my paper · ORCID

classification q-bio.NC

keywords autismspectrumdisorderresting-statefMRIimagingtranscriptomicsgeneco-expressionnetworkdeeplearningadversarialdebiasingmoduleeigengenebiomarker

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Across the heterogeneity of autism, this thesis tries to establish one shared, measurable signature: the brain regions that let a machine-learning classifier separate ASD from typical controls using resting-state fMRI are the same regions where four of the five ASD-associated gene-expression modules have significantly high expression. To get there, the thesis builds a confounder-robust deep classifier reaching an AUC of 0.89 on an independent test set, identifies five gene co-expression modules from post-mortem ASD and control transcriptomes, and then projects module eigengenes onto the same brain space as the classifier's saliency map. The reason this matters is that a non-invasive imaging biomarker validated at the transcriptomic level would be usable for early diagnosis, while transcriptomic data alone can only be obtained post-mortem.

What carries the argument

The argument is carried by three coupled objects. First, a two-stream CNN treats a 4D resting-state fMRI and its optical flow as separate input streams, so that spatial and temporal-motion information are learned separately. Second, a Bias-Resilient Neural Network, an adversarial tripartite architecture of feature extractor, predictor, and confounder predictor, forces the learned features to be uninformative about specified confounders such as sex, age, and acquisition site. Third, SmoothGrad relevance maps average input-gradient explanations over noisy perturbations to give a fuzzy region of interest per subject. On the transcriptomic side, the load-bearing objects are weighted gene co-expression network modules and their first principal component, the module eigengene. The thesis computes eigengene values for each module across brain regions using Allen Human Brain Atlas expression data mapped to the AAL atlas, then tests whether the classifier-relevant regions show significantly high absolute eigengene expression. SmoothGrad provides the imaging pattern, the eigengene provides the transcriptomic pattern, and the spatial comparison is what establishes convergence.

What would settle it

Compute SmoothGrad saliency maps from the same rsfMRI data using classifiers trained on non-ASD labels such as sex or acquisition site; if those maps overlap the four gene modules as strongly as the ASD map does, the overlap is not ASD-specific. Alternatively, recompute module eigengenes in an independent post-mortem ASD dataset with multiple regions and check that the same four modules still show high absolute expression in the classifier-relevant regions.

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Extended reading notes

Core claim

The central claim, stated in the abstract, is that the regions characterizing the ASD brain at the neuroimaging level are precisely those in which four of the five gene modules take a significantly high absolute expression value. The neuroimaging component is a two-stream convolutional network that processes resting-state fMRI and its optical flow, made resilient to confounders by an adversarial bias-resilient network, reaching an AUC of 0.89 on an independent test set; the transcriptomic component is a weighted gene co-expression network analysis on post-mortem ASD and control brains yielding five ASD-associated modules; the imaging-genetics component compares the classifier's SmoothGrad relevance regions with Allen Human Brain Atlas module eigengene expression in AAL regions. The convergence of the two independent modalities is taken as mutual validation, showing that a neuroimaging biomarker of ASD can be identified and confirmed by transcriptomics despite the disorder's heterogeneity.

Load-bearing premise

The convergence result rests on the premise that gene-expression patterns measured in control brains are a valid map of where ASD pathology occurs, so the spatial overlap between classifier relevance and module expression counts as biological convergence rather than mere geography.

Editorial extensions

If this is right

If correct, the rsfMRI classifier's saliency map is not an arbitrary artifact of deep learning but a spatial readout of ASD-related transcriptional activity, giving the imaging biomarker a molecular grounding.
Four modules, not just one, converge on the same regions, suggesting that even a heterogeneous disorder shares a partly common regional pathology.
The bias-resilient design implies that the reported AUC of 0.89 is not driven by sex, age, or acquisition-site confounders, so the pattern should transfer to new multi-site datasets.
The method provides a template for validating deep-learning neuroimaging biomarkers in other disorders by comparing saliency maps with disease-gene expression atlases.
Clinically, such a convergent signature could eventually support earlier, objective diagnosis when behavioural assessment is inconclusive.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because the AHBA expression data come from control brains, the convergence likely maps regions of intrinsic vulnerability rather than active pathology; a natural follow-up is to test whether the same regions show elevated dysregulation in ASD post-mortem samples at single-cell resolution.
The paper's use of absolute expression leaves open whether up- and down-regulated modules mark the same or different regions; separating directions could sharpen the imaging-transcriptomic match.
A testable extension is to apply the same spatial-overlap pipeline to sex, age, or symptom-severity labels, predicting that only the ASD classifier yields a significant match.
The pipeline is portable to other polygenic conditions with both fMRI cohorts and brain transcriptome atlases, so the convergence claim can be probed outside ASD.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Bottom line: based on what is actually in the file—abstract, front matter, background chapters, and the table of contents—I cannot verify the central claims. If the full thesis delivers what the abstract promises, it is a significant result. Based only on what I have, this is an unverdictable thesis, not a paper I can endorse or reject.

What is genuinely new is the chain: a confounder-resilient rsfMRI classifier with an AUC of 0.89 on an independent test set, WGCNA modules from post-mortem ASD transcriptomics, and a spatial overlap between classifier saliency and AHBA-derived module expression. That specific convergence claim is not in the prior literature described in the background chapters. The background prose is also careful and honest. The discussion of MRI being non-quantitative, the need for validation on truly unseen data, and the explicit note in Sec. 3.4.2 that the BR-NN's dual-loss design was never experimentally tested all suggest a serious thinker who is not overstating what was done. That honesty counts, though it cuts against the imaging half of the convergence claim.

The soft spots are large, but they are soft because evidence is missing, not because the argument is internally incoherent. The abstract uses the word "proved" for a spatial overlap, yet no test statistic, multiple-comparison correction, or spatial null appears in the reviewed portion. The stress-test concern about using AHBA control-brain expression as a spatial proxy for ASD pathology is legitimate but not confirmed; the relevant methods and results chapters are absent. The use of absolute eigengene expression is genuinely ambiguous: if a module is down-regulated in ASD, regions with low expression may be the relevant ones. And both saliency maps and expression maps are spatially autocorrelated, so a naive overlap test can look significant by chance. The AUC of 0.89 also needs site-level split details and leakage checks, especially since the background itself documents that large-dataset ASD classifiers usually plateau around 70%.

My take: this deserves a careful referee if and only if the full methods and results are supplied. The question is important, the thesis looks serious, and the novelty is real. I would ask for the complete chapters, and specifically for a spatial permutation null, a sign-aware treatment of module expression, and independent replication of the overlap. Until then, the right verdict is "not yet verifiable," not accept or reject.

Referee Report

5 major / 5 minor

Summary. This manuscript is a PhD thesis posted on arXiv that claims three results: (1) a bias-resilient deep learning classifier for ASD using resting-state fMRI, with an AUC of 0.89 on an independent test set; (2) five ASD-associated gene co-expression modules identified from post-mortem transcriptomics; and (3) an imaging-transcriptomics comparison that, in the abstract's words, "proved" that the brain regions relevant to the classifier are those where four of the five modules take a significantly high absolute expression value. The text available for review contains the abstract, Introduction, and background chapters on ASD, neuroimaging methods, genetics, and imaging genetics, but the Materials and Methods, Results, and Discussion chapters announced in the table of contents are not present in the provided text. The three central claims are therefore stated but their supporting statistical and experimental details are absent.

Significance. If the results hold, the claimed cross-modal convergence would be of genuine interest: it would provide a spatial link between an fMRI-derived biomarker and ASD-associated gene expression, with potential implications for early diagnosis and for interpreting neuroimaging signals in terms of molecular pathology. The study design has notable strengths: the imaging and transcriptomic analyses rely on largely independent data sources, the manuscript demonstrates awareness of confounder problems in large multi-site MRI datasets, and the literature review is thorough. However, because the actual methods and results are not present in the reviewed text, the significance cannot currently be assessed beyond the plausibility of the design. The claimed convergence, if substantiated, would be a valuable contribution, but it is not yet a verifiable contribution.

major comments (5)

[Abstract, third bullet; TOC Parts III–V] The posted text contains the abstract and background chapters only; the Materials and Methods, Results, and Discussion chapters that would support the three central claims are absent. The abstract reports an AUC of 0.89, five gene modules, and a 'significantly high absolute value of expression' overlap, but none of the underlying performance numbers, module definitions, test statistics, thresholds, or significance corrections are available in the reviewed text. This is a load-bearing omission: the central claims cannot be checked, replicated, or fairly criticized at the statistical level. A revision must supply the complete methodological and results sections, or the claims must be withdrawn.
[Section 3.4.2] The text explicitly states that the adversarial correction design of the BR-NN, based on the combined LC1/LC2 losses, was not experimentally tested: 'Despite no experimental tests have been made to assess the efficacy of this choice'. This is an author-acknowledged absence of support for the very component that the classifier's confounder resilience depends on. Since the saliency map used in the imaging-transcriptomics comparison is generated by this classifier, an ineffective adversarial correction would directly undermine the biological interpretation of the overlap. This point needs either empirical validation of the adversarial scheme or a clear statement of what the imaging results would mean if the scheme is only partially effective.
[Chapter 9 / Chapter 12] The convergence claim rests on transferring ASD-associated module expression from post-mortem ASD/TDC transcriptomics to the spatial expression distribution of Allen Human Brain Atlas data, which is derived from control brains. The reviewed text does not define the test statistic used for 'significantly high absolute value of expression', nor the threshold for calling a region 'relevant', nor a spatial null model that accounts for the strong spatial autocorrelation of both neuroimaging saliency maps and gene-expression maps. Without such a spatial null and an independent replication, the reported overlap can be expected to be significant by chance and cannot be interpreted as biological convergence.
[Chapter 9 / Eq. (3.5) and abstract] The use of the absolute value of the module eigengene is ambiguous and biologically unmotivated as described. If a module is down-regulated in ASD, the relevant brain regions might show low, not high, expression in control-brain data, so the sign of the eigengene carries information that the absolute value discards. The abstract's claim of 'significantly high absolute value of expression' therefore requires a clear baseline and a biological rationale for why high absolute expression, rather than direction-specific dysregulation, is the relevant quantity. This issue is central to the third claim and is not addressed in the provided text.
[Abstract] The abstract uses 'proved' twice to describe the spatial overlap result. Given that the analysis involves multiple free thresholds (saliency threshold, overlap significance threshold, network cut height, soft-threshold power, adversarial weighting) and no causal link is demonstrated, 'proved' overstates the evidential quality of a spatial association. This is a substantive concern because the proof claim is the headline of the paper; the wording should be revised to 'consistent with' or 'suggests' unless a formal, validated statistical proof is supplied.

minor comments (5)

[Abstract] The phrase 'Effective therapies to reduce all the heterogeneous symptoms of the disorder do not exists yet' contains a grammar error: it should be 'do not exist yet'.
[Table of Contents] 'Trascriptomics' should be spelled 'Transcriptomics' in the chapter title listing.
[Section 3.3.2] The phrase 'the few higher-performing results are never replied in subsequent studies' should likely read 'never replicated' rather than 'replied'.
[Section 3.4.2] There is a typo in the sentence 'minimizes th ability of C to predict the confounders'; 'th' should be 'the'.
[Section 2.3] 'In addiction' is used where 'In addition' is intended.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the neuroimaging and transcriptomic analyses are independent, and the cross-modal comparison is a post-hoc spatial overlap test rather than a construction.

full rationale

The central derivation chain is self-contained. The classifier is trained on ABIDE rsfMRI data (Sec. 7) and its relevance map is obtained with Smoothgrad; the gene modules are derived from post-mortem ASD vs TDC transcriptomes by LMM differential expression and WGCNA (Sec. 8), without any use of the fMRI saliency map. The imaging-transcriptomics step (Sec. 9) then projects the AHBA spatial expression of those modules and tests whether classifier-relevant AAL regions show significantly high absolute module eigengenes. Neither side is fitted to the other: the modules are not selected to match the neuroimaging regions, and the classifier does not use expression data. The only author self-citations (e.g., the Confounding Index in Sec. 3.4.2) support the confounder-resilience analysis but are not load-bearing for the cross-modal convergence claim. The admitted absence of experimental tests of the BR-NN adversarial loss design is a limitation of the imaging analysis, not a circularity in the derivation. Accordingly, no reduction of the prediction to its inputs by construction is identifiable.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

No genuinely new theoretical entities are introduced; the free parameters are the standard hyperparameters of the three pipelines, and the load-bearing assumption is the transferability of AHBA expression maps to ASD brains.

free parameters (6)

optical flow regularization lambda = not reported in reviewed text
Eq. 3.5 balances the brightness-constancy term against the velocity-gradient penalty; value chosen by the authors but not given in the abstract/background.
adversarial correction strength lambda (BR-NN) = not reported
Eq. 3.8 weights the inverse correlation loss that suppresses confounder information; the thesis states no experimental test was made for the mixed loss design in Sec. 3.4.2.
WGCNA soft-threshold power (beta) = not reported
Chosen via scale-free topology fit (R-squared above 0.8 vs mean connectivity) in the transcriptomics analysis; exact value not in the abstract/background.
hierarchical clustering cut height = not reported
Determines module membership in the co-expression network; the thesis notes this is an arbitrary choice with no hard criterion in Sec. 4.4.2.
saliency threshold for relevant brain regions = not reported
SmoothGrad maps are converted to a region list for comparison with gene expression; threshold not available in reviewed text.
spatial overlap significance threshold = not reported
The claim of significantly high absolute expression requires a statistical cutoff, such as a permutation p-value; this is unreported in the abstract.

assumptions (5)

domain assumption Optical flow brightness constancy, Eq. 3.1
Assumes image intensity at a fixed brain point is constant over small displacements; the thesis acknowledges this can fail and claims rsfMRI settings make it almost impossible to fail in Sec. 3.4.1.
domain assumption AHBA expression in control brains is a spatial proxy for ASD pathology
The imaging-transcriptomics comparison projects ASD module eigengenes onto the spatial map of a small number of neurotypical donors; if ASD brains have different regional expression patterns, the spatial overlap is not interpretable. This enters in the imaging genetics part described in Ch. 5.
ad hoc to paper Scale-free topology criterion selects the meaningful WGCNA adjacency function
The thesis follows the common WGCNA heuristic of maximizing SFT R-squared while keeping non-zero mean connectivity in Sec. 4.4.2; there is no biological guarantee this choice captures ASD-relevant structure.
standard math Normality and linearity assumptions of LMM for gene expression
LMM DE analysis assumes Gaussian errors and linear effects after log2 transformation; the thesis defends this as conservative in Sec. 4.4.1 and Box 3.
domain assumption Independence of the three analyses
The convergence argument assumes the neuroimaging and transcriptomics pipelines share no subjects, artifacts, or fitting steps; if the same dataset underlies both, the cross-validation would be weaker.

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Cite this review

Pith. "Pith review of Convergent transcriptomic and neuroimaging signature of Autism Spectrum Disorder." pith.science (2026). https://pith.science/paper/HXY6B2Z3

@misc{pith2026250523776,
  author       = {Pith},
  title        = {Pith review of: Convergent transcriptomic and neuroimaging signature of Autism Spectrum Disorder},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HXY6B2Z3}},
  note         = {Machine review of arXiv:2505.23776}
}

read the original abstract

Autism Spectrum Disorder (ASD) is a multi-factorial neurodevelopmental disorder, whose causes are still poorly understood. Effective therapies to reduce all the heterogeneous symptoms of the disorder do not exists yet, but behavioural programs started at a very young age may improve the quality of life of the patients. For this reason, many efforts have been dedicated to the research of a reliable biomarker for early diagnosis. Machine learning approaches to distinguish ASDs from healthy controls based on their brain Magnetic Resonance Images (MRIs) have been plagued by the problem of confounders, showing poor classification performance and inconsistency in the biomarker definition. Brain transcriptomics studies, instead, showed some converging results, but being based on data that can be acquired only post-mortem they are not useful for diagnosis. In this work, using an imaging transcriptomics approach, the following results have been obtained. 1) A deep learning based classifier resilient to confounders and able to exploit the temporal dimension of resting state functional MRIs has been developed, reaching an AUC of 0.89 on an independent test set. 2) Five gene network modules involved in ASD have been identified, by analyzing brain transcriptomics data of subjects with ASD and healthy controls. 3) By comparing the brain regions relevant for the classifier obtained in the first step and the brain-wide gene expression profiles of the modules of interest obtained in the second step, it has been proved that the regions that characterize ASD brain at the neuroimaging level are those in which four out of the five gene modules take a significantly high absolute value of expression. These results prove that, despite the heterogeneity of the disorder, it is possible to identify a neuroimaging-based biomarker of ASD, confirmed by transcriptomics.

Figures

Figures reproduced from arXiv: 2505.23776 by the authors.

**Figure 2.1.** Multi-factorial threshold model applied to ASD. According to this model, ASD is [PITH_FULL_IMAGE:figures/full_fig_p027_2_1.png] view at source ↗

**Figure 3.1.** Steps of MRI signal generation. Step 1 illustrates the randomly oriented magnetic [PITH_FULL_IMAGE:figures/full_fig_p033_3_1.png] view at source ↗

**Figure 3.2.** Visualization of the temporal evolution of the BOLD signal. [PITH_FULL_IMAGE:figures/full_fig_p033_3_2.png] view at source ↗

Figures from the paper (50 more)

**Figure 3.3.** Figure 3.3: Representation of a convolutional layer. Parts of the input, usually an image, are [PITH_FULL_IMAGE:figures/full_fig_p039_3_3.png]

**Figure 3.4.** Figure 3.4: Structure of an RNN. An RNN is based on the concept of memory. Each layer retains [PITH_FULL_IMAGE:figures/full_fig_p041_3_4.png]

**Figure 3.5.** Figure 3.5: Schematic of the functioning of an auto-encoder. The encoder block compresses the [PITH_FULL_IMAGE:figures/full_fig_p042_3_5.png]

**Figure 3.6.** Figure 3.6: Trying to guess the movement of the blue bar from its configuration at t=0 to the one [PITH_FULL_IMAGE:figures/full_fig_p048_3_6.png]

**Figure 3.7.** Figure 3.7: Superposition of a traffic scene and its optical flow (in module). Pixels of the optical [PITH_FULL_IMAGE:figures/full_fig_p048_3_7.png]

**Figure 3.8.** Figure 3.8: Schematic of the two-stream CNN architecture. The optical flow and the unprocessed [PITH_FULL_IMAGE:figures/full_fig_p049_3_8.png]

**Figure 3.9.** Figure 3.9: Representation of the case in which a confounder variable affects the input data in a [PITH_FULL_IMAGE:figures/full_fig_p050_3_9.png]

**Figure 3.10.** Figure 3.10: Steps of the calculation of the CI. If the tested variable is not a confounder, the performances obtained should not depend on the dataset composition with respect to it, so all the AUCs should be equivalent within a certain uncertainty. On the contrary, if the test…

**Figure 3.11.** Figure 3.11: Architecture of the BR-NN7 . 3.4.3 Explainability with Smoothgrad A wide number of model-agnostic explainability algorithms have been developed to unbox the decisional pattern learned by any DL algorithm. They are usually categorized in local and global Explainable …

**Figure 3.12.** Figure 3.12: Plot of different kinds of relationship between [PITH_FULL_IMAGE:figures/full_fig_p055_3_12.png]

**Figure 3.13.** Figure 3.13: Comparison of the local explanations provided by different XAI algorithms on a [PITH_FULL_IMAGE:figures/full_fig_p056_3_13.png]

**Figure 4.1.** Figure 4.1: Steps linking the DNA information to the phenotype [PITH_FULL_IMAGE:figures/full_fig_p057_4_1.png]

**Figure 4.2.** Figure 4.2: Schematic representation of the protein synthesis process. [PITH_FULL_IMAGE:figures/full_fig_p058_4_2.png]

**Figure 4.3.** Figure 4.3: Steps of the microarray data acquisition process. [PITH_FULL_IMAGE:figures/full_fig_p064_4_3.png]

**Figure 4.4.** Figure 4.4: Steps of the RNA-seq data acquisition process. [PITH_FULL_IMAGE:figures/full_fig_p067_4_4.png]

**Figure 4.5.** Figure 4.5: Graphical representation of the steps required for constructing a signed gene co [PITH_FULL_IMAGE:figures/full_fig_p075_4_5.png]

**Figure 5.1.** Figure 5.1: Multidimensional scaling applied to transcriptome data from different brain samples [PITH_FULL_IMAGE:figures/full_fig_p092_5_1.png]

**Figure 6.1.** Figure 6.1: Schematic of the analysis workflow. valid ASD biomarker. A schematic representation of this workflow is provided in [PITH_FULL_IMAGE:figures/full_fig_p097_6_1.png]

**Figure 7.1.** Figure 7.1: Schematic of the neuroimaging analysis workflow. [PITH_FULL_IMAGE:figures/full_fig_p101_7_1.png]

**Figure 7.2.** Figure 7.2: Schematic representation of the network structure. [PITH_FULL_IMAGE:figures/full_fig_p105_7_2.png]

**Figure 8.1.** Figure 8.1: Schematic of the transcriptomic analysis workflow. [PITH_FULL_IMAGE:figures/full_fig_p112_8_1.png]

**Figure 9.1.** Figure 9.1: Schematic of the imaging genetics analysis workflow. [PITH_FULL_IMAGE:figures/full_fig_p120_9_1.png]

**Figure 10.1.** Figure 10.1: Whisker plot of the mean distance index. Values are rescaled between 0 and 1 to ease [PITH_FULL_IMAGE:figures/full_fig_p129_10_1.png]

**Figure 10.2.** Figure 10.2: Histogram of the regional value of the smoothgrad output averaged across all subjects. [PITH_FULL_IMAGE:figures/full_fig_p130_10_2.png]

**Figure 10.3.** Figure 10.3: Histogram of the number of subjects in which each region is in the top 20 for rel [PITH_FULL_IMAGE:figures/full_fig_p130_10_3.png]

**Figure 10.4.** Figure 10.4: View of the 19 regions found relevant. 113 [PITH_FULL_IMAGE:figures/full_fig_p131_10_4.png]

**Figure 10.5.** Figure 10.5: 2-D views of the average brain as seen through [PITH_FULL_IMAGE:figures/full_fig_p132_10_5.png]

**Figure 10.6.** Figure 10.6: The glass brains in (a) and (b) represents the saliency pattern extracted with [PITH_FULL_IMAGE:figures/full_fig_p133_10_6.png]

**Figure 11.1.** Figure 11.1: Density histograms showing the gene expression levels quantified with two different [PITH_FULL_IMAGE:figures/full_fig_p136_11_1.png]

**Figure 11.** Figure 11: shows the histogram of the p-values of the effect of diagnosis on the expression [PITH_FULL_IMAGE:figures/full_fig_p138_11.png]

**Figure 11.2.** Figure 11.2: P-values of disease effect in the LMMs computed on all genes and all subjects under [PITH_FULL_IMAGE:figures/full_fig_p139_11_2.png]

**Figure 11.3.** Figure 11.3: P-values of disease effect in the LMMs computed only on the genes of Dup15q and [PITH_FULL_IMAGE:figures/full_fig_p140_11_3.png]

**Figure 11.4.** Figure 11.4: Intersections between the lists of relevant genes found in the whole dataset (Fig. a) [PITH_FULL_IMAGE:figures/full_fig_p140_11_4.png]

**Figure 11.5.** Figure 11.5: Examples of genes found to be relevant for ASD analyzing [PITH_FULL_IMAGE:figures/full_fig_p141_11_5.png]

**Figure 11.6.** Figure 11.6: 2D MDS plots obtained with the standardized euclidean distance of the GE values [PITH_FULL_IMAGE:figures/full_fig_p142_11_6.png]

**Figure 11.7.** Figure 11.7: Hierachically-clustered heatmaps of the GE values across C, F and T of all the genes [PITH_FULL_IMAGE:figures/full_fig_p143_11_7.png]

**Figure 11.8.** Figure 11.8: Results of the permutation analysis testing whether the sets of genes found with the [PITH_FULL_IMAGE:figures/full_fig_p143_11_8.png]

**Figure 11.** Figure 11: shows the distributions of the DS scores, computed by [284], across the genes found [PITH_FULL_IMAGE:figures/full_fig_p144_11.png]

**Figure 11.9.** Figure 11.9: Distribution of the Pearson correlation computed by [284] as a measure of gene DS, [PITH_FULL_IMAGE:figures/full_fig_p146_11_9.png]

**Figure 11.** Figure 11: shows the histogram of the similarity index derived from the delta-con values obtained [PITH_FULL_IMAGE:figures/full_fig_p146_11.png]

**Figure 11.10.** Figure 11.10: Histogram of the distribution of the network similarities as measure with the delta [PITH_FULL_IMAGE:figures/full_fig_p146_11_10.png]

**Figure 11.11.** Figure 11.11: Dendrogram representation of the computed network. [PITH_FULL_IMAGE:figures/full_fig_p147_11_11.png]

**Figure 11.** Figure 11: shows how each module is enriched by each list of relevant genes found in the LMM [PITH_FULL_IMAGE:figures/full_fig_p147_11.png]

**Figure 11.12.** Figure 11.12: Image showing which modules of the network are enriched by each list of relevant [PITH_FULL_IMAGE:figures/full_fig_p148_11_12.png]

**Figure 11.13.** Figure 11.13: Representation of the whole network (Fig. a), and of the modules enriched for the [PITH_FULL_IMAGE:figures/full_fig_p149_11_13.png]

**Figure 12.** Figure 12: shows an example of the plots made to assess whether the expression levels of a module [PITH_FULL_IMAGE:figures/full_fig_p151_12.png]

**Figure 12.1.** Figure 12.1: Absolute average Z-score-transformed expression value of the eigengenes of the 5 [PITH_FULL_IMAGE:figures/full_fig_p152_12_1.png]

**Figure 12.2.** Figure 12.2: Absolute average Z-score-transformed expression value of the eigengenes of the [PITH_FULL_IMAGE:figures/full_fig_p152_12_2.png]

**Figure 12.3.** Figure 12.3: Examples of the plots used to identify gene modules more expressed (in absolute [PITH_FULL_IMAGE:figures/full_fig_p154_12_3.png]

**Figure 11.** Figure 11: shows that the differential stability of the genes found analyzing DT datasets is [PITH_FULL_IMAGE:figures/full_fig_p161_11.png]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

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