Pith. sign in

REVIEW 4 major objections 5 minor 44 references

Enhanced Single-Cell RNA-seq Embedding through Gene Expression and Data-Driven Gene-Gene Interaction Integration

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper proposes Dual Aspect Embedding (DAE), which fuses gene expression similarity with random-forest-derived gene-gene interaction information to produce cell embeddings that better identify rare cell types and improve clustering.

desk verdict A plausible embedding pipeline with an overstated biological story and a circular hyperparameter choice; worth refereeing, but the central claim needs an ablation. read the letter →

arxiv 2509.02639 v1 pith:4MIRFIOH submitted 2025-09-01 q-bio.GN cs.AI

Hojjat Torabi Goudarzi , Maziyar Baran Pouyan This is my paper · ORCID
classification q-bio.GNcs.AI
keywords single-cellRNA-seqcellembeddinggene-geneinteractionrandomforestsimilaritycell-leafgraphraretypedetectionlearning
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

The paper is trying to establish that single-cell RNA-seq embeddings work better when they encode not only how much each gene is expressed but also how genes regulate one another. It derives the regulatory view from the expression data itself: random forests trained to predict each gene's expression place cells in leaf nodes, and cells sharing leaves are treated as having similar gene-gene interaction profiles. These leaf relationships are fused with an ordinary nearest-neighbor graph and embedded with LINE. If the claim holds, researchers get a purely data-driven way to sharpen clustering, visualization, and rare-cell detection without needing external interaction databases.

What carries the argument

Enriched Cell-Leaf Graph (ECLG): a fused graph in which each cell is linked, with unit weight, to the random-forest leaf nodes it falls into (the Cell-Leaf Graph, a bipartite graph capturing regulatory similarity), then augmented with expression-based K-nearest-neighbor edges weighted by a Gaussian RBF kernel. LINE embedding on this graph produces cell vectors that preserve both first-order and second-order proximities.

What would settle it

Repeat the Cortex experiment after shuffling cell-to-leaf assignments in the CLG while keeping the graph's degree sequence and the KNNG unchanged; if the Nearest Neighbor Error and microglia/ependymal/mural separation stay as low as the real DAE's, then the leaf-proximity channel carries no unique biological signal.

Watch

Extended reading notes

Core claim

The paper claims that a cell embedding can be improved by fusing two views of the same scRNA-seq data: the usual expression-similarity view and a regulatory view inferred by random forests. For each gene, a random forest is trained to predict its expression from all other genes; cells that land in the same or nearby leaf nodes across the forest are taken to share gene-gene interaction context. The two views are combined into a single graph and embedded with LINE. Across six datasets, this Dual Aspect Embedding (DAE) reports lower nearest-neighbor error than PCA, SVD, kPCA, t-SNE, SIMLR, scVI, and RAFSIL, and it keeps rare cell types (microglia, ependymal, mural) visually separated in the mou

Load-bearing premise

That leaf co-assignment in random forests trained to predict each gene from the others captures gene-gene interaction structure that is genuinely distinct from the expression profiles themselves, rather than a redundant re-encoding of the same expression similarities.

Editorial extensions

If this is right

  • On six benchmark datasets, DAE achieves lower nearest-neighbor error than expression-only and deep-learning baselines, in both direct embedding and t-SNE visualization settings.
  • Rare cell populations — microglia, ependymal, and mural in mouse cortex — are separated more cleanly in DAE's embedding, which the paper attributes to preserved regulatory interactions.
  • Clustering algorithms applied on top of DAE embeddings give higher adjusted Rand index and normalized mutual information than on PCA/SVD/kPCA embeddings for most clustering methods tested.
  • The method requires no external gene-interaction database: the interaction layer is inferred from the same expression matrix, and performance plateaus with about 200 trees per forest.
  • The feature-cluster variant makes the approach runnable on a standard personal computer, with random-forest construction being parallelizable.

Reading between the lines

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

  • A testable consequence the paper does not report: removing the KNNG edges should degrade performance mostly for abundant cell types, while removing the CLG edges should degrade performance mostly for rare types; that asymmetry would confirm the two channels carry different information.
  • The same leaf-co-assignment construction could be plugged into other graph-based single-cell tools, for example as an additional similarity kernel, without retraining the rest of the pipeline.
  • Because the method learns interactions from the data, it could be extended to multi-omics settings by building leaf graphs on each modality; the paper lists this only as a future direction.
  • The reported rare-cell separation in Cortex is demonstrated visually rather than by a quantitative recall or precision metric, so a quantitative rare-cell detection benchmark would be a natural next check.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The manuscript proposes Dual Aspect Embedding (DAE), a method for scRNA-seq cell embedding that combines a K-nearest neighbor graph (KNNG) built from gene expression profiles with a Cell-Leaf Graph (CLG) constructed from random-forest leaf co-assignment, then embeds the fused Enriched Cell-Leaf Graph (ECLG) using the LINE algorithm. DAE is evaluated on six datasets against PCA, SVD, kPCA, t-SNE, SIMLR, scVI, RAFSIL, Seurat, and Monocle, using nearest-neighbor error (NNE), clustering ARI/NMI, visualization, sensitivity analyses, and runtime. The paper claims improved rare-cell identification, clustering, and trajectory inference, with the main technical novelty attributed to the CLG's capture of gene-gene interactions.

Significance. If the CLG genuinely adds information beyond expression-based similarities, the idea of integrating tree-based leaf co-assignment with a KNNG for cell embedding is a useful and potentially generalizable contribution. The paper includes a reasonably broad six-dataset benchmark and a runtime analysis on a 76,899-cell dataset, which are strengths. However, the current evidence does not establish the core claim: the reported NNE improvements rely on tuning the RBF bandwidth sigma on the same test datasets, no ablation isolates the CLG's contribution, and the abstract's trajectory-inference claim is not supported by any trajectory experiment. The central idea is plausible, but the evaluation needs substantial additional work.

major comments (4)
  1. [Section 2.2, Eq. (1)] The RBF bandwidth sigma is set to 0.3 by "minimizing the Nearest Neighbor Error (NNE) across our experimental datasets," and Tables 2 and 3 then report NNE on those same datasets. This is test-set tuning: the reported NNE values are not independent estimates and are optimistically biased relative to the baselines, whose parameters are not tuned in the same way. Please use a separate calibration set or nested cross-validation for sigma selection, report the selected sigma per dataset, and include uncertainty estimates.
  2. [Sections 2.1 and 2.3] The central claim is that the CLG contributes gene-gene interaction information beyond expression profiles, but no experiment isolates the CLG's contribution. Random-forest leaf co-assignment is a deterministic function of the same expression matrix used for the KNNG, and the paper itself states in Section 2.1 that "the final feature importance ranking and regulatory network extraction is not performed in this study." Without ablations comparing DAE to (i) KNNG alone embedded with LINE, (ii) CLG alone, and (iii) ECLG with permuted leaf assignments or shuffled interaction scores, the improved NNE/ARI and rare-cell separation in Tables 2-3 and Figures 3-5 could be due entirely to LINE on the KNNG or other implementation choices. This ablation is necessary to substantiate the biological interpretation that gene-gene interactions drive the improvement.
  3. [Abstract and Section 3] The abstract and conclusion state that DAE improves "downstream analyses such as visualization, clustering, and trajectory inference," but Section 3 contains no trajectory inference experiments. The evaluation covers similarity learning (Section 3.1), visualization (Section 3.2), and clustering (Section 3.3), with no trajectory reconstruction or ordering metrics. Either add trajectory-reconstruction experiments on suitable datasets or remove the trajectory-inference claim from the abstract and conclusion.
  4. [Section 3.2] The reported rare-cell percentages for the Cortex dataset (microglia 0.03%, ependymal 0.008%, mural 0.02%) are inconsistent with the dataset size of 3,005 cells; 0.008% would correspond to about 0.24 cells. These percentages appear to be off by one or two orders of magnitude. Please provide exact cell counts and correct the percentages, since the quantitative rare-cell-detection claim and the interpretation of Figure 3 depend on them.
minor comments (5)
  1. [Section 3.2 and Table 3] Seurat and Monocle are evaluated with UMAP while the other methods are evaluated with t-SNE applied to their embeddings. This conflates the embedding method with the visualization algorithm. Please use the same visualization algorithm for all methods, or clearly justify why UMAP is only used for these two baselines.
  2. [Tables 2 and 3] No error bars, standard deviations, or confidence intervals are reported for the main NNE values. Given the stochasticity in random forests and LINE, please report repeated-run variability or at least summarize the seed-to-seed variation mentioned in Section 2.5.
  3. [Section 3.3] Kmeans++ is given the correct number of clusters, while SC3, Phenograph, Seurat, and Monocle are unsupervised with respect to cluster count. Comparisons of ARI/NMI across clustering methods with different amounts of oracle information should be interpreted cautiously and this limitation should be stated.
  4. [Figure 4 and Section 2.1] Figure 4 reports "gene-gene interaction scores" for rare cell types, but Section 2.1 states that feature-importance ranking and regulatory-network extraction are not performed in this study. Please clarify how these interaction scores were computed and whether they come from the same CLG used in the embedding or from a separate post-hoc analysis.
  5. [General] Several presentation issues need correction: "Monoloce" in the Figure 6 caption, "Campbel" in Table 1, and duplicated/malformed text at the end of reference list entry [41]. Also, the paper does not provide code or a data-availability statement; please add one.

Circularity Check

1 steps flagged · score 6.0 of 10

Reported NNE improvements are partly fitted: the RBF bandwidth sigma is chosen by minimizing NNE on the same benchmark datasets whose NNE values are then reported as DAE's performance.

  1. fitted input called prediction [Section 2.2 (KNNG extraction), Eq. (1); reported in Section 3.1, Tables 2 and 3]
    "In our implementation, we setσ = 0.3 to optimize the trade-off between local and global structure preservation. We determined this value by minimizing the Nearest Neighbor Error (NNE) across our experimental datasets."

    The kernel bandwidth sigma controls the KNNG edge weights and hence the embedding learned by LINE. The paper selects sigma by minimizing the same NNE metric on the same six datasets that are later used to report DAE's NNE in Tables 2 and 3. Therefore the reported 'predictive' quality of the embedding is, at least in part, a training-set fit: the evaluation metric was optimized with respect to this hyperparameter on the very benchmarks being compared. Baseline methods are not reported to receive equivalent metric-driven tuning, so the comparison is not fully out-of-sample. This does not make the core embedding construction itself circular, but it makes the headline performance improvements partially forced by the evaluation loop.

full rationale

The core DAE pipeline is not circular in its derivation: it takes the expression matrix X, builds a Cell-Leaf Graph from random-forest leaf co-assignment, builds a KNNG from expression similarities, merges them into the ECLG, and applies LINE to obtain cell embeddings. Each step is a deterministic, stated function of X and the chosen hyperparameters, so the embedding method itself is self-contained rather than equivalent to its inputs by construction. The main circularity is evaluation-side and specific: the RBF bandwidth sigma in Eq. (1) is explicitly chosen by minimizing the Nearest Neighbor Error on the same experimental datasets whose NNE values are then reported as DAE's performance in Tables 2 and 3. That is a fitted hyperparameter being presented as part of an independent comparison, and it partially accounts for the reported NNE advantage. I do not count the use of [13] (Pouyan and Kostka) as load-bearing circularity: it is a self-citation used for a feature-construction module and for a baseline method, not for a uniqueness theorem or for the central claim. The paper's biological interpretation of the CLG as capturing gene-gene interactions is less well supported: Section 2.1 admits that 'the final feature importance ranking and regulatory network extraction is not performed in this study,' while also asserting that the interactions 'represent distinct biological insights' despite being 'inferred from the same gene expression data.' This is a missing-ablation / unsupported-interpretation concern rather than a demonstrated circular reduction, so it does not increase the circularity score beyond the sigma-tuning issue. On balance, the reported improvements are partially fitted, giving a circularity score of 6.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central method relies on the assumption that leaf co-assignment in expression-trained random forests captures biologically meaningful gene-gene interaction signal beyond raw expression, and on the accuracy of cell type labels used as ground truth. The evaluation also depends on the selection of the RBF bandwidth sigma on the test datasets, which is a free parameter rather than an assumption.

free parameters (6)
  • RBF kernel bandwidth sigma = 0.3
    Chosen by minimizing NNE on the same six datasets used for evaluation (Section 2.2).
  • Number of trees per forest = 200
    Set based on NNE plateau on the same datasets (Section 3.4).
  • Embedding dimension = 100
    Empirically set (Section 3).
  • Number of highly variable genes = 2000
    Top 2000 genes by variance, a standard but data-dependent choice (Section 2).
  • K in KNNG
    K value not stated in text; required for KNNG construction (Section 2.2).
  • Minimum leaf size = 10
    Set to capture rare populations (Section 2.1).
assumptions (4)
  • domain assumption Random forest leaf co-assignment reflects gene-gene interaction similarity
    The paper argues cells in shared leaves have similar gene-gene interactions, but the network itself is never extracted (Section 2.1).
  • domain assumption Cell type labels are accurate gold standards
    Evaluation treats annotated labels as ground truth (Section 3).
  • standard math LINE preserves relevant cell-cell structure in the fused graph
    Standard graph embedding assumption from the LINE paper (Section 2.3).
  • ad hoc to paper The gene-cluster feature approximation reduces to the full method
    Approximation claimed without close validation (Section 2.4).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Enhanced Single-Cell RNA-seq Embedding through Gene Expression and Data-Driven Gene-Gene Interaction Integration." pith.science (2026). https://pith.science/paper/4MIRFIOH

@misc{pith2026250902639,
  author       = {Pith},
  title        = {Pith review of: Enhanced Single-Cell RNA-seq Embedding through Gene Expression and Data-Driven Gene-Gene Interaction Integration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4MIRFIOH}},
  note         = {Machine review of arXiv:2509.02639}
}
read the original abstract

Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity, enabling detailed analysis of complex biological systems at single-cell resolution. However, the high dimensionality and technical noise inherent in scRNA-seq data pose significant analytical challenges. While current embedding methods focus primarily on gene expression levels, they often overlook crucial gene-gene interactions that govern cellular identity and function. To address this limitation, we present a novel embedding approach that integrates both gene expression profiles and data-driven gene-gene interactions. Our method first constructs a Cell-Leaf Graph (CLG) using random forest models to capture regulatory relationships between genes, while simultaneously building a K-Nearest Neighbor Graph (KNNG) to represent expression similarities between cells. These graphs are then combined into an Enriched Cell-Leaf Graph (ECLG), which serves as input for a graph neural network to compute cell embeddings. By incorporating both expression levels and gene-gene interactions, our approach provides a more comprehensive representation of cellular states. Extensive evaluation across multiple datasets demonstrates that our method enhances the detection of rare cell populations and improves downstream analyses such as visualization, clustering, and trajectory inference. This integrated approach represents a significant advance in single-cell data analysis, offering a more complete framework for understanding cellular diversity and dynamics.

Figures

Figures reproduced from arXiv: 2509.02639 by the authors.

Figure 1
Figure 1. Enhanced Single-Cell RNA-Seq Embedding Workflow. [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Overview of DAE. DAE takes a gene-expression matrix as its input and learns a [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. The figure presents 2D visualization plots for various embedding methods, where [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Significant gene-gene interaction scores for 3 rare cell types in mouse cortex and [PITH_FULL_IMAGE:figures/full_fig_p020_4.png]
Figure 5
Figure 5. Figure 5: DAE enhances the performance of single-cell clustering methods. Each sub [PITH_FULL_IMAGE:figures/full_fig_p022_5.png]
Figure 6
Figure 6. Figure 6: DAE enhances the performance of single-cell clustering methods. Each sub [PITH_FULL_IMAGE:figures/full_fig_p023_6.png]
Figure 7
Figure 7. Figure 7: NNE values when different number of high variable genes is used for DAE [PITH_FULL_IMAGE:figures/full_fig_p024_7.png]
Figure 8
Figure 8. Figure 8: Sensitivity analysis of DAE when various number of trees are used to generated [PITH_FULL_IMAGE:figures/full_fig_p025_8.png]
Figure 9
Figure 9. Figure 9: Runtime Analysis 26 [PITH_FULL_IMAGE:figures/full_fig_p026_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

44 extracted references · 40 canonical work pages

  1. [1]

    Single-Cell RNA Sequencing Tech- nology Landscape in 2023

    Qu HQ, Kao C, Hakonarson H. Single-Cell RNA Sequencing Tech- nology Landscape in 2023. Stem Cells. 2024 Jan 13;42(1):1-12. doi: 10.1093/stmcls/sxad077. PMID: 37934608

    work page doi:10.1093/stmcls/sxad077 2023

  2. [2]

    Dragomirka Jovic, Xue Liang, Hua Zeng, Lin Lin, Fengping Xu, and Yonglun Luo. 2022. Single-cell RNA sequencing technologies and ap- plications: A brief overview. Clinical and Translational Medicine 12, 3 (2022), e694

    work page 2022

  3. [3]

    Xiangjie Li, Kui Wang, Yafei Lyu, Huize Pan, Jingxiao Zhang, Dwight Stambolian, Katalin Susztak, Muredach P Reilly, Gang Hu, and Mingyao Li. 2020. Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis. Nature communications 11, 1 (2020), 2338

    work page 2020

  4. [4]

    Van de Sande, B., Lee, J.S., Mutasa-Gottgens, E. et al. Applications of single-cell RNA sequencing in drug discovery and development. Nat Rev Drug Discov 22, 496–520 (2023). https://doi.org/10.1038/s41573- 023-00688-4

    doi:10.1038/s41573- 2023

  5. [5]

    Hervé AbdiLynne and J Williams. 2010. Principal component analysis Wiley Interdisciplinary Reviews. Computational Statistics 2, 4 (2010), 433–459

    work page 2010

  6. [6]

    Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, 11 (2008)

    2008

  7. [7]

    Leland McInnes, John Healy, and James Melville. 2018. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018). 28

    arXiv 2018

  8. [8]

    Bo Wang, Junjie Zhu, Emma Pierson, Daniele Ramazzotti, and Ser- afim Batzoglou. 2017. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nature methods 14, 4 (2017), 414–416

    work page 2017

Show all 44 references
  1. [9]

    Romain Lopez, Jeffrey Regier, Michael B Cole, Michael I Jordan, and Nir Yosef. 2018. Deep generative modeling for single-cell transcriptomic. Nature methods 15, 12 (2018), 1053–1058

    2018

  2. [10]

    Andrew Butler, Paul Hoffman, Peter Smibert, Efthymia Papalexi, and Rahul Satija. 2018. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature biotechnology 36, 5 (2018), 411–420

    2018

  3. [11]

    Waqas Haider Khan Bangyal, Rukhma Qasim, Najeeb Ur Rehman, Zeeshan Ahmad, Hafsa Shareef Dar, Laiqa Rukhsar, Zahra Aman, and Jamil Ahmad. 2021. Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches. Computational and Mathematical Methods in Medic...

    2021

  4. [12]

    Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach

    Bangyal, W.H.; Rehman, N.U.; Nawaz, A.; Nisar, K.; Ibrahim, A.A.A.; Shakir, R.; Rawat, D.B. Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach. Electronics 2022, 11,

    2022

  5. [13]

    Maziyar Baran Pouyan and Dennis Kostka. 2018. Random forest based similarity learning for single cell RNA sequencing data. Bioinformatics 34, 13 (2018), i79–i88

    2018

  6. [14]

    Kenji Kamimoto, Blerta Stringa, Christy M Hoffmann, Kunal Jindal, Lilianna Solnica-Krezel, and Samantha A Morris. 2023. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 7949 (2023), 742–751

    2023

  7. [15]

    Forward only counter propagation network for balance scale weight and distance classification task,

    W. H. Bangyal, J. Ahmad, I. Shafi and Q. Abbas, "Forward only counter propagation network for balance scale weight and distance classification task," 2011 Third World Congress on Na- ture and Biologically Inspired Computing, Salamanca, Spain, 2011, 29 pp. 342-347, doi: 10.1109...

    2011

  8. [16]

    Vân Anh Huynh-Thu, Alexandre Irrthum, Louis Wehenkel, and Pierre Geurts. 2010. Inferring regulatory networks from expression data using tree-based methods. PloS one 5, 9 (2010), e12776

    2010

  9. [17]

    Aditya Pratapa, Amogh P Jalihal, Jeffrey N Law, Aditya Bharadwaj, and TM Murali. 2020. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature methods 17, 2 (2020), 147–154

    2020

  10. [18]

    Brief Bioinform

    WeiQ,IslamMT,ZhouY,XingL.Self-superviseddeeplearningofgene- gene interactions for improved gene expression recovery. Brief Bioinform. 2024Jan22;25(2):bbae031.doi: 10.1093/bib/bbae031.PMID:38349062; PMCID: PMC10939378

    doi

  11. [19]

    G3DC: a Gene-Graph-Guided se- lective Deep Clustering method for single cell RNA-seq data

    Shuqing He, Jicong Fan, Tianwei Yu. G3DC: a Gene-Graph-Guided se- lective Deep Clustering method for single cell RNA-seq data. bioRxiv 2023.01.15.524109; doi: https://doi.org/10.1101/2023.01.15.524109

    2023 doi

  12. [21]

    Dayu Hu, Renxiang Guan, Ke Liang, Hao Yu, Hao Quan, Yawei Zhao, Xinwang Liu, Kunlun He, scEGG: an exogenous gene- guided clustering method for single-cell transcriptomic data, Brief- ings in Bioinformatics, Volume 25, Issue 6, November 2024, bbae483, https://doi.org/10.1093/bi...

    2024 doi

  13. [22]

    Leo Breiman. 2001. Random forests. Machine learning 45 (2001), 5–32

    2001

  14. [23]

    David Eppstein, Michael S Paterson, and F Frances Yao. 1997. On nearest-neighbor graphs. Discrete & Computational Geometry 17 (1997), 263–282

    1997

  15. [24]

    Douglas Brent West et al. 2001. Introduction to graph theory. Vol. 2. Prentice hall Upper Saddle River. 30

    2001

  16. [25]

    Jacob H Levine, Erin F Simonds, Sean C Bendall, Kara L Davis, D Amir El-ad, Michelle D Tadmor, Oren Litvin, Harris G Fienberg, Astraea Jager, Eli R Zunder, et al. 2015. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 1...

    2015

  17. [26]

    Christopher M Bishop and Nasser M Nasrabadi. 2006. Pattern recogni- tion and machine learning. Vol. 4. Springer

    2006

  18. [27]

    Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang. 2018. Net- work representation learning: A survey. IEEE transactions on Big Data 6, 1 (2018), 3–28

    2018

  19. [28]

    Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. In Proceedings of the 24th international conference on world wide web. 1067–1077

    2015

  20. [29]

    DmitryUsoskin, AlessandroFurlan, SaifulIslam, HindAbdo, PeterLön- nerberg, Daohua Lou, Jens Hjerling-Leffler, Jesper Haeggström, Olga Kharchenko, Peter V Kharchenko, et al. 2015. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nature ...

    2015

  21. [30]

    Amit Zeisel, Ana B Muñoz-Manchado, Simone Codeluppi, Peter Lön- nerberg, Gioele La Manno, Anna Juréus, Sueli Marques, Hermany Munguba, Liqun He, Christer Betsholtz, et al . 2015. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 6226 ...

    2015

  22. [31]

    Evan Z Macosko, Anindita Basu, Rahul Satija, James Nemesh, Karthik Shekhar, Melissa Goldman, Itay Tirosh, Allison R Bialas, Nolan Kami- taki, Emily M Martersteck, et al. 2015. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 1...

    2015

  23. [32]

    Renchao Chen, Xiaoji Wu, Lan Jiang, and Yi Zhang. 2017. Single-cell RNA-seq reveals hypothalamic cell diversity. Cell reports 18, 13 (2017), 3227–3241. 31

    2017

  24. [33]

    John N Campbell, Evan Z Macosko, Henning Fenselau, Tune H Pers, Anna Lyubetskaya, Danielle Tenen, Melissa Goldman, Anne MJ Verste- gen, Jon M Resch, Steven A McCarroll, et al. 2017. A molecular census of arcuate hypothalamus and median eminence cell types. Nature neu- roscienc...

    2017

  25. [34]

    Maria Brbić, Marinka Zitnik, Sheng Wang, Angela O Pisco, Russ B Altman, Spyros Darmanis, and Jure Leskovec. 2020. Discovering novel cell types across heterogeneous single-cell experiments. BioRxiv (2020), 2020–02

    2020

  26. [35]

    Robert Thorndike. 1953. Who belongs in the family? Psychometrika 18, 4 (1953), 267–276

    1953

  27. [36]

    David Arthur and Sergei Vassilvitskii. 2007. K-means++ the advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. 1027–1035

    2007

  28. [37]

    Yuhan Hao, Stephanie Hao, Erica Andersen-Nissen, William M Mauck, Shiwei Zheng, Andrew Butler, Maddie J Lee, Aaron J Wilk, Charlotte Darby, Michael Zager, et al. 2021. Integrated analysis of multimodal single-cell data. Cell 184, 13 (2021), 3573–3587

    2021

  29. [38]

    Xiaojie Qiu, Qi Mao, Ying Tang, Li Wang, Raghav Chawla, Hannah A Pliner, and Cole Trapnell. 2017. Reversed graph embedding resolves complex single-cell trajectories. Nature methods 14, 10 (2017), 979–982

    2017

  30. [39]

    Michael Hahsler, Matthew Piekenbrock, and Derek Doran. 2019. dbscan: Fast density-based clustering with R. Journal of Statistical Software 91 (2019), 1–30

    2019

  31. [40]

    Vladimir Yu Kiselev, Kristina Kirschner, Michael T Schaub, Tallulah Andrews, Andrew Yiu, Tamir Chandra, Kedar N Natarajan, Wolf Reik, Mauricio Barahona, Anthony R Green, et al. 2017. SC3: consensus clustering of single-cell RNA-seq data. Nature methods 14, 5 (2017), 483–486

    2017

  32. [41]

    Pavel Berkhin. 2006. A survey of clustering data mining tech- niques. Grouping multidimensional data: Recent advances in clus- tering (2006), 25–71. features. Nat Methods 21, 1003–1013 (2024). https://doi.org/10.1038/s41592-023-01899-8 32

    2006 doi

  33. [42]

    Andy Liaw and Matthew Wiener. 2015. randomForest: Breiman and Cutler’s random forests for classification and regression. R package ver- sion 4 (2015), 14

    2015

  34. [43]

    A Beygelzimer, S Kakadet, J Langford, S Arya, D Mount, and S Li

  35. [1890]

    https://doi.org/10.3390/electronics11121890

    doi

  36. [2019]

    R package version 1.1

    FNN: Fast nearest neighbor search algorithms and applications. R package version 1.1. 3. 2019. 33

    2019

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.