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REVIEW 2 major objections 6 minor 134 references

Single-cell spatial (scs) omics: Recent developments in data analysis

T0 review · 2 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This review claims that diverse single-cell spatial omics data, across transcriptomics, genomics, epigenomics, proteomics, and metabolomics, can be organized by one tensor structure, and that modeling spatial dependence, heterogeneity…

desk verdict Competent scs omics survey with a solid ML taxonomy, but it overpromises coverage of genomics and epigenomics and needs citation fixes. read the letter →

arxiv 2412.13591 v1 pith:N7LWWGPO submitted 2024-12-18 stat.CO q-bio.GN

José Camacho , Michael Sorochan Armstrong , Luz García-Martínez , Caridad Díaz , Carolina Gómez-Llorente This is my paper · ORCID
classification stat.COq-bio.GN
keywords single-cellspatialomicstranscriptomicsmetabolomicsmassspectrometryimagingtensordatarepresentationdimensionalityreductionspatiallyinformedmachinelearningdependence
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

Single-cell spatial (scs) omics data arrive from very different technologies, from imaging-based transcriptomics to mass spectrometry imaging for proteins and metabolites, but the paper argues they share a common mathematical skeleton: a tensor with one mode for individuals, one for omics features, and two or three spatial coordinates. By placing this tensor at the center, the review clarifies what each computational step does: preprocessing turns raw measurements into omics-per-pixel tensors or omics-per-cell matrices, dimensionality reduction methods either ignore or exploit the spatial indices, and machine-learning models encode spatial information either in the input matrix or in the learning algorithm. The authors' thesis is that scs data should be treated as spatial stochastic processes with dependence, heterogeneity, and scale, and that explicitly modeling these properties will determine whether scs omics delivers on its promise for studying disease across time and space. The paper also flags the load-bearing requirement that samples from different individuals be aligned to a common coordinate system, which it calls a strong assumption.

What carries the argument

The central object is the tensor $\mathbf{\underline{X}}_s$ with dimensions $I \times J \times S_1 \times S_2$ (and its three-dimensional extension), which represents omics-per-pixel or omics-per-grid-cell data for $I$ individuals, $J$ features, and $S_1 \times S_2$ spatial locations. This tensor does the work of making spatial information explicit before cell segmentation; after segmentation, spatial information is carried instead by a coarse grid or a neighborhood graph. Around this object the paper organizes the whole survey: the tensor explains why spatially agnostic methods can be trustworthy for spatial patterns (they were not looking for them), why spatially informed projections need extra validation, and why alignment of samples to a common coordinate system is the prerequisite for spatio-temporal analysis.

What would settle it

Take scs datasets of the same tissue produced by different laboratories or platforms and attempt to align their spatial domains to a common coordinate system; if cell-type distributions and spatial domains cannot be matched beyond chance, the tensor representation's core assumption fails for that tissue and the paper's framework cannot support cross-sample spatio-temporal inference.

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Extended reading notes

Core claim

The paper's central contribution is a unified view of scs omics as a data-analysis problem. It defines spatial resolution and spatial localization as separate properties, surveys acquisition technologies across the five major omics modalities, and proposes the four-way tensor $\mathbf{\underline{X}}_s$ with dimensions $I \times J \times S_1 \times S_2$ for spatially resolved measurements on a two-dimensional grid. Using this structure, it classifies dimensionality-reduction methods as spatially agnostic (PCA, t-SNE, UMAP, and relatives) or spatially informed (variants of PCA and matrix factorization that build spatial correlation into the model), and it organizes machine-learning approaches by whether spatial properties enter through the observation matrix or through the learning algorithm itself. The authors' main thesis is that the spatial properties of dependence, heterogeneity, and scale are information, not noise, and that future progress depends on modeling them explicitly while borrowing ideas from multivariate image analysis, multivariate curve resolution, and spectral analysis.

Load-bearing premise

The load-bearing premise is that spatial samples from different individuals can be transformed to a common coordinate system so that the tensor $\mathbf{\underline{X}}_s$ is meaningful across a study; the paper itself calls this a strong requirement, and if alignment fails, cross-sample spatio-temporal analysis of scs data loses its foundation.

Editorial extensions

If this is right

  • If the tensor representation is adopted, a single data organization can serve transcriptomics, genomics, epigenomics, proteomics, and metabolomics, easing cross-modal method transfer.
  • Because spatially agnostic visualizations can be trusted for spatial patterns while spatially informed ones require extra validation, the paper's framework implies that scs studies should report both kinds of projection to distinguish genuine tissue organization from method-induced smoothness.
  • Extending experimental-design-aware tools such as ASCA and PERMANOVA to spatial, massive, sparse data would let scs analyses incorporate randomization, replication, and blocking, increasing statistical power.
  • Alignment algorithms are a cornerstone for spatio-temporal studies, and their improvement determines whether the tensor structure can be used across individuals and time points.
  • Ideas from multivariate image analysis, multivariate curve resolution, and spectral analysis are ready-made sources of spatially informed methods for scs omics.

Reading between the lines

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

  • One testable extension: benchmark spatially agnostic against spatially informed projections on the same tissue, measuring how often spatial domains reproduce in held-out samples; this would quantify the paper's warning that informed methods can find spatial patterns even where none exist.
  • If cross-sample alignment becomes routine, the same tensor framework could enable cohort-level spatio-temporal atlases, comparing disease progression across individuals at matched spatial coordinates, a consequence the paper points toward but does not develop.
  • The paper's taxonomy suggests that modern attention-based and generative models, mentioned only briefly, could be grafted onto the tensor structure for super-resolution and missing-data imputation in scs data.
  • A practical extension of the review's logic is that method developers should report whether their pipeline is spatially agnostic or informed, since the trust one can place in discovered spatial patterns depends on that choice.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 6 minor

Summary. This manuscript is a review of single-cell spatial (scs) omics from a data-analysis perspective. It introduces a glossary distinguishing spatial resolution from spatial localization, surveys acquisition technologies for transcriptomics/genomics and for proteomics/metabolomics, describes computational pipelines and a tensor data representation for spatially resolved data, reviews dimensionality reduction methods under the spatially agnostic/informed dichotomy, and surveys machine-learning approaches for modeling spatial information. It closes with future challenges centered on spatio-temporal modeling and experimental design integration.

Significance. If delivered at its stated breadth, the review would be a useful cross-modal entry point for researchers moving from single-cell transcriptomics into spatial methods, and it would help disseminate ideas from multivariate image analysis and chemometrics into the spatial-omics community. The paper is strongest where it synthesizes concepts: the resolution/localization distinction, the explicit treatment of spatially agnostic versus spatially informed dimensionality reduction, the discussion of tensor unfolding for omics-per-pixel data, the connection to MIA/MCR/spectral-analysis literature, and the acknowledgment that the tensor representation requires a common coordinate system across samples. It also provides a broad and current reference list. The main weakness is that the stated scope, covering all major omics modalities, is not matched by the actual content, particularly for genomics and epigenomics, so the review's central claim of comprehensive coverage needs either substantial expansion or a more modest framing.

major comments (2)
  1. [Abstract; Section 1; Sections 3.1.1–3.1.2; Sections 4–6] The survey's stated goal of covering all major modalities, including genomics and epigenomics, is not met by the actual content. Section 3.1.1 gives only two sentences to imaging-based genome and epigenome profiling, and Section 3.1.2 mentions microfluidic barcoding for ATAC&RNA-seq, spatial CUT&Tag-RNA, and DNA-GPS, but Sections 4–6, the computational core that distinguishes this review, discuss almost exclusively transcriptomics and MSI-based metabolomics/proteomics. There is no treatment of computational challenges specific to scs genomics (e.g., spatial copy-number alterations, somatic mutation inference) or scs epigenomics (e.g., spatial peak calling, chromatin domains). The comprehensive-overview claim in the abstract is therefore not supported by the body of the paper. Please either add dedicated computational material for these modalities or revise the stated scope to match the content.
  2. [Section 4.2] The tensor representation Xs (I × J × S1 × S2) is presented as the general data structure for spatial omics, but it depends on the assumption that samples from different individuals can be transformed to a common coordinate system. The authors acknowledge this as 'a strong requirement' and cite alignment algorithms [56]–[59], but the subsequent discussion in Sections 5 and 6 largely treats Xs as the default setting without explaining how often this assumption holds or what an analyst should do when alignment fails. The alternative neighborhood-network or coarse-grid representations are mentioned only briefly at the end of Section 4.2; the relationship between these alternatives and the tensor formalism, and the consequences for each downstream step, deserve more development given the review's stated focus on challenges in downstream analysis.
minor comments (6)
  1. [Section 1] Reference [15], cited for sequencing preprocessing, is a deep-learning attention-mechanism paper and does not support the stated claim; please replace it with a relevant preprocessing reference or remove it.
  2. [Section 3.1.2] The sentence 'The detection efficiency of FISSEQ is lower than 0.005%' is an uncited quantitative claim; it should cite the primary FISSEQ work or be removed.
  3. [Section 6.2.1] The claim about a deep masked auto-encoder is attributed to 'Prasad et al. [113]', but reference [113] is BayesSpace by Zhao et al.; this citation appears to be mismatched and should be corrected.
  4. [Section 3.1.2] The technique name 'DBIiT-seq' is a typo and should read 'DBiT-seq'.
  5. [Section 3.1.1] In the sentence listing seqFISH+, MERFISH, and EEL-FISH with citations [29], [30], reference [30] is the DNA-GPS theoretical framework; please verify whether [30] supports the EEL-FISH sentence or replace it with [32].
  6. [Figure 1] The bibliometric counts in Figure 1 are described only as extracted from the Web of Knowledge; the exact query strings, search dates, and inclusion criteria should be reported to make the figure reproducible.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: this is a survey whose content is external literature review, and the few self-citations are non-essential supporting references.

full rationale

This manuscript is a review/survey, not a derivation-based paper. It does not claim to derive new results or make predictions from fitted parameters. Its central content consists of summarizing external technologies, data structures, dimensionality-reduction methods, and machine-learning approaches, with citations to the original literature. The only self-citations are [101] (an arXiv paper on inverse non-uniform fast Fourier transform) and [106] (a paper on ASCA power curves), both used as supporting references for isolated technical points: [101] supports a remark about the difficulty of inverting a non-uniform FFT, and [106] is cited alongside [105] to note that power analysis tools exist for PERMANOVA/ASCA. Neither citation is load-bearing for the survey's organizational or conceptual claims, and neither is used to justify the survey's scope, classification, or conclusions. The paper also does not rename a known result or smuggle in an ansatz through self-citation; it explicitly positions itself as covering existing methodologies. The closest thing to an internal assumption is the tensor representation in Section 4.2, which the authors themselves flag as 'a strong requirement,' but that is a stated modeling limitation, not a circular argument. No step in the paper reduces by construction to its inputs. The circularity score is therefore 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

As a survey, no new parameters or entities are introduced. The review's conclusions rest on the accuracy and representativeness of the cited literature.

assumptions (2)
  • domain assumption The categorization of methods into spatially agnostic and spatially informed is a valid organizing principle for scs data analysis.
    Used throughout Sections 5 and 6 to frame the review.
  • domain assumption The cited literature is accurately summarized, including specific numeric claims.
    The review makes a factual claim about FISSEQ detection efficiency (0.005%) with no citation, and at least one citation seems mismatched (EEL-FISH cited to DNA-GPS reference).

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Cite this review

Pith. "Pith review of Single-cell spatial (scs) omics: Recent developments in data analysis." pith.science (2026). https://pith.science/paper/N7LWWGPO

@misc{pith2026241213591,
  author       = {Pith},
  title        = {Pith review of: Single-cell spatial (scs) omics: Recent developments in data analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N7LWWGPO}},
  note         = {Machine review of arXiv:2412.13591}
}
read the original abstract

Over the past few years, technological advances have allowed for measurement of omics data at the cell level, creating a new type of data generally referred to as single-cell (sc) omics. On the other hand, the so-called spatial omics are a family of techniques that generate biological information in a spatial domain, for instance, in the volume of a tissue. In this survey, we are mostly interested in the intersection between sc and spatial (scs) omics and in the challenges and opportunities that this new type of data pose for downstream data analysis methodologies. Our goal is to cover all major omics modalities, including transcriptomics, genomics, epigenomics, proteomics and metabolomics.

Figures

Figures reproduced from arXiv: 2412.13591 by the authors.

Figure 1
Figure 1. (a) also shows that recent years have seen a surge of interest in spatial data from metabolomics but especially transcriptomics experiments. Regarding sc technologies ( [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. B) [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 2
Figure 2. Data structure for A) omics with one modality, B) multi-omics, (C) spatial omics in a 2D grid The omics-per-cell data can be represented in a matrix with I cells and J features, just like in sc data. Yet, this matrix does not provide spatial information. Given that the cells have uneven volumes, the omics-per-cell data cannot be represented directly as a tensor like in Xs. There are two simple solutions to handle th… view at source ↗
Figures from the paper (1 more)
Figure 3
Figure 3. Figure 3: Data structure for A) multi-omics data unfolded into a single omics mode, (B) spatial multi-omics in a 2D grid and (C) unfolded multi-omics data in a single sample mode. 5.1 Spatially Agnostic Methods Let us consider a matrix X with I rows and J columns that represents…

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Works this paper leans on

134 extracted references · 76 canonical work pages

  1. [56]

    STalign: Alignment of Spatial Transcriptomics Data Using Diffeomorphic Metric Mapping,

    K. Clifton, M. Anant, G. Aihara, L. Atta, O. K. Aimiuwu, J. M. Kebschull, M. I. Miller, D. Tward, and J. Fan, "STalign: Alignment of Spatial Transcriptomics Data Using Diffeomorphic Metric Mapping," Nat. Commun., vol. 14, no. 1, Art. no. 8123, 2023, doi: 10.1038/s41467-023-43915-7

    doi:10.1038/s41467-023-43915-7 2023

  2. [59]

    Search and Match across Spatial Omics Samples at Single-Cell Resolution,

    Z. Tang, S. Luo, H. Zeng, J. Huang, X. Sui, M. Wu, and X. Wang, "Search and Match across Spatial Omics Samples at Single-Cell Resolution," Nature Methods, vol. 21, no. 10, pp. 1818-1829, 2024, doi: 10.1038/s41592-024-02410-7

    work page doi:10.1038/s41592-024-02410-7 2024

  3. [1]

    T. Hu, J. Li, H. Zhou, C. Li, E. C. Holmes, and W. Shi, ‘Bioinformatics resources for SARS- CoV-2 discovery and surveillance’, Brief. Bioinform., vol. 22, no. 2, pp. 631–641, Mar. 2021, doi: 10.1093/bib/bbaa386

    work page doi:10.1093/bib/bbaa386 2021

  4. [2]

    Alseekh et al., ‘Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices’, Nat

    S. Alseekh et al., ‘Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices’, Nat. Methods, vol. 18, no. 7, pp. 747–756, Jul. 2021, doi: 10.1038/s41592-021-01197-1

    work page doi:10.1038/s41592-021-01197-1 2021

  5. [3]

    Stuart et al., ‘Comprehensive Integration of Single-Cell Data’, Cell, vol

    T. Stuart et al., ‘Comprehensive Integration of Single-Cell Data’, Cell, vol. 177, no. 7, pp. 1888-1902.e21, Jun. 2019, doi: 10.1016/j.cell.2019.05.031

    doi:10.1016/j.cell.2019.05.031 1902

  6. [4]

    Marx, ‘Method of the Year: spatially resolved transcriptomics’, Nat

    V. Marx, ‘Method of the Year: spatially resolved transcriptomics’, Nat. Methods, vol. 18, no. 1, pp. 9–14, Jan. 2021, doi: 10.1038/s41592-020-01033-y

    work page doi:10.1038/s41592-020-01033-y 2021

  7. [5]

    Eisenstein, ‘Seven technologies to watch in 2022’, Nature, vol

    M. Eisenstein, ‘Seven technologies to watch in 2022’, Nature, vol. 601, no. 7894, pp. 658– 661, Jan. 2022, doi: 10.1038/d41586-022-00163-x

    work page doi:10.1038/d41586-022-00163-x 2022

  8. [6]

    Accessed: Apr

    ‘Top 10 Emerging Technologies of 2023 report’, World Economic Forum. Accessed: Apr. 10, 2024. [Online]. Available: https://www.weforum.org/publications/top-10-emerging- technologies-of-2023/

    2023

Show all 134 references
  1. [7]

    S. Qin, D. Miao, X. Zhang, Y. Zhang, and Y. Bai, ‘Methods developments of mass spectrometry based single cell metabolomics’, TrAC Trends Anal. Chem., vol. 164, p. 117086, Jul. 2023, doi: 10.1016/j.trac.2023.117086

    2023

  2. [8]

    S. Ma, Y. Leng, X. Li, Y. Meng, Z. Yin, and W. Hang, ‘High spatial resolution mass spectrometry imaging for spatial metabolomics: Advances, challenges, and future perspectives’, TrAC Trends Anal. Chem., vol. 159, p. 116902, Feb. 2023, doi: 10.1016/j.trac.2022.116902

    2023

  3. [9]

    Kleino, P

    I. Kleino, P. Frolovaitė, T. Suomi, and L. L. Elo, ‘Computational solutions for spatial transcriptomics’, Comput. Struct. Biotechnol. J., vol. 20, pp. 4870–4884, 2022, doi: 10.1016/j.csbj.2022.08.043

    2022 doi

  4. [10]

    Y. Wu, Y. Cheng, X. Wang, J. Fan, and Q. Gao, ‘Spatial omics: Navigating to the golden era of cancer research’, Clin. Transl. Med., vol. 12, no. 1, p. e696, Jan. 2022, doi: 10.1002/ctm2.696

    2022 doi

  5. [11]

    Ahmed et al., ‘Spatial mapping of cancer tissues by OMICS technologies’, Biochim

    R. Ahmed et al., ‘Spatial mapping of cancer tissues by OMICS technologies’, Biochim. Biophys. Acta BBA - Rev. Cancer, vol. 1877, no. 1, p. 188663, Jan. 2022, doi: 10.1016/j.bbcan.2021.188663

    2022

  6. [12]

    L. Tian, F. Chen, and E. Z. Macosko, ‘The expanding vistas of spatial transcriptomics’, Nat. Biotechnol., vol. 41, no. 6, pp. 773–782, Jun. 2023, doi: 10.1038/s41587-022-01448-2

    2023 doi

  7. [13]

    Larsson, J

    L. Larsson, J. Frisén, and J. Lundeberg, ‘Spatially resolved transcriptomics adds a new dimension to genomics’, Nat. Methods, vol. 18, no. 1, pp. 15–18, Jan. 2021, doi: 10.1038/s41592-020-01038-7

    2021 doi

  8. [14]

    He et al., ‘Assessing the Impact of Data Preprocessing on Analyzing Next Generation Sequencing Data’, Front

    B. He et al., ‘Assessing the Impact of Data Preprocessing on Analyzing Next Generation Sequencing Data’, Front. Bioeng. Biotechnol., vol. 8, Jul. 2020, doi: 10.3389/fbioe.2020.00817

    2020

  9. [15]

    Y. Liu, Z. Shao, and N. Hoffmann, ‘Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions’. arXiv, Dec. 10, 2021. Accessed: Apr. 01, 2024. [Online]. Available: http://arxiv.org/abs/2112.05561

    2021 arXiv

  10. [16]

    Mishra, A

    P. Mishra, A. Biancolillo, J. M. Roger, F. Marini, and D. N. Rutledge, ‘New data preprocessing trends based on ensemble of multiple preprocessing techniques’, TrAC Trends Anal. Chem., vol. 132, p. 116045, Nov. 2020, doi: 10.1016/j.trac.2020.116045

    2020

  11. [17]

    M. D. Sorochan Armstrong, J. L. Hinrich, A. P. de la Mata, and J. J. Harynuk, ‘PARAFAC2×N: Coupled decomposition of multi-modal data with drift in N modes’, Anal. Chim. Acta, vol. 1249, p. 340909, Apr. 2023, doi: 10.1016/j.aca.2023.340909

    2023

  12. [18]

    W. W. B. Goh and L. Wong, ‘Dealing with Confounders in Omics Analysis’, Trends Biotechnol., vol. 36, no. 5, pp. 488–498, May 2018, doi: 10.1016/j.tibtech.2018.01.013

    2018 doi

  13. [19]

    Yamada, D

    R. Yamada, D. Okada, J. Wang, T. Basak, and S. Koyama, ‘Interpretation of omics data analyses’, J. Hum. Genet., vol. 66, no. 1, pp. 93–102, Jan. 2021, doi: 10.1038/s10038-020- 0763-5

    2021 doi

  14. [20]

    Tarazona, A

    S. Tarazona, A. Arzalluz-Luque, and A. Conesa, ‘Undisclosed, unmet and neglected challenges in multi-omics studies’, Nat. Comput. Sci., vol. 1, no. 6, pp. 395–402, Jun. 2021, doi: 10.1038/s43588-021-00086-z

    2021 doi

  15. [21]

    Lähnemann et al., ‘Eleven grand challenges in single-cell data science’, Genome Biol., vol

    D. Lähnemann et al., ‘Eleven grand challenges in single-cell data science’, Genome Biol., vol. 21, no. 1, p. 31, Feb. 2020, doi: 10.1186/s13059-020-1926-6

    2020 doi

  16. [22]

    Velten and O

    B. Velten and O. Stegle, ‘Principles and challenges of modeling temporal and spatial omics data’, Nat. Methods, vol. 20, no. 10, pp. 1462–1474, Oct. 2023, doi: 10.1038/s41592-023- 01992-y

    2023 doi

  17. [23]

    B. A. M. Bouwman, N. Crosetto, and M. Bienko, ‘The era of 3D and spatial genomics’, Trends Genet., vol. 38, no. 10, pp. 1062–1075, Oct. 2022, doi: 10.1016/j.tig.2022.05.010

    2022 doi

  18. [24]

    Cao et al., ‘Microfluidics-based single cell analysis: from transcriptomics to spatiotemporal multi-omics’, TrAC Trends Anal

    J. Cao et al., ‘Microfluidics-based single cell analysis: from transcriptomics to spatiotemporal multi-omics’, TrAC Trends Anal. Chem., vol. 158, p. 116868, Jan. 2023, doi: 10.1016/j.trac.2022.116868

    2023

  19. [25]

    Z. Wang, M. Cao, S. M. Lam, and G. Shui, ‘Embracing lipidomics at single-cell resolution: Promises and pitfalls’, TrAC Trends Anal. Chem., vol. 160, p. 116973, Mar. 2023, doi: 10.1016/j.trac.2023.116973

    2023

  20. [26]

    Heumos et al., ‘Best practices for single-cell analysis across modalities’, Nat

    L. Heumos et al., ‘Best practices for single-cell analysis across modalities’, Nat. Rev. Genet., vol. 24, no. 8, pp. 550–572, Aug. 2023, doi: 10.1038/s41576-023-00586-w

    2023 doi

  21. [27]

    Wang et al., ‘Spatial transcriptomics: Technologies, applications and experimental considerations’, Genomics, vol

    Y. Wang et al., ‘Spatial transcriptomics: Technologies, applications and experimental considerations’, Genomics, vol. 115, no. 5, p. 110671, Sep. 2023, doi: 10.1016/j.ygeno.2023.110671

    2023

  22. [28]

    Vandereyken, A

    K. Vandereyken, A. Sifrim, B. Thienpont, and T. Voet, ‘Methods and applications for single- cell and spatial multi-omics’, Nat. Rev. Genet., vol. 24, no. 8, pp. 494–515, Aug. 2023, doi: 10.1038/s41576-023-00580-2

    2023 doi

  23. [29]

    C. G. Williams, H. J. Lee, T. Asatsuma, R. Vento-Tormo, and A. Haque, ‘An introduction to spatial transcriptomics for biomedical research’, Genome Med., vol. 14, no. 1, p. 68, Jun. 2022, doi: 10.1186/s13073-022-01075-1

    2022 doi

  24. [30]

    Greenstreet et al., ‘DNA-GPS: A theoretical framework for optics-free spatial genomics and synthesis of current methods’, Cell Syst., vol

    L. Greenstreet et al., ‘DNA-GPS: A theoretical framework for optics-free spatial genomics and synthesis of current methods’, Cell Syst., vol. 14, no. 10, pp. 844-859.e4, Oct. 2023, doi: 10.1016/j.cels.2023.08.005

    2023 doi

  25. [31]

    D. J. Burgess, ‘Spatial transcriptomics coming of age’, Nat. Rev. Genet., vol. 20, no. 6, pp. 317–317, Jun. 2019, doi: 10.1038/s41576-019-0129-z

    2019 doi

  26. [32]

    L. E. Borm et al., ‘Scalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH’, Nat. Biotechnol., Sep. 2022, doi: 10.1038/s41587-022-01455-3

    2022 doi

  27. [33]

    Moses and L

    L. Moses and L. Pachter, ‘Museum of spatial transcriptomics’, Nat. Methods, vol. 19, no. 5, pp. 534–546, May 2022, doi: 10.1038/s41592-022-01409-2

    2022 doi

  28. [34]

    J. Chen, Y. Wang, and J. Ko, ‘Single-cell and spatially resolved omics: Advances and limitations’, J. Pharm. Anal., vol. 13, no. 8, pp. 833–835, Aug. 2023, doi: 10.1016/j.jpha.2023.07.002

    2023 doi

  29. [35]

    Wang et al., ‘Three-dimensional intact-tissue sequencing of single-cell transcriptional states’, Science, vol

    X. Wang et al., ‘Three-dimensional intact-tissue sequencing of single-cell transcriptional states’, Science, vol. 361, no. 6400, p. eaat5691, Jul. 2018, doi: 10.1126/science.aat5691

    2018 doi

  30. [36]

    Jiang et al., ‘A new direction in metabolomics: Analysis of the central nervous system based on spatially resolved metabolomics’, TrAC Trends Anal

    X. Jiang et al., ‘A new direction in metabolomics: Analysis of the central nervous system based on spatially resolved metabolomics’, TrAC Trends Anal. Chem., vol. 165, p. 117103, Aug. 2023, doi: 10.1016/j.trac.2023.117103

    2023

  31. [37]

    M. J. Taylor, J. K. Lukowski, and C. R. Anderton, ‘Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics’, J. Am. Soc. Mass Spectrom., vol. 32, no. 4, pp. 872–894, Apr. 2021, doi: 10.1021/jasms.0c00439

    2021 doi

  32. [38]

    Xing et al., ‘Next Generation of Mass Spectrometry Imaging: from Micrometer to Subcellular Resolution’, Chem

    L. Xing et al., ‘Next Generation of Mass Spectrometry Imaging: from Micrometer to Subcellular Resolution’, Chem. Biomed. Imaging, vol. 1, no. 8, pp. 670–682, Nov. 2023, doi: 10.1021/cbmi.3c00061

    2023 doi

  33. [39]

    Alexandrov, ‘Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence’, Annu

    T. Alexandrov, ‘Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence’, Annu. Rev. Biomed. Data Sci., vol. 3, no. 1, pp. 61–87, Jul. 2020, doi: 10.1146/annurev-biodatasci-011420-031537

    2020 doi

  34. [40]

    H. Tian, S. Sheraz Née Rabbani, J. C. Vickerman, and N. Winograd, ‘Multiomics Imaging Using High-Energy Water Gas Cluster Ion Beam Secondary Ion Mass Spectrometry [(H 2 O) n -GCIB-SIMS] of Frozen-Hydrated Cells and Tissue’, Anal. Chem., vol. 93, no. 22, pp. 7808–7814, Jun. 202...

    2021 doi

  35. [41]

    S. Lee, H. M. Vu, J.-H. Lee, H. Lim, and M.-S. Kim, ‘Advances in Mass Spectrometry- Based Single Cell Analysis’, Biology, vol. 12, no. 3, p. 395, Mar. 2023, doi: 10.3390/biology12030395

    2023 doi

  36. [42]

    B. Wang, K. Yao, and Z. Hu, ‘Advances in mass spectrometry-based single-cell metabolite analysis’, TrAC Trends Anal. Chem., vol. 163, p. 117075, Jun. 2023, doi: 10.1016/j.trac.2023.117075

    2023

  37. [43]

    Rappez et al., ‘SpaceM reveals metabolic states of single cells’, Nat

    L. Rappez et al., ‘SpaceM reveals metabolic states of single cells’, Nat. Methods, vol. 18, no. 7, pp. 799–805, Jul. 2021, doi: 10.1038/s41592-021-01198-0

    2021 doi

  38. [44]

    Karrobi et al., ‘Fluorescence Lifetime Imaging Microscopy (FLIM) reveals spatial- metabolic changes in 3D breast cancer spheroids’, Sci

    K. Karrobi et al., ‘Fluorescence Lifetime Imaging Microscopy (FLIM) reveals spatial- metabolic changes in 3D breast cancer spheroids’, Sci. Rep., vol. 13, no. 1, p. 3624, Mar. 2023, doi: 10.1038/s41598-023-30403-7

    2023 doi

  39. [45]

    L. M. Dowling et al., ‘Fourier Transform Infrared microspectroscopy identifies single cancer cells in blood. A feasibility study towards liquid biopsy’, PLOS ONE, vol. 18, no. 8, p. e0289824, Aug. 2023, doi: 10.1371/journal.pone.0289824

    2023 doi

  40. [46]

    Yue et al., ‘A guidebook of spatial transcriptomic technologies, data resources and analysis approaches’, Comput

    L. Yue et al., ‘A guidebook of spatial transcriptomic technologies, data resources and analysis approaches’, Comput. Struct. Biotechnol. J., vol. 21, pp. 940–955, 2023, doi: 10.1016/j.csbj.2023.01.016

    2023 doi

  41. [47]

    Ràfols et al., ‘Signal preprocessing, multivariate analysis and software tools for MA(LDI)-TOF mass spectrometry imaging for biological applications’, Mass Spectrom

    P. Ràfols et al., ‘Signal preprocessing, multivariate analysis and software tools for MA(LDI)-TOF mass spectrometry imaging for biological applications’, Mass Spectrom. Rev., vol. 37, no. 3, pp. 281–306, 2018, doi: 10.1002/mas.21527

    2018 doi

  42. [48]

    Baquer, L

    G. Baquer, L. Sementé, T. Mahamdi, X. Correig, P. Ràfols, and M. García-Altares, ‘What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging’, Mass Spectrom. Rev., vol. 42, no. 5, pp. 1927–1...

    1927 doi

  43. [49]

    W. D. Cameron, A. M. Bennett, C. V. Bui, H. H. Chang, and J. V. Rocheleau, ‘Leveraging multimodal microscopy to optimize deep learning models for cell segmentation’, APL Bioeng., vol. 5, no. 1, p. 016101, Jan. 2021, doi: 10.1063/5.0027993

    2021 doi

  44. [50]

    Park et al., ‘Cell segmentation-free inference of cell types from in situ transcriptomics data’, Nat

    J. Park et al., ‘Cell segmentation-free inference of cell types from in situ transcriptomics data’, Nat. Commun., vol. 12, no. 1, p. 3545, Jun. 2021, doi: 10.1038/s41467-021-23807-4

    2021 doi

  45. [51]

    Hao et al., ‘Integrated analysis of multimodal single-cell data’, Cell, vol

    Y. Hao et al., ‘Integrated analysis of multimodal single-cell data’, Cell, vol. 184, no. 13, pp. 3573-3587.e29, Jun. 2021, doi: 10.1016/j.cell.2021.04.048

    2021 doi

  46. [52]

    Palla et al., ‘Squidpy: a scalable framework for spatial omics analysis’, Nat

    G. Palla et al., ‘Squidpy: a scalable framework for spatial omics analysis’, Nat. Methods, vol. 19, no. 2, pp. 171–178, Feb. 2022, doi: 10.1038/s41592-021-01358-2

    2022 doi

  47. [53]

    F. A. Wolf, P. Angerer, and F. J. Theis, ‘SCANPY: Large-scale single-cell gene expression data analysis’, Genome Biol., vol. 19, no. 1, 2018, doi: 10.1186/s13059-017-1382-0

    2018 doi

  48. [54]

    Dries et al., ‘Giotto: a toolbox for integrative analysis and visualization of spatial expression data’, Genome Biol., vol

    R. Dries et al., ‘Giotto: a toolbox for integrative analysis and visualization of spatial expression data’, Genome Biol., vol. 22, no. 1, p. 78, Dec. 2021, doi: 10.1186/s13059-021- 02286-2

    2021 doi

  49. [55]

    Hu et al., ‘Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology’, Nat

    T. Hu et al., ‘Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology’, Nat. Commun., vol. 14, no. 1, p. 8260, Dec. 2023, doi: 10.1038/s41467-023-43917-5

    2023 doi

  50. [57]

    SANTO: A Coarse-to-Fine Alignment and Stitching Method for Spatial Omics,

    H. Li, Y. Lin, W. He, W. Han, X. Xu, C. Xu, E. Gao, H. Zhao, and X. Gao, "SANTO: A Coarse-to-Fine Alignment and Stitching Method for Spatial Omics," Nature Communications, vol. 15, no. 1, p. 6048, 2024, doi: 10.1038/s41467-024-50308-x

    2024 doi

  51. [58]

    Cross- Modality Mapping Using Image Varifolds to Align Tissue-Scale Atlases to Molecular-Scale Measures with Application to 2D Brain Sections,

    K. M. Stouffer, A. Trouvé, L. Younes, M. Kunst, L. Ng, H. Zeng, M. Anant, *et al.*, "Cross- Modality Mapping Using Image Varifolds to Align Tissue-Scale Atlases to Molecular-Scale Measures with Application to 2D Brain Sections," Nature Communications, vol. 15, no. 1, p. 3530, ...

    2024 doi

  52. [60]

    Koutrouli, E

    M. Koutrouli, E. Karatzas, D. Paez-Espino, and G. A. Pavlopoulos, ‘A Guide to Conquer the Biological Network Era Using Graph Theory’, Front. Bioeng. Biotechnol., vol. 8, p. 34, Jan. 2020, doi: 10.3389/fbioe.2020.00034

    2020

  53. [61]

    McInnes, J

    L. McInnes, J. Healy, N. Saul, and L. Großberger, ‘UMAP: Uniform Manifold Approximation and Projection’, J. Open Source Softw., vol. 3, no. 29, p. 861, Sep. 2018, doi: 10.21105/joss.00861

    2018 doi

  54. [62]

    A. K. Smilde, T. Næs, and K. H. Liland, Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences. John Wiley & Sons, 2022

    2022

  55. [63]

    Single-Cell Multi-Omic Integration Compares and Contrasts Features of Brain Cell Identity,

    J. D. Welch, V. Kozareva, A. Ferreira, C. Vanderburg, C. Martin, and E. Z. Macosko, "Single-Cell Multi-Omic Integration Compares and Contrasts Features of Brain Cell Identity," Cell, vol. 177, no. 7, pp. 1873–1887.e17, 2019, doi: 10.1016/j.cell.2019.05.006

    2019 doi

  56. [64]

    Visualizing structure and transitions in high-dimensional biological data,

    K. R. Moon, D. Van Dijk, Z. Wang, S. Gigante, D. B. Burkhardt, W. S. Chen, K. Yim, et al., "Visualizing structure and transitions in high-dimensional biological data," Nature Biotechnology, vol. 37, no. 12, pp. 1482–1492, 2019

    2019

  57. [65]

    Seamless Integration of Image and Molecular Analysis for Spatial Transcriptomics Workflows,

    J. Bergenstråhle, L. Larsson, and J. Lundeberg, "Seamless Integration of Image and Molecular Analysis for Spatial Transcriptomics Workflows," BMC Genomics, vol. 21, no. 1, p. 482, 2020, doi: 10.1186/s12864-020-06832-3

    2020 doi

  58. [66]

    Jolliffe, ‘Principal Component Analysis for Special Types of Data’, in Principal Component Analysis, in Springer Series in Statistics

    I. Jolliffe, ‘Principal Component Analysis for Special Types of Data’, in Principal Component Analysis, in Springer Series in Statistics. , New York: Springer-Verlag, 2002, pp. 338–372. doi: 10.1007/0-387-22440-8_13

    2002 doi

  59. [67]

    D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor, ‘Canonical correlation analysis: An overview with application to learning methods’, Neural Comput., vol. 16, no. 12, pp. 2639– 2664, 2004

    2004

  60. [68]

    S. Wold, M. Sjöström, and L. Eriksson, ‘PLS-regression: a basic tool of chemometrics’, Chemom. Intell. Lab. Syst., vol. 58, no. 2, pp. 109–130, 2001

    2001

  61. [69]

    Barker and W

    M. Barker and W. Rayens, ‘Partial least squares for discrimination’, J. Chemom., vol. 17, no. 3, pp. 166–173, Mar. 2003, doi: 10.1002/cem.785

    2003 doi

  62. [70]

    H. Zou, T. Hastie, and R. Tibshirani, ‘Sparse Principal Component Analysis’, J. Comput. Graph. Stat., vol. 15, no. 2, pp. 265–286, Jun. 2006, doi: 10.1198/106186006X113430

    2006 doi

  63. [71]

    Lê Cao, D

    K.-A. Lê Cao, D. Rossouw, C. Robert-Granié, and P. Besse, ‘A Sparse PLS for Variable Selection when Integrating Omics Data’, Stat. Appl. Genet. Mol. Biol., vol. 7, no. 1, Jan. 2008, doi: 10.2202/1544-6115.1390

    2008

  64. [72]

    S. Park, E. Ceulemans, and K. Van Deun, ‘Sparse common and distinctive covariates regression’, J. Chemom., vol. 35, no. 2, Feb. 2021, doi: 10.1002/cem.3270

    2021 doi

  65. [73]

    Lee and H

    D. Lee and H. S. Seung, ‘Algorithms for non-negative matrix factorization’, Adv. Neural Inf. Process. Syst., vol. 13, 2000, Accessed: Apr. 12, 2024. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2000/hash/f9d1152547c0bde01830b7e8b d60024c-Abstract.html

    2000

  66. [74]

    Borg and P

    I. Borg and P. J. F. Groenen, Modern Multidimensional Scaling: Theory and Applications. Springer Science & Business Media, 2005

    2005

  67. [75]

    van der Maaten and G

    L. van der Maaten and G. Hinton, ‘Visualizing Data using t-SNE’, J. Mach. Learn. Res., vol. 9, no. 86, pp. 2579–2605, 2008

    2008

  68. [76]

    Szymańska, E

    E. Szymańska, E. Saccenti, A. K. Smilde, and J. A. Westerhuis, ‘Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies’, Metabolomics, vol. 8, no. 1, pp. 3–16, Jun. 2012, doi: 10.1007/s11306-011-0330-3

    2012 doi

  69. [77]

    M. A. Myers, S. Zaccaria, and B. J. Raphael, ‘Identifying tumor clones in sparse single-cell mutation data’, Bioinformatics, vol. 36, no. Supplement_1, pp. i186–i193, Jul. 2020, doi: 10.1093/bioinformatics/btaa449

    2020 doi

  70. [78]

    W. S. Torgerson, ‘Multidimensional scaling: I. Theory and method’, Psychometrika, vol. 17, no. 4, pp. 401–419, Dec. 1952, doi: 10.1007/BF02288916

    1952 doi

  71. [79]

    Kobak and P

    D. Kobak and P. Berens, ‘The art of using t-SNE for single-cell transcriptomics’, Nat. Commun., vol. 10, no. 1, p. 5416, Nov. 2019, doi: 10.1038/s41467-019-13056-x

    2019 doi

  72. [80]

    V. H. Do and S. Canzar, ‘A generalization of t-SNE and UMAP to single-cell multimodal omics’, Genome Biol., vol. 22, no. 1, p. 130, Dec. 2021, doi: 10.1186/s13059-021-02356-5

    2021 doi

  73. [81]

    Smets et al., ‘Evaluation of Distance Metrics and Spatial Autocorrelation in Uniform Manifold Approximation and Projection Applied to Mass Spectrometry Imaging Data’, Anal

    T. Smets et al., ‘Evaluation of Distance Metrics and Spatial Autocorrelation in Uniform Manifold Approximation and Projection Applied to Mass Spectrometry Imaging Data’, Anal. Chem., vol. 91, no. 9, pp. 5706–5714, May 2019, doi: 10.1021/acs.analchem.8b05827

    2019 doi

  74. [82]

    Sarycheva et al., ‘Structure-Preserving and Perceptually Consistent Approach for Visualization of Mass Spectrometry Imaging Datasets’, Anal

    A. Sarycheva et al., ‘Structure-Preserving and Perceptually Consistent Approach for Visualization of Mass Spectrometry Imaging Datasets’, Anal. Chem., vol. 93, no. 3, pp. 1677–1685, Jan. 2021, doi: 10.1021/acs.analchem.0c04256

    2021 doi

  75. [83]

    H. Hu, R. Yin, H. M. Brown, and J. Laskin, ‘Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding’, Anal. Chem., vol. 93, no. 7, pp. 3477–3485, Feb. 2021, doi: 10.1021/acs.analchem.0c04798

    2021 doi

  76. [84]

    Shang and X

    L. Shang and X. Zhou, ‘Spatially aware dimension reduction for spatial transcriptomics’, Nat. Commun., vol. 13, no. 1, p. 7203, 2022

    2022

  77. [85]

    F. W. Townes and B. E. Engelhardt, ‘Nonnegative spatial factorization applied to spatial genomics’, Nat. Methods, vol. 20, no. 2, pp. 229–238, Feb. 2023, doi: 10.1038/s41592- 022-01687-w

    2023 doi

  78. [86]

    Chidester, T

    B. Chidester, T. Zhou, S. Alam, and J. Ma, ‘SpiceMix enables integrative single-cell spatial modeling of cell identity’, Nat. Genet., vol. 55, no. 1, pp. 78–88, Jan. 2023, doi: 10.1038/s41588-022-01256-z

    2023 doi

  79. [87]

    Moses et al., ‘Voyager: exploratory single-cell genomics data analysis with geospatial statistics’, bioRxiv, p

    L. Moses et al., ‘Voyager: exploratory single-cell genomics data analysis with geospatial statistics’, bioRxiv, p. 2023.07.20.549945, Aug. 2023, doi: 10.1101/2023.07.20.549945

    2023 doi

  80. [88]

    J. M. Prats-Montalbán, A. De Juan, and A. Ferrer, ‘Multivariate image analysis: A review with applications’, Chemom. Intell. Lab. Syst., vol. 107, no. 1, pp. 1–23, May 2011, doi: 10.1016/j.chemolab.2011.03.002

    2011 doi

  81. [89]

    M. H. Bharati and J. F. MacGregor, ‘Texture analysis of images using principal component analysis’, in Process Imaging for Automatic Control, SPIE, Feb. 2001, pp. 27–37. doi: 10.1117/12.417179

    2001 doi

  82. [90]

    de Juan and R

    A. de Juan and R. Tauler, ‘Multivariate Curve Resolution: 50 years addressing the mixture analysis problem – A review’, Anal. Chim. Acta, vol. 1145, pp. 59–78, Feb. 2021, doi: 10.1016/j.aca.2020.10.051

    2021 doi

  83. [91]

    Ruckebusch and L

    C. Ruckebusch and L. Blanchet, ‘Multivariate curve resolution: A review of advanced and tailored applications and challenges’, Anal. Chim. Acta, vol. 765, pp. 28–36, Feb. 2013, doi: 10.1016/j.aca.2012.12.028

    2013 doi

  84. [92]

    Jaumot and R

    J. Jaumot and R. Tauler, ‘Potential use of multivariate curve resolution for the analysis of mass spectrometry images’, The Analyst, vol. 140, no. 3, pp. 837–846, 2015, doi: 10.1039/C4AN00801D

    2015 doi

  85. [93]

    Hugelier, O

    S. Hugelier, O. Devos, and C. Ruckebusch, ‘On the implementation of spatial constraints in multivariate curve resolution alternating least squares for hyperspectral image analysis’, J. Chemom., vol. 29, no. 10, pp. 557–561, 2015, doi: 10.1002/cem.2742

    2015 doi

  86. [94]

    Firmani, S

    P. Firmani, S. Hugelier, F. Marini, and C. Ruckebusch, ‘MCR-ALS of hyperspectral images with spatio-spectral fuzzy clustering constraint’, Chemom. Intell. Lab. Syst., vol. 179, pp. 85–91, Aug. 2018, doi: 10.1016/j.chemolab.2018.06.007

    2018 doi

  87. [95]

    Vitale, S

    R. Vitale, S. Hugelier, D. Cevoli, and C. Ruckebusch, ‘A spatial constraint to model and extract texture components in Multivariate Curve Resolution of near-infrared hyperspectral images’, Anal. Chim. Acta, vol. 1095, pp. 30–37, Jan. 2020, doi: 10.1016/j.aca.2019.10.028

    2020 doi

  88. [96]

    Li Vigni, J

    M. Li Vigni, J. M. Prats‐Montalban, A. Ferrer, and M. Cocchi, ‘Coupling 2D‐wavelet decomposition and multivariate image analysis (2D WT‐MIA)’, J. Chemom., vol. 32, no. 1, p. e2970, Jan. 2018, doi: 10.1002/cem.2970

    2018 doi

  89. [97]

    S. M. Zandavi et al., ‘Disentangling single-cell omics representation with a power spectral density-based feature extraction’, Nucleic Acids Res., vol. 50, no. 10, pp. 5482–5492, Jun. 2022, doi: 10.1093/nar/gkac436

    2022 doi

  90. [98]

    Y.-L. Gao, Q. Qiao, J. Wang, S.-S. Yuan, and J.-X. Liu, ‘BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data’, IEEE J. Biomed. Health Inform., vol. 27, no. 10, pp. 5187–5198, Oct. 20...

    2023

  91. [99]

    A. A. Stepanchuk and P. K. Stys, ‘Spectral Fluorescence Pathology of Protein Misfolding Disorders’, ACS Chem. Neurosci., vol. 15, no. 5, pp. 898–908, Mar. 2024, doi: 10.1021/acschemneuro.3c00798

    2024 doi

  92. [100]

    Kircheis and D

    M. Kircheis and D. Potts, ‘Direct inversion of the nonequispaced fast Fourier transform’, Linear Algebra Its Appl., vol. 575, pp. 106–140, Aug. 2019, doi: 10.1016/j.laa.2019.03.028

    2019 doi

  93. [101]

    M. S. Armstrong, J. C. Pérez-Girón, J. Camacho, and R. Zamora, ‘A direct solution to the interpolative inverse non-uniform fast Fourier transform problem for spectral analyses of non-equidistant time-series data’. arXiv, Feb. 27, 2024. Accessed: Apr. 12, 2024. [Online]. Availa...

    2024 arXiv

  94. [102]

    Montgomery, Design and Analysis of Experiments

    D. Montgomery, Design and Analysis of Experiments. Wiley, 2020

    2020

  95. [103]

    Permutational multivariate analysis of variance (PERMANOVA),

    M. J. Anderson, "Permutational multivariate analysis of variance (PERMANOVA)," Wiley StatsRef: Statistics Reference Online, pp. 1–15, 2014

    2014

  96. [104]

    Anova-simultaneous component analysis (asca): A new tool for analyzing designed metabolomics data,

    A. K. Smilde, J. J. Jansen, H. C. Hoefsloot, R.-J. A. Lamers, J. Van Der Greef, and M. E. Timmerman, "Anova-simultaneous component analysis (asca): A new tool for analyzing designed metabolomics data," Bioinformatics, vol. 21, no. 13, pp. 3043–3048, 2005

    2005

  97. [105]

    Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA,

    B. J. Kelly, R. Gross, K. Bittinger, S. Sherrill-Mix, J. D. Lewis, R. G. Collman, F. D. Bushman, and H. Li, "Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA," Bioinformatics, vol. 31, no. 15, pp. 2461–2468, Aug. 2015, doi: 10.1093...

    2015 doi

  98. [106]

    Population Power Curves in ASCA With Permutation Testing,

    J. Camacho and M. S. Armstrong, "Population Power Curves in ASCA With Permutation Testing," Journal of Chemometrics, 2024, doi: e3596

    2024

  99. [107]

    H. Chen, D. Li, and Z. Bar-Joseph, ‘SCS: cell segmentation for high-resolution spatial transcriptomics’, Nat. Methods, vol. 20, no. 8, pp. 1237–1243, Aug. 2023, doi: 10.1038/s41592-023-01939-3

    2023 doi

  100. [108]

    J. Xu, D. Huang, and X. Zhang, ‘scmFormer Integrates Large‐Scale Single‐Cell Proteomics and Transcriptomics Data by Multi‐Task Transformer’, Adv. Sci., p. 2307835, Mar. 2024, doi: 10.1002/advs.202307835

    2024 doi

  101. [109]

    Biancalani et al., ‘Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram’, Nat

    T. Biancalani et al., ‘Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram’, Nat. Methods, vol. 18, no. 11, pp. 1352–1362, Nov. 2021, doi: 10.1038/s41592-021-01264-7

    2021 doi

  102. [110]

    Lin, T.-Y

    Y. Lin, T.-Y. Wu, S. Wan, J. Y. H. Yang, W. H. Wong, and Y. X. R. Wang, ‘scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning’, Nat. Biotechnol., vol. 40, no. 5, pp. 703–710, May 2022, doi: 10.1038/s41587-021-01161-6

    2022 doi

  103. [111]

    Hu et al., ‘SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network’, Nat

    J. Hu et al., ‘SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network’, Nat. Methods, vol. 18, no. 11, pp. 1342–1351, Nov. 2021, doi: 10.1038/s41592-021-01255-8

    2021 doi

  104. [112]

    D. Pham et al., ‘stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues’. May 31, 2020. doi: 10.1101/2020.05.31.125658

    2020 doi

  105. [113]

    Zhao et al., ‘Spatial transcriptomics at subspot resolution with BayesSpace’, Nat

    E. Zhao et al., ‘Spatial transcriptomics at subspot resolution with BayesSpace’, Nat. Biotechnol., vol. 39, no. 11, pp. 1375–1384, Nov. 2021, doi: 10.1038/s41587-021-00935-2

    2021 doi

  106. [114]

    Long et al., ‘Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST’, Nat

    Y. Long et al., ‘Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST’, Nat. Commun., vol. 14, no. 1, p. 1155, Mar. 2023, doi: 10.1038/s41467-023-36796-3

    2023 doi

  107. [115]

    Erfanian et al., ‘Deep learning applications in single-cell genomics and transcriptomics data analysis’, Biomed

    N. Erfanian et al., ‘Deep learning applications in single-cell genomics and transcriptomics data analysis’, Biomed. Pharmacother., vol. 165, p. 115077, Sep. 2023, doi: 10.1016/j.biopha.2023.115077

    2023

  108. [116]

    Atta and J

    L. Atta and J. Fan, ‘Computational challenges and opportunities in spatially resolved transcriptomic data analysis’, Nat. Commun., vol. 12, no. 1, p. 5283, Sep. 2021, doi: 10.1038/s41467-021-25557-9

    2021 doi

  109. [117]

    Nikparvar and J.-C

    B. Nikparvar and J.-C. Thill, ‘Machine Learning of Spatial Data’, ISPRS Int. J. Geo-Inf., vol. 10, no. 9, Art. no. 9, Sep. 2021, doi: 10.3390/ijgi10090600

    2021 doi

  110. [118]

    Andersson et al., ‘Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography’, Commun

    A. Andersson et al., ‘Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography’, Commun. Biol., vol. 3, no. 1, p. 565, Oct. 2020, doi: 10.1038/s42003-020-01247-y

    2020 doi

  111. [119]

    Cang et al., ‘Screening cell–cell communication in spatial transcriptomics via collective optimal transport’, Nat

    Z. Cang et al., ‘Screening cell–cell communication in spatial transcriptomics via collective optimal transport’, Nat. Methods, vol. 20, no. 2, pp. 218–228, Feb. 2023, doi: 10.1038/s41592-022-01728-4

    2023 doi

  112. [120]

    Svensson, S

    V. Svensson, S. A. Teichmann, and O. Stegle, ‘SpatialDE: identification of spatially variable genes’, Nat. Methods, vol. 15, no. 5, pp. 343–346, May 2018, doi: 10.1038/nmeth.4636

    2018 doi

  113. [121]

    L. M. Weber, A. Saha, A. Datta, K. D. Hansen, and S. C. Hicks, ‘nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes’, Nat. Commun., vol. 14, no. 1, p. 4059, Jul. 2023, doi: 10.1038/s41467-023-39748-z

    2023 doi

  114. [122]

    B. F. Miller, D. Bambah-Mukku, C. Dulac, X. Zhuang, and J. Fan, ‘Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities’, Genome Res., vol. 31, no. 10, pp. 1843–1855, Jan. 2021, doi: 10.1101...

    2021 doi

  115. [123]

    Singhal et al., ‘BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis’, Nat

    V. Singhal et al., ‘BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis’, Nat. Genet., vol. 56, no. 3, pp. 431–441, Mar. 2024, doi: 10.1038/s41588-024-01664-3

    2024 doi

  116. [124]

    Ilastik: Interactive Machine Learning for (Bio)Image Analysis,

    S. Berg, D. Kutra, T. Kroeger, et al., "Ilastik: Interactive Machine Learning for (Bio)Image Analysis," Nature Methods, vol. 16, pp. 1226–1232, 2019, doi: 10.1038/s41592-019-0582- 9

    2019 doi

  117. [125]

    SM-Omics is an automated platform for high-throughput spatial multi-omics,

    S. Vickovic, B. Lötstedt, J. Klughammer, et al., "SM-Omics is an automated platform for high-throughput spatial multi-omics," Nature Communications, vol. 13, p. 795, 2022, doi: 10.1038/s41467-022-28445-y

    2022 doi

  118. [126]

    Prasad, G

    M. Prasad, G. Postma, P. Franceschi, L. M. C. Buydens, and J. J. Jansen, ‘Evaluation and comparison of unsupervised methods for the extraction of spatial patterns from mass spectrometry imaging data (MSI)’, Sci. Rep., vol. 12, no. 1, p. 15687, Sep. 2022, doi: 10.1038/s41598-02...

    2022 doi

  119. [127]

    D. S. Fischer, A. C. Schaar, and F. J. Theis, ‘Modeling intercellular communication in tissues using spatial graphs of cells’, Nat. Biotechnol., vol. 41, no. 3, pp. 332–336, Mar. 2023, doi: 10.1038/s41587-022-01467-z

    2023 doi

  120. [128]

    Pham et al., ‘Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues’, Nat

    D. Pham et al., ‘Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues’, Nat. Commun., vol. 14, no. 1, p. 7739, Nov. 2023, doi: 10.1038/s41467-023-43120-6

    2023 doi

  121. [129]

    Q. Zhu, S. Shah, R. Dries, L. Cai, and G.-C. Yuan, ‘Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data’, Nat. Biotechnol., vol. 36, no. 12, pp. 1183–1190, Dec. 2018, doi: 10.1038/nbt.4260

    2018 doi

  122. [130]

    Monjo, M

    T. Monjo, M. Koido, S. Nagasawa, Y. Suzuki, and Y. Kamatani, ‘Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation’, Sci. Rep., vol. 12, no. 1, p. 4133, Mar. 2022, doi: 10.1038/s41598- 022-07685-4

    2022 doi

  123. [131]

    Zhao et al., ‘Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression’, Brief

    C. Zhao et al., ‘Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression’, Brief. Bioinform., vol. 25, no. 2, p. bbae052, Mar. 2024, doi: 10.1093/bib/bbae052

    2024 doi

  124. [132]

    X. Xiao, Y. Kong, R. Li, Z. Wang, and H. Lu, ‘Transformer with convolution and graph-node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image’, Med. Image Anal., vol. 91, p. 103040, Jan. 2024, doi: 10.1...

    2024

  125. [133]

    Guo et al., ‘Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action’, 2023, doi: 10.48550/ARXIV.2302.10907

    Z. Guo et al., ‘Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action’, 2023, doi: 10.48550/ARXIV.2302.10907

    2023 doi arXiv

  126. [134]

    Sadria and A

    M. Sadria and A. Layton, ‘The Power of Two: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis’. Apr. 16, 2023. doi: 10.1101/2023.04.13.536789

    2023 doi

Pith tools

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