Deep Learning in Single-Cell and Spatial Transcriptomics Data Analysis: Advances and Challenges from a Data Science Perspective

Huan Xu; Qiang Cheng; Shuang Ge; Shuqing Sun; Zhixiang Ren

REVIEW 4 major objections 6 minor 230 references

Deep Learning in Single-Cell and Spatial Transcriptomics Data Analysis: Advances and Challenges from a Data Science Perspective

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This review maps the deep learning toolbox for single-cell and spatial transcriptomics onto four data challenges — sparsity, diversity, scarcity, and correlation — and compares 58 methods across 21 benchmark datasets.

desk verdict A useful descriptive review that overclaims its benchmark contribution: the 'evaluation' is a re-visualization of prior benchmarks, and the authors need to say so and release their aggregation code. read the letter →

arxiv 2412.03614 v2 pith:5RWB2QDJ submitted 2024-12-04 q-bio.GN cs.LG

Shuang Ge , Shuqing Sun , Huan Xu , Qiang Cheng , Zhixiang Ren This is my paper · ORCID

classification q-bio.GNcs.LG

keywords single-cellRNAsequencingspatialtranscriptomicsdeeplearningdatasparsitymultimodalintegrationbenchmarkevaluationcell-cellcommunicationscience

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

Deep learning is becoming the default tool for single-cell and spatial transcriptomics, but the field lacks a clear map of which methods work and why. This review's central claim is that the analytical problems all reduce to four data-science obstacles: sparsity (high-dimensional noisy, zero-inflated expression matrices), diversity (multiple modalities and independent data sources), scarcity (missing annotations and missing modalities), and correlation (spatiotemporal dependencies and prior knowledge). Working within that taxonomy, the paper curates 21 datasets from nine benchmarks covering 58 computational methods and presents side-by-side comparisons of their reported performance on imputation, multi-source integration, modality prediction, and cell-cell interaction inference. If the picture is right, it gives practitioners a principled way to match deep learning architecture families to specific data obstacles and a baseline view of where the current methods stand.

What carries the argument

The central organizing device is the four-challenge taxonomy — data sparsity, diversity, scarcity, and correlation — which frames every method family (autoencoders, GANs, VAEs, CNNs, GNNs, statistical models) by the data property it is designed to overcome. The comparative engine is a curated benchmark corpus: 21 datasets drawn from nine prior benchmark studies, covering 58 computational methods. Figures 3, 8, 11, and 14 re-visualize the reported scores from those studies on common scales so that methods can be compared across imputation, multi-source integration, modality prediction, and cell-cell interaction tasks.

What would settle it

Re-running all 58 methods on the same 21 datasets under one common preprocessing and evaluation pipeline would settle the claims; if the rankings in Figures 3, 8, 11, and 14 (for example, DCA and scIGANs leading imputation, totalVI leading modality prediction, CellPhoneDB leading cell-cell interaction) do not reproduce, the comparative conclusions would be falsified. A cheaper check is to compare the re-plotted values directly against the numbers published in the source benchmarks.

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Extended reading notes

Core claim

On its own terms, the paper establishes that the diverse deep learning literature in single-cell and spatial transcriptomics can be organized by the data problem it solves, and that each problem has a characteristic winning strategy. For data sparsity, autoencoder and generative approaches such as DCA and scIGANs show the best imputation consistency and clustering across five benchmark datasets. For multi-source integration, NMF-based and nearest-neighbor methods dominate batch correction and biological conservation across seven benchmark datasets, while VAE- and GAN-based models also preserve biology. For missing modalities, totalVI achieves the highest cell-cell Pearson correlation on predicted modalities, with PoE leading among VAE-based models. For cell-cell interaction inference, CellPhoneDB is consistently among the top across five benchmark datasets. The paper also identifies three forward-looking needs: novel AI methods, fair and biologically meaningful benchmark datasets, and practical applications in biology, medicine, and clinical care.

Load-bearing premise

The paper's comparative conclusions rest on the assumption that the benchmark studies it re-visualizes were correct, mutually comparable, and run under conditions that still represent current practice, since no method is re-run in this review.

Editorial extensions

If this is right

Practitioners can choose an architecture family from the dominant data obstacle: autoencoder/generative models for sparse noisy matrices, NMF or nearest-neighbor methods for multi-source integration, and VAE-based joint embeddings for missing modalities.
The benchmark corpus gives a ready-made testbed: any new imputation, integration, modality-prediction, or cell-cell interaction method can be positioned against the 58 methods already summarized on these 21 datasets.
The taxonomy suggests that the field's bottleneck is no longer a single algorithm but the absence of fair, biologically validated benchmarks and standardized evaluation metrics.
The review's future-directions list points to foundation models and agent-based systems as the next step for interpretable, generalizable analysis of single-cell and spatial data.

Reading between the lines

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

Editorial extension: because the comparative figures re-plot prior benchmark results rather than new experiments, the rankings inherit any preprocessing choices, metric choices, or dataset biases of the source benchmarks; a uniform rerun would be needed before the rankings are treated as definitive.
The four-challenge taxonomy could be reused as a design template for new spatial multi-omics methods: sparsity and missing-modality problems favor deep generative models with explicit noise models, while correlation problems favor graph-based architectures; this division is implicit in the survey but not stated as a design rule.
A testable extension is to build hybrid models that keep the interpretable statistical layers highlighted in the review (negative-binomial/ZINB count models, GAMLSS simulation) and wrap them in deep encoders; comparing such hybrids against DCA, totalVI, and CellPhoneDB on the same 21 datasets would directly test the taxonomy's predictive value.

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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

Colleague,

You should know this paper is a review, not a new experimental result. It organizes the deep-learning toolkit for single-cell and spatial transcriptomics around four data-science challenges—sparsity, diversity, scarcity, correlation—and it does that well. The descriptive sections are accurate, current, and well-referenced, and the tables of databases plus the structured method trees give practitioners a genuinely useful map. If you work in this area, it is worth having on hand as a reference.

The new contribution is organizational, not empirical. The four-challenge framing is a reasonable lens, and the paper says sensible things about future directions. But the benchmark section is where I would push back. The abstract and Key Points claim the authors curated 21 datasets from 9 benchmarks covering 58 methods and "evaluated their performance." The figures, however, are captioned "Revisualize the benchmark results" from earlier papers. No methods are rerun, no scores are tabulated, and no code or plotting scripts are provided. That means every comparative claim—DCA and scIGANs best at imputation consistency, totalVI highest cell-cell PCC, CellPhoneDB among the top—rests on the original benchmarks plus the authors' re-aggregation, and readers cannot check the re-aggregation. This is a transparency problem, not misconduct, but it directly undercuts the strength of the "evaluated" claim.

There are also small but telling slips: the imputation section says 12 methods were collected but lists 11, and there are a few typos. Those are minor; the benchmark provenance is the real issue.

My bottom line: this is a solid review that needs an honest rewrite of its benchmark claims. If the authors reframe the figures as a synthesis of existing benchmark results, make the aggregation rules explicit, and release the plotting code, it is a useful contribution. As is, it should not be presented as an independent evaluation. I would send it to peer review, but with the expectation of a significant revision before publication.

Recommendation: engage with it, but insist on the transparency fixes.

Referee Report

4 major / 6 minor

Summary. This review paper surveys deep learning methods for single-cell and spatial transcriptomics data analysis, organizing the field around four data-science challenges: sparsity, diversity, scarcity, and correlation. For each challenge it describes representative methods, often with mathematical formulations, and it claims to have curated 21 datasets from 9 benchmarks covering 58 computational methods and to have evaluated these methods on their respective tasks. The actual benchmark sections, however, re-visualize results from prior benchmark papers rather than reporting new runs. The review also catalogs sequencing technologies, public databases, and future research directions regarding AI methodologies, benchmarks, and applications.

Significance. If accepted as a survey, the paper offers a broad and current map of deep learning approaches for single-cell and spatial transcriptomics, with a useful organizing framework (sparsity, diversity, scarcity, correlation) and a rich reference list. The mathematical descriptions of selected methods (e.g., scvis, scVI, scDesign3) add pedagogical value, and the coverage of databases and future directions is generally accurate and up to date. However, the paper's central comparative contribution—the benchmark-based method map—is currently not independently checkable: the figures are re-visualizations of prior benchmarks, no code or data are provided, and the aggregation rules are only loosely described. This limits the scientific value of the claimed evaluation, although it is a correctable presentation and transparency issue rather than a fundamental flaw in the survey's descriptive content.

major comments (4)

[Abstract and Key Points; §3.1.2, §3.2.2, §3.3.2, §3.4.2] The abstract and Key Points state that the authors 'curated 21 datasets from 9 benchmarks, encompassing 58 computational methods, and evaluated their performance,' but the manuscript does not perform any new evaluation. Figures 3, 8, 11, and 14 are explicitly captioned 'Revisualize the benchmark results' from prior studies (refs [41,14], [131], [86,137], [208,164,214,125]), and no methods are rerun, no per-dataset scores are tabulated, and no code or plotting scripts are supplied. The comparative conclusions—e.g., 'DCA and scIGANs each achieved the highest imputation consistency,' 'totalVI shows highest cell-cell PCC,' and 'CellPhoneDB ranks among the top'—therefore rest entirely on the correctness and comparability of the original benchmarks plus the authors' re-aggregation of those numbers. This is a transparency and reproducibility problem: a reader cannot independently check the comparative map. Please either reframe the claim as a re-visualization with explicit caveats about inherited benchmark limitations or provide the aggregated data tables, plotting scripts, and a precise description of the aggregation procedure, or both.
[§3.1.2 (Imputation)] The text states 'We have collected 12 methods, including ... to evaluate their imputation performance,' but only 11 methods are listed: scImpute, SAVER, ALRA, MAGIC, scTSSR, DCA, DrImpute, DeepImpute, AutoImpute, scIGANs, and scGAIN. This internal count discrepancy, combined with the loosely specified aggregation rules in the Figure 3 caption (e.g., 'consistancy refers to the mean metrics of F1, AUC and ACC'), makes it impossible to reproduce the imputation comparison from the text alone. Please correct the count and provide the exact metric definitions and source values used.
[§3.4.2 and Figure 14] The claim that the authors 'evaluate their cell-cell interaction prediction performance on five benchmark datasets' again refers to re-visualizations of refs [208,164,214,125]. The figure shows five datasets but the caption groups them into four benchmarks, and the mapping of datasets to benchmarks and metrics (precision, F1, sum of communication scores, AUC, distance enrichment) is not tabulated. Without a clear mapping of the underlying raw numbers, the statement 'CellPhoneDB ranks among the top across all benchmark datasets' cannot be verified. Please add a supplementary table with the per-dataset scores for each method.
[Abstract, Key Points, and §3] The paper nowhere provides a consolidated list of the claimed 21 datasets, 9 benchmarks, or 58 computational methods, even though these numbers are central to the abstract's promise. The reader cannot verify the curation even at the level of counts. Please add a supplementary table enumerating each benchmark source, the datasets used, the methods included, and the section where each comparison appears.

minor comments (6)

[Figure 3 caption] The word 'consistancy' is a typo for 'consistency.'
[§3.3.1 and Figure 9] The method name is spelled 'GlouNdGAN' in the text and 'GRouNdGAN' in Figure 9 and the reference list; please standardize the spelling.
[§4.1] The text refers to 'OpenAI’s O13' but the intended model name is likely 'O1'; the footnote marker for the O1 URL also appears as a superscript '3' rather than a footnote symbol.
[§4.3] There is a typo: 'in additon' should be 'in addition.'
[Table 1] The 'Cells (M)' column lists values such as '3' and '5.6' without explicitly stating that the unit is millions in the header; please clarify the units or use a consistent notation.
[General] Several figures (e.g., Figures 5, 7, 10) are dense and contain small text; consider enlarging fonts and adding panel labels to improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a review whose comparative claims are re-visualizations of external benchmark papers, not derivations from its own inputs.

full rationale

This manuscript is a narrative review, not a derivation or prediction pipeline, so the circularity patterns enumerated in the rubric do not apply. The abstract states that the authors 'curated 21 datasets from 9 benchmarks, encompassing 58 computational methods, and evaluated their performance on the respective modeling tasks,' but the figures themselves are captioned as re-visualizations of prior benchmark results, e.g., Figure 3: 'Revisualize the benchmark results for data imputation from five benchmark datasets[41, 14]' and Figure 8: 'Revisualize the benchmark results for multi-source data integration from seven benchmark datasets[131].' The conclusions, such as 'DCA and scIGANs each achieved the highest imputation consistency' and 'totalVI shows highest cell-cell PCC,' are inherited from those external benchmark papers rather than generated by any model, fit, or self-referential argument in the present work. There is no fitted parameter that is later called a prediction, no uniqueness theorem imported from the authors' own prior work, and no ansatz smuggled in via self-citation; indeed, the reference list contains no self-citations by the authors. The lack of code, per-dataset score tables, and reruns is a transparency and reproducibility limitation that could undermine the strength of the 'evaluation' claim, but it is not circularity under the rubric: the review's content is not equivalent to its own inputs by construction, and its comparative statements are externally sourced rather than self-derived. Accordingly, the appropriate finding is no significant circularity, with a score of 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The review introduces no fitted parameters and no new postulated entities. Its content depends on the authors' taxonomic choices and on the validity of the prior benchmark results it re-visualizes, plus standard domain assumptions about single-cell data modeling.

assumptions (3)

ad hoc to paper The four challenges (sparsity, diversity, scarcity, correlation) form a complete and useful organizing framework for deep learning methods in this field.
Section 1 introduces these four as the review's backbone; this categorization is the authors' own construction and is not derived from an external benchmark or formal analysis.
ad hoc to paper Re-plotted benchmark outcomes from prior papers are treated as valid proxies for a new evaluation of method performance.
The benchmark figures (Figures 3, 8, 11, 14) re-visualize results from prior benchmark papers, and the text interprets them as evidence for method comparisons, implicitly assuming the original benchmarks are correct and comparable.
domain assumption Standard generative and deep learning assumptions, such as zero-inflated negative binomial count models and variational inference, are appropriate for single-cell transcriptomics data.
Section 3.1 relies on ZINB and VAE frameworks (e.g., scVI, DCA) as accepted modeling choices, following the cited literature rather than re-deriving or testing these assumptions.

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Cite this review

Pith. "Pith review of Deep Learning in Single-Cell and Spatial Transcriptomics Data Analysis: Advances and Challenges from a Data Science Perspective." pith.science (2026). https://pith.science/paper/5RWB2QDJ

@misc{pith2026241203614,
  author       = {Pith},
  title        = {Pith review of: Deep Learning in Single-Cell and Spatial Transcriptomics Data Analysis: Advances and Challenges from a Data Science Perspective},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5RWB2QDJ}},
  note         = {Machine review of arXiv:2412.03614}
}

read the original abstract

The development of single-cell and spatial transcriptomics has revolutionized our capacity to investigate cellular properties, functions, and interactions in both cellular and spatial contexts. However, the analysis of single-cell and spatial omics data remains challenging. First, single-cell sequencing data are high-dimensional and sparse, often contaminated by noise and uncertainty, obscuring the underlying biological signals. Second, these data often encompass multiple modalities, including gene expression, epigenetic modifications, and spatial locations. Integrating these diverse data modalities is crucial for enhancing prediction accuracy and biological interpretability. Third, while the scale of single-cell sequencing has expanded to millions of cells, high-quality annotated datasets are still limited. Fourth, the complex correlations of biological tissues make it difficult to accurately reconstruct cellular states and spatial contexts. Traditional feature engineering-based analysis methods struggle to deal with the various challenges presented by intricate biological networks. Deep learning has emerged as a powerful tool capable of handling high-dimensional complex data and automatically identifying meaningful patterns, offering significant promise in addressing these challenges. This review systematically analyzes these challenges and discusses related deep learning approaches. Moreover, we have curated 21 datasets from 9 benchmarks, encompassing 58 computational methods, and evaluated their performance on the respective modeling tasks. Finally, we highlight three areas for future development from a technical, dataset, and application perspective. This work will serve as a valuable resource for understanding how deep learning can be effectively utilized in single-cell and spatial transcriptomics analyses, while inspiring novel approaches to address emerging challenges.

Figures

Figures reproduced from arXiv: 2412.03614 by the authors.

**Figure 1.** The overall structure of the article is organized into three main sections. (a) An overview of key sequencing technologies in single-cell and spatial transcriptomics; (b) A discussion of four significant scientific and technical challenges within the field from a data science perspective, namely: data sparsity, data diversity, data scarcity, and data correlation; (c) An exploration of potential future perspectives t… view at source ↗

**Figure 2.** The sequencing pipeline for single cell and spatial transcriptomics data. (a) Bulk-based technique provides average gene expression profiles at the tissue level, with cell proportions estimated through deconvolution methods. (b) Microfluidic-based techniques isolate individual cells into droplets or wells, followed by barcoding and sequencing. (c) Spatial barcode-based techniques utilize cell barcodes to capture pol… view at source ↗

**Figure 3.** Revisualize the benchmark results for data imputation from five benchmark datasets[41, 14]. In benchmark 1 (dataset 1 and 2), ’clustering’ represents the average value of clustering evaluation metrics, including NMI and ARI, while ’consistency’ includes PCC. In benchmark 2 (dataset 3-5), ’clustering’ represents the mean of the NMI and ARI, and ’consistancy’ refers to the mean metrics of F1, AUC and ACC. The green re… view at source ↗

Figures from the paper (12 more)

**Figure 4.** Figure 4: The structure of section "Data Sparsity" and related methods. The tree chart outlines the challenges associated with processing sparse single-cell data, focusing on issues including the curse of dimensionality, noise, and uncertainty. whereas the discriminator distingu…

**Figure 5.** Figure 5: The challenges and typical approaches for data sparsity. Neural networks often modeling data representations in complex latent spaces, particularly in scenarios with increased factors of variability, such as uncertainties in experimental processes. (a) For curse of di…

**Figure 6.** Figure 6: 3.2.1 Multimodal Data Alignment This section focuses on the alignment of multimodal data, including multi-omics data as well as paired single-cell and spatial transcriptomics data, with the goal of uncovering intrinsic patterns in the alignment of homologous data. 11 …

**Figure 6.** Figure 6: The structure of section "Data Diversity" and related methods. The tree chart outlines the challenges associated with processing multi-view single-cell data, focusing on issues including multimodal data alignment, and the integration of multi-source data. Multi-omics D…

**Figure 7.** Figure 7: The challenges and typical approaches for data diversity. The integration of multi-modal and multi-omics data via DL is a trend in the study of data diversity. (a) For multi-omics integration, we plotted the framework of LIGER[204]. Multi-omics data alignment aims to a…

**Figure 8.** Figure 8: Revisualize the benchmark results for multi-source data integration from seven benchmark datasets[131]. This analysis includes 19 reported methods, including nearest neighbor (NN), PCA, AE, VAE, NMF, Bayes, and GAN. The orange rectangle represents the largest point siz…

**Figure 9.** Figure 9: The structure of section "Data Scarcity" and related methods. The tree chart outlines the challenges related to the scarcity of high-quality single-cell data, emphasizing issues such as missing data annotation and missing modalities. model regulatory interactions betwe…

**Figure 10.** Figure 10: The challenges and typical approaches for data scarcity. DL-based methods mainly rely on VAE architectures, using either single-modality or dual-modality joint embeddings for feature modeling to learn a shared latent space. We plot the basic architectures of the follo…

**Figure 11.** Figure 11: Revisualize the benchmark results for modality prediction from four benchmark datasets[86, 137]. The orange rectangle represents the largest point size, while the green rectangle indicates the points with the highest color value, closest to red on the color bar. In Be…

**Figure 12.** Figure 12: The structure of section "Data correlation" and related methods. The tree chart outlines the challenges related to data correlation, emphasizing challenges such as modeling spatiotemporal dependencies and prior knowledge. cell-cell interactions, and complex tissue or …

**Figure 13.** Figure 13: The challenges and typical approaches for data correlation. DL methods for integrating prior knowledge and modeling data correlation typically use three fusion strategies: early, intermediate, and late fusion. (a) For early fusion, DeepLinc[117] constructs a cell adja…

**Figure 14.** Figure 14: Revisualize the benchmark results for cell-cell interactions from five benchmark datasets[208, 164, 214, 125]. "Metric 1 and Metric 2 are both accuracy metrics for cell-cell interactions. In Benchmark 1 (Datasets 1 and 2), ’Metric 1’ refers to precision, and ’Metric 2…

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

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