# Multiscale Methods for Signal Selection in Single-Cell Data

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Preliminaries

**Definition**

**1.**

**Definition**

**2.**

**Definition**

**3.**

#### 2.2. Eigenscores

**Definition**

**4**

#### 2.2.1. The 0th Eigenscore

#### 2.2.2. Eigenscores to Visualise Graph Signals

#### 2.3. Multiscale Laplacian Score

#### 2.3.1. Random Walks on Graphs

**Remark**

**1.**

#### 2.3.2. Community Detection

**Definition**

**5.**

**Definition**

**6.**

#### 2.3.3. Signal Scores at Multiple Resolutions

**Definition**

**7.**

#### 2.3.4. MLS Analysis Pipeline

#### 2.4. Persistent Rayleigh Quotient

**Definition**

**8**

**.**A filtration of a graph G is a integer-valued function $f:V\to \mathbb{Z}$ on the nodes of G. For $i\in \mathbb{Z}$ the sub-level set $\alpha \left(i\right)$ of f at i is the set

#### 2.4.1. Persistent Laplacian

**Definition**

**9**

**.**The Kron reduction (or 0-degree persistent Laplacian) of L with respect to α is the matrix

**Lemma**

**1**

- 1.
- ${L}_{\alpha}$ is well-defined as $L[{\alpha}^{c},{\alpha}^{c}]$ is invertible.
- 2.
- ${L}_{\alpha}$ is symmetric.
- 3.
- ${L}_{\alpha}\mathbf{1}=\mathbf{0}$, where $\mathbf{1}$ is the column vector of ones.

**Definition**

**10**

#### 2.4.2. Application to Cell Bifurcation

#### 2.5. Data Sets

#### 2.5.1. Preprocessing of PBMC and T Cell Data Sets

`R`-library Seurat [1]. The VST returns the 3000 genes with the highest dispersion in each data set, and it is then reduced to its 30 principal components with the highest variance, following the recommendation given in the manual of Seurat. We then construct a k-nearest neighbour (k-nn) graph on cells for both data sets, using $k=15$, and weight the edges of these graphs according to the weights given by the dimension reduction algorithm UMAP [3]. We note that the method of eigenscores is particularly robust to changes in the value of k as well as leaving out preprocessing with PCA (results not shown). We use cosine-dissimilarity for the PBMC data (following the Seurat tutorials) and Pearson correlation-distance for the T cell data (following [15]). We sample 3000 cells at random between the PCA and k-nn graph steps in the T cell data set (following [15]).

**Remark**

**2.**

#### 2.5.2. Preprocessing of Mouse Foetal Liver Cell Data Set

#### 2.5.3. Previous Results on PBMC Data

#### 2.5.4. Previous Results on T Cell Data

#### 2.5.5. Previous Results on Mouse Foetal Liver Cell Data Set

#### 2.6. Code Availability

## 3. Results

#### 3.1. Eigenscores

#### 3.1.1. The Geometry of PBMC Genes via Eigenscores

#### 3.1.2. Eigenscores for Analysing Data with Continuous Structure: T Cells

#### 3.2. Multiscale Laplacian Score

#### 3.2.1. Multiscale Laplacian Score of PBMC Data

#### 3.2.2. Multiscale Laplacian Score of T Cell Data

#### 3.3. Persistent Rayleigh Quotient

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Figure A1.**Seurat clusters on PBMC data [34] from Seurat VST Vignette [36] numbered according to the vignette and interpretations of overarching cell types inferred from previous results. The cells in this data set divide into broad clusters corresponding to the cell types found in peripheral blood mononuclear cells: lymphocytes (T cells, NK cells, B cells), monocytes, and dendritic cells, as also platelets which are not mononuclear but are found in this specific data set. The DGE analysis from Seurat (non-parametric Wilcoxon rank sum test [41]) defines twelve smaller clusters, in particular sublcustering T cells, NK cells and monocytes, and searches only for differentially expressed genes on these subclusters.

**Figure A2.**Eigenscore ranks for PBMC data [34]. On the top row are plots of the Laplacian eigenvectors, coloured by sign (red positive, purple negative). For each eigenvector ${e}_{i}$, genes are listed with the highest alignment ($ei{g}_{i}^{+}$) and highest anti-alignment ($ei{g}_{i}^{-}$) with ${e}_{i}$. Below the table are a selection of genes ranked highly by eigenscores. For example gene FTL shown below the table is strongly expressed on the monocyte cluster on the right, which is purple (negative) for both ${e}_{1}$ and ${e}_{2}$, hence FTL has high scores on $ei{g}_{1}^{-}$ and $ei{g}_{2}^{-}$.

0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

0 | RPS27 | LTB | S100A8 | CD79A | IFITM3 | GZMK | GZMB | GZMH | CD8B | FCER1A | IFIT1 | GP9 |

1 | RPL32 | IL32 | LGALS2 | MS4A1 | RP11-290F20.3 | CCL5 | FGFBP2 | CST7 | RP11-291B21.2 | ENHO | IFIT3 | ITGA2B |

2 | RPS6 | IL7R | S100A9 | TCL1A | LST1 | NKG7 | SPON2 | NKG7 | CD8A | CLEC10A | RTP4 | TMEM40 |

3 | RPS12 | CD3D | CD14 | CD79B | AIF1 | LYAR | GNLY | CCL5 | S100B | SERPINF1 | SPATS2L | AP001189.4 |

4 | RPL31 | AQP3 | FCN1 | HLA-DQA1 | MS4A7 | GZMA | PRF1 | GZMA | CARS | CD1C | DDX58 | LY6G6F |

5 | RPS14 | LDHB | TYROBP | LINC00926 | IFI30 | IL32 | XCL2 | FGFBP2 | RPS12 | CACNA2D3 | RSAD2 | sep-05 |

6 | RPS25 | CD2 | MS4A6A | VPREB3 | CD68 | CD8A | AKR1C3 | CD8A | RPL13 | HLA-DQB2 | MX1 | HGD |

7 | LDHB | CD40LG | LYZ | HLA-DQB1 | FCER1G | CTSW | CLIC3 | GZMB | RPS6 | HLA-DQA2 | ISG15 | PTCRA |

8 | RPS3A | TPT1 | GPX1 | CD74 | CFD | CST7 | KLRD1 | CTSW | CCR7 | HLA-DQA1 | IFI6 | TREML1 |

9 | RPL30 | CD3E | CST3 | HLA-DRA | SERPINA1 | HOPX | CST7 | CCL4 | RPL32 | NDRG2 | HERC5 | ITGB3 |

**Figure A4.**A weighted graph constructed from mouse foetal liver cells sampled from days 10–17 during development. Parent cell type hepatoblasts differentiate into two daughter cell types, cholangiocytes and hepatocytes.

## References

- Hao, Y.; Hao, S.; Andersen-Nissen, E.; Mauck, W.M., III; Zheng, S.; Butler, A.; Lee, M.J.; Wilk, A.J.; Darby, C.; Zagar, M.; et al. Integrated analysis of multimodal single-cell data. Cell
**2021**, 184, 3573–3587. [Google Scholar] [CrossRef] [PubMed] - Wolf, F.A.; Angerer, P.; Theis, F.J. SCANPY: Large-scale single-cell gene expression data analysis. Genome Biol.
**2018**, 19, 1–5. [Google Scholar] [CrossRef] - McInnes, L.; Healy, J.; Saul, N.; Großberger, L. UMAP: Uniform Manifold Approximation and Projection. J. Open Source Softw.
**2018**, 3, 861. [Google Scholar] [CrossRef] - Becht, E.; McInnes, L.; Healy, J.; Dutertre, C.A.; Kwok, I.W.; Ng, L.G.; Ginhoux, F.; Newell, E.W. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol.
**2019**, 37, 38–44. [Google Scholar] [CrossRef] [PubMed] - Jeitziner, R.; Carrière, M.; Rougemont, J.; Oudot, S.; Hess, K.; Brisken, C. Two-tier mapper: A user-independent clustering method for global gene expression analysis based on topology. arXiv
**2017**, arXiv:1801.01841. [Google Scholar] - Rizvi, A.H.; Camara, P.G.; Kandror, E.K.; Roberts, T.J.; Schieren, I.; Maniatis, T.; Rabadan, R. Single-Cell Topological RNA-Seq Analysis Reveals Insights into Cellular Differentiation and Development. Nat. Biotechnol.
**2017**, 35, 551–560. [Google Scholar] [CrossRef] - Kuchroo, M.; DiStasio, M.; Calapkulu, E.; Ige, M.; Zhang, L.; Sheth, A.H.; Menon, M.; Xing, Y.; Gigante, S.; Huang, J.; et al. Topological Analysis of Single-Cell Data Reveals Shared Glial Landscape of Macular Degeneration and Neurodegenerative Diseases. bioRxiv
**2012**. [Google Scholar] [CrossRef] - Vandaele, R.; Rieck, B.; Saeys, Y.; De Bie, T. Stable Topological Signatures for Metric Trees through Graph Approximations. Pattern Recog. Lett.
**2021**, 147, 85–92. [Google Scholar] [CrossRef] - Ortega, A.; Frossard, P.; Kovačević, J.; Moura, J.M.; Vandergheynst, P. Graph signal processing: Overview, challenges, and applications. Proc. IEEE
**2018**, 106, 808–828. [Google Scholar] [CrossRef] - Chung, F.R. Spectral Graph Theory; Number 92; American Mathematical Soc.: Providence, RI, USA, 1997. [Google Scholar]
- Robinson, M. Topological Signal Processing; Springer: Berlin/Heidelberg, Germany, 2014; Volume 8. [Google Scholar]
- Schaub, M.T.; Zhu, Y.; Seby, J.B.; Roddenberry, T.M.; Segarra, S. Signal processing on higher-order networks: Livin’on the edge... and beyond. Signal Process.
**2021**, 187, 108149. [Google Scholar] [CrossRef] - Barbarossa, S.; Sardellitti, S. Topological signal processing over simplicial complexes. IEEE Trans. Signal Process.
**2020**, 68, 2992–3007. [Google Scholar] [CrossRef] - He, X.; Cai, D.; Niyogi, P. Laplacian score for feature selection. Adv. Neural Inf. Process. Syst.
**2005**, 18, 1–8. [Google Scholar] - Govek, K.W.; Yamajala, V.S.; Camara, P.G. Clustering-Independent Analysis of Genomic Data Using Spectral Simplicial Theory. PLoS Comput. Biol.
**2019**, 15, e1007509. [Google Scholar] [CrossRef] - Delvenne, J.C.; Schaub, M.T.; Yaliraki, S.N.; Barahona, M. The stability of a graph partition: A dynamics-based framework for community detection. In Dynamics On and Of Complex Networks, Volume 2; Springer: Berlin/Heidelberg, Germany, 2013; pp. 221–242. [Google Scholar]
- Schaub, M.T.; Delvenne, J.C.; Yaliraki, S.N.; Barahona, M. Markov Dynamics as a Zooming Lens for MultiscaleCommunity Detection: Non Clique-Like Communitiesand the Field-of-View Limit. PLoS ONE
**2012**, 7, e32210. [Google Scholar] [CrossRef] - Dorfler, F.; Bullo, F. Kron Reduction of Graphs With Applications to Electrical Networks. IEEE Trans. Circ. Syst. I Regul. Pap.
**2013**, 60, 150–163. [Google Scholar] [CrossRef] - Wang, R.; Nguyen, D.D.; Wei, G.W. Persistent spectral graph. Int. J. Numer. Methods Biomed. Eng.
**2020**, 36, e3376. [Google Scholar] [CrossRef] - Mémoli, F.; Wan, Z.; Wang, Y. Persistent Laplacians: Properties, Algorithms and Implications. arXiv
**2021**, arXiv:2012.02808. [Google Scholar] [CrossRef] - Belkin, M.; Niyogi, P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput.
**2003**, 15, 1373–1396. [Google Scholar] [CrossRef] - Calvetti, D.; Reichel, L.; Sorensen, D.C. An implicitly restarted Lanczos method for large symmetric eigenvalue problems. Electron. Trans. Numer. Anal.
**1994**, 2, 21. [Google Scholar] - Delvenne, J.C.; Yaliraki, S.N.; Barahona, M. Stability of graph communities across time scales. Proc. Natl. Acad. Sci. USA
**2010**, 107, 12755–12760. [Google Scholar] [CrossRef] - Lambiotte, R.; Delvenne, J.C.; Barahona, M. Random walks, Markov processes and the multiscale modular organization of complex networks. IEEE Trans. Netw. Sci. Eng.
**2014**, 1, 76–90. [Google Scholar] [CrossRef] - Masuda, N.; Porter, M.A.; Lambiotte, R. Random walks and diffusion on networks. Phys. Rep.
**2017**, 716, 1–58. [Google Scholar] [CrossRef] - Porter, M.A.; Onnela, J.P.; Mucha, P.J. Communities in networks. Not. AMS
**2009**, 56, 1082–1097. [Google Scholar] - Blondel, V.D.; Guillaume, J.L.; Lambiotte, R.; Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp.
**2008**, 2008, P10008. [Google Scholar] [CrossRef] - Bacik, K.A.; Schaub, M.T.; Beguerisse-Díaz, M.; Billeh, Y.N.; Barahona, M. Flow-based network analysis of the Caenorhabditis elegans connectome. PLoS Comput. Biol.
**2016**, 12, e1005055. [Google Scholar] [CrossRef] - Beguerisse-Diaz, M.; Vangelov, B.; Barahona, M. Finding role communities in directed networks using Role-Based Similarity, Markov Stability and the Relaxed Minimum Spanning Tree. In Proceedings of the 2013 IEEE Global Conference on Signal and Information Processing, Austin, TX, USA, 3–5 December 2013. [Google Scholar]
- Liu, Z.; Barahona, M. Graph-based data clustering via multiscale community detection. Appl. Netw. Sci.
**2020**, 5, 1–20. [Google Scholar] [CrossRef] - Meilă, M. Comparing clusterings—An information based distance. J. Multivar. Anal.
**2007**, 98, 873–895. [Google Scholar] [CrossRef] - Barahona, M. The Stability of a Graph Partition. Available online: https://www.ma.imperial.ac.uk/~mpbara/Partition_Stability/ (accessed on 23 May 2022).
- Ghrist, R. Barcodes: The persistent topology of data. Bull. Am. Math. Soc.
**2008**, 45, 61–75. [Google Scholar] [CrossRef] - Genomics 1. 10X Peripheral Blood Mononuclear Cells (PBMC) Data. 1 June 2022. Available online: https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz (accessed on 1 June 2022).
- Satija Lab, N. Seurat Guided Clustering Tutorial. Available online: https://satijalab.org/seurat/articles/pbmc3k_tutorial.html (accessed on 23 May 2022).
- Hafemeister, C.; Satija, R. Using Sctransform in Seurat. Available online: https://satijalab.org/seurat/articles/sctransform_vignette.html (accessed on 23 May 2022).
- Wolf, A.; Ramirez, F.; Rybakov, S. Scanpy Tutorials Preprocessing and Clustering 3k PBMCs. Available online: https://scanpy-tutorials.readthedocs.io/en/latest/pbmc3k.html (accessed on 2 August 2022).
- Lambrechts, D.; Wauters, E.; Boeckx, B.; Aibar, S.; Nittner, D.; Burton, O.; Bassez, A.; Decaluwé, H.; Pircher, A.; Van den Eynde, K.; et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. Med.
**2018**, 24, 1277–1289. [Google Scholar] [CrossRef] - Yang, L.; Wang, W.H.; Qiu, W.L.; Guo, Z.; Bi, E.; Xu, C.R. A Single-Cell Transcriptomic Analysis Reveals Precise Pathways and Regulatory Mechanisms Underlying Hepatoblast Differentiation. Hepatology
**2017**, 66, 1387–1401. [Google Scholar] [CrossRef] - Hafemeister, C.; Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol.
**2019**, 20, 1–15. [Google Scholar] [CrossRef] [PubMed] - Satija Lab, NYU. Differential Expression Testing. Available online: https://satijalab.org/seurat/articles/de_vignette.html (accessed on 23 July 2022).
- Mu, T.; Xu, L.; Zhong, Y.; Liu, X.; Zhao, Z.; Huang, C.; Lan, X.; Lufei, C.; Zhou, Y.; Su, Y.; et al. Embryonic Liver Developmental Trajectory Revealed by Single-Cell RNA Sequencing in the Foxa2eGFP Mouse. Commun. Biol.
**2020**, 3, 1–12. [Google Scholar] [CrossRef] [PubMed] - Alvarez, M.; Rahmani, E.; Jew, B.; Garske, K.M.; Miao, Z.; Benhammou, J.N.; Ye, C.J.; Pisegna, J.R.; Pietiläinen, K.H.; Halperin, E.; et al. Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM. Sci. Rep.
**2020**, 10, 11019. [Google Scholar] [CrossRef] [PubMed] - Rindler, K.; Bauer, W.M.; Jonak, C.; Wielscher, M.; Shaw, L.E.; Rojahn, T.B.; Thaler, F.M.; Porkert, S.; Simonitsch-Klupp, I.; Weninger, W.; et al. Single-cell RNA sequencing reveals tissue compartment-specific plasticity of mycosis fungoides tumor cells. Front. Immunol.
**2021**, 12, 666935. [Google Scholar] [CrossRef] [PubMed] - Sookoian, S.; Flichman, D.; Garaycoechea, M.E.; San Martino, J.; Castaño, G.O.; Pirola, C.J. Metastasis-associated lung adenocarcinoma transcript 1 as a common molecular driver in the pathogenesis of nonalcoholic steatohepatitis and chronic immune-mediated liver damage. Hepatol. Commun.
**2018**, 2, 654–665. [Google Scholar] [CrossRef] [PubMed] - Cohen, L.A.; Gutierrez, L.; Weiss, A.; Leichtmann-Bardoogo, Y.; Zhang, D.l.; Crooks, D.R.; Sougrat, R.; Morgenstern, A.; Galy, B.; Hentze, M.W.; et al. Serum ferritin is derived primarily from macrophages through a nonclassical secretory pathway. Blood J. Am. Soc. Hematol.
**2010**, 116, 1574–1584. [Google Scholar] [CrossRef] [PubMed] - Theurl, I.; Mattle, V.; Seifert, M.; Mariani, M.; Marth, C.; Weiss, G. Dysregulated monocyte iron homeostasis and erythropoietin formation in patients with anemia of chronic disease. Blood
**2006**, 107, 4142–4148. [Google Scholar] [CrossRef] - Zarjou, A.; Black, L.M.; McCullough, K.R.; Hull, T.D.; Esman, S.K.; Boddu, R.; Varambally, S.; Chandrashekar, D.S.; Feng, W.; Arosio, P.; et al. Ferritin light chain confers protection against sepsis-induced inflammation and organ injury. Front. Immunol.
**2019**, 10, 131. [Google Scholar] [CrossRef] - Pizzolato, G.; Kaminski, H.; Tosolini, M.; Franchini, D.M.; Pont, F.; Martins, F.; Valle, C.; Labourdette, D.; Cadot, S.; Quillet-Mary, A.; et al. Single-cell RNA sequencing unveils the shared and the distinct cytotoxic hallmarks of human TCRVδ1 and TCRVδ2 γδ T lymphocytes. Proc. Natl. Acad. Sci. USA
**2019**, 116, 11906–11915. [Google Scholar] [CrossRef] - Geng, Z.; Tao, Y.; Zheng, F.; Wu, L.; Wang, Y.; Wang, Y.; Sun, Y.; Fu, S.; Wang, W.; Xie, C.; et al. Altered monocyte subsets in Kawasaki disease revealed by single-cell RNA-sequencing. J. Inflamm. Res.
**2021**, 14, 885. [Google Scholar] [CrossRef] - Cormican, S.; Griffin, M.D. Human monocyte subset distinctions and function: Insights from gene expression analysis. Front. Immunol.
**2020**, 11, 1070. [Google Scholar] [CrossRef] [PubMed] - Victor, A.R.; Weigel, C.; Scoville, S.D.; Chan, W.K.; Chatman, K.; Nemer, M.M.; Mao, C.; Young, K.A.; Zhang, J.; Yu, J.; et al. Epigenetic and posttranscriptional regulation of CD16 expression during human NK cell development. J. Immunol.
**2018**, 200, 565–572. [Google Scholar] [CrossRef] [PubMed] - Crinier, A.; Dumas, P.Y.; Escalière, B.; Piperoglou, C.; Gil, L.; Villacreces, A.; Vély, F.; Ivanovic, Z.; Milpied, P.; Narni-Mancinelli, É.; et al. Single-cell profiling reveals the trajectories of natural killer cell differentiation in bone marrow and a stress signature induced by acute myeloid leukemia. Cell. Mol. Immunol.
**2021**, 18, 1290–1304. [Google Scholar] [CrossRef] - Stegle, O.; Teichmann, S.A.; Marioni, J.C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet.
**2015**, 16, 133–145. [Google Scholar] [CrossRef] [PubMed] - Lee, R.D.; Munro, S.A.; Knutson, T.P.; LaRue, R.S.; Heltemes-Harris, L.M.; Farrar, M.A. Single-cell analysis identifies dynamic gene expression networks that govern B cell development and transformation. Nat. Commun.
**2021**, 12, 1–16. [Google Scholar] [CrossRef] [PubMed] - Ullah, H.; Sajid, M.; Yan, K.; Feng, J.; He, M.; Shereen, M.A.; Li, Q.; Xu, T.; Hao, R.; Guo, D.; et al. Antiviral activity of interferon alpha-inducible protein 27 against hepatitis B virus gene expression and replication. Front. Microbiol.
**2021**, 12, 656353. [Google Scholar] [CrossRef] - Monticelli, L.A.; Osborne, L.C.; Noti, M.; Tran, S.V.; Zaiss, D.M.; Artis, D. IL-33 promotes an innate immune pathway of intestinal tissue protection dependent on amphiregulin–EGFR interactions. Proc. Natl. Acad. Sci. USA
**2015**, 112, 10762–10767. [Google Scholar] [CrossRef] - Zaiss, D.M.; Gause, W.C.; Osborne, L.C.; Artis, D. Emerging functions of amphiregulin in orchestrating immunity, inflammation, and tissue repair. Immunity
**2015**, 42, 216–226. [Google Scholar] [CrossRef] - Bennstein, S.B.; Weinhold, S.; Manser, A.R.; Scherenschlich, N.; Noll, A.; Raba, K.; Kögler, G.; Walter, L.; Uhrberg, M. Umbilical cord blood-derived ILC1-like cells constitute a novel precursor for mature KIR+ NKG2A-NK cells. Elife
**2020**, 9, e55232. [Google Scholar] [CrossRef] - Bernink, J.H.; Peters, C.P.; Munneke, M.; Te Velde, A.A.; Meijer, S.L.; Weijer, K.; Hreggvidsdottir, H.S.; Heinsbroek, S.E.; Legrand, N.; Buskens, C.J.; et al. Human type 1 innate lymphoid cells accumulate in inflamed mucosal tissues. Nat. Immunol.
**2013**, 14, 221–229. [Google Scholar] [CrossRef] - Saelens, W.; Cannoodt, R.; Todorov, H.; Saeys, Y. A Comparison of Single-Cell Trajectory Inference Methods. Nat. Biotechnol.
**2019**, 37, 547–554. [Google Scholar] [CrossRef] [PubMed] - Van den Berge, K.; Roux de Bézieux, H.; Street, K.; Saelens, W.; Cannoodt, R.; Saeys, Y.; Dudoit, S.; Clement, L. Trajectory-Based Differential Expression Analysis for Single-Cell Sequencing Data. Nat. Commun.
**2020**, 11, 1201. [Google Scholar] [CrossRef] [PubMed] - Trapnell, C.; Cacchiarelli, D.; Grimsby, J.; Pokharel, P.; Li, S.; Morse, M.; Lennon, N.J.; Livak, K.J.; Mikkelsen, T.S.; Rinn, J.L. The Dynamics and Regulators of Cell Fate Decisions Are Revealed by Pseudotemporal Ordering of Single Cells. Nat. Biotechnol.
**2014**, 32, 381–386. [Google Scholar] [CrossRef] [PubMed] - Qiu, X.; Mao, Q.; Tang, Y.; Wang, L.; Chawla, R.; Pliner, H.A.; Trapnell, C. Reversed Graph Embedding Resolves Complex Single-Cell Trajectories. Nat. Methods
**2017**, 14, 979–982. [Google Scholar] [CrossRef] [PubMed] - Lönnberg, T.; Svensson, V.; James, K.R.; Fernandez-Ruiz, D.; Sebina, I.; Montandon, R.; Soon, M.S.F.; Fogg, L.G.; Nair, A.S.; Liligeto, U.; et al. Single-Cell RNA-seq and Computational Analysis Using Temporal Mixture Modelling Resolves Th1/Tfh Fate Bifurcation in Malaria. Sci. Immunol.
**2017**, 2, eaal2192. [Google Scholar] [CrossRef] [PubMed] - Ji, Z.; Ji, H. TSCAN: Pseudo-time Reconstruction and Evaluation in Single-Cell RNA-seq Analysis. Nucl. Acids Res.
**2016**, 44, e117. [Google Scholar] [CrossRef] - Su, X.; Shi, Y.; Zou, X.; Lu, Z.N.; Xie, G.; Yang, J.Y.; Wu, C.C.; Cui, X.F.; He, K.Y.; Luo, Q.; et al. Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development. BMC Genom.
**2017**, 18, 1–14. [Google Scholar] [CrossRef] - The Human Protein Atlas—MDK. Available online: https://www.proteinatlas.org/ENSG00000110492-MDK/single+cell+type/liver (accessed on 2 August 2022).
- Leu, J.J.; George, D.L. Hepatic IGFBP1 is a prosurvival factor that binds to BAK, protects the liver from apoptosis, and antagonizes the proapoptotic actions of p53 at mitochondria. Genes Dev.
**2007**, 21, 3095–3109. [Google Scholar] [CrossRef] - Rabadán, R.; Mohamedi, Y.; Rubin, U.; Chu, T.; Alghalith, A.N.; Elliott, O.; Arnés, L.; Cal, S.; Obaya, Á.J.; Levine, A.J.; et al. Identification of relevant genetic alterations in cancer using topological data analysis. Nat. Commun.
**2020**, 11, 1–10. [Google Scholar] [CrossRef] - Tremblay, N.; Borgnat, P. Graph wavelets for multiscale community mining. IEEE Trans. Signal Process.
**2014**, 62, 5227–5239. [Google Scholar] [CrossRef] - Bick, C.; Gross, E.; Harrington, H.A.; Schaub, M.T. What are higher-order networks? arXiv
**2021**, arXiv:2104.11329. [Google Scholar] - Kuchroo, M.; Godavarthi, A.; Tong, A.; Wolf, G.; Krishnaswamy, S. Multimodal Data Visualization and Denoising with Integrated Diffusion. In Proceedings of the 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), Gold Coast, Australia, 25–28 October 2021; pp. 1–6. [Google Scholar]

**Figure 1.**The eigenscore method (defined in Section 2.2) demonstrated here on a graph constructed by taking 100 random points each from of four touching balls in 30 dimensions and connecting them via a 15-nearest-neighbour graph. (

**A**) Laplacian eigenvectors ${e}_{1}$ and ${e}_{2}$ distinguish the left and right two clusters and the top and bottom two clusters, respectively. (

**B**) Different graph signals align or anti-align differently with the two eigenvectors, resulting in a plot of eigenscore $({\mathrm{eig}}_{1}$, ${\mathrm{eig}}_{2})$-space that differentiates the various signals. A random signal plots near the origin.

**Figure 2.**The graph on the

**left**displays community structures at four different scales, exemplified by the groups A, B, C and D. When computing the mean pairwise variation of information (

**right**) as a function of scale (Markov time), we find local minima corresponding to resolutions A (256 communities), B (64 communities), C (16 communities) and D (4 communities). Figure inspired by [32].

**Figure 3.**We construct a graph with three communities, all of different sizes. (

**A**) The VI (on y-axis, VI is 0 except for a brief spike around $t=3.35$) identifies resolutions ${t}_{1}$, at which all three communities are identified, and ${t}_{2},$ at which two communities are identified (note that due to the simplicity of the graph, there are intervals of local minima instead of points; we pick ${t}_{1}$ before the spike and ${t}_{2}$ after). In (

**B**), we calculate the MLS at ${t}_{1}$ and ${t}_{2}$ (given by black circles) of three signals that are equal to 1 on one of the ${t}_{1}$-communities (constant part of the signal is highlighted by arrows) and uniformly random elsewhere, and one completely random signal. The signal that is constant on the largest cluster (

**bottom left**) is identified as highly consistent at both times. The random signal (

**top right**) is identified as inconsistent at both times. Conversely, the signal constant on the smallest community (

**top left**) has a high MLS at ${t}_{2}$ relative to the MLS at ${t}_{1}$, separating it from the signal constant on the community of intermediate size (

**centre**).

**Figure 4.**The persistent Rayleigh quotient for cell differentiation. (

**A**) (

**left**) Signals (genes) on the graph that we aim to differentiate. (

**right**) The model for the bifurcating differentiation process. (

**B**) The effects on the graph and graph Laplacian after applying the Kron reduction process to the daughter cells. (

**C**) The normalised Rayleigh quotients of (x-axis) full Laplacian ${L}_{{t}_{1}-{t}_{0}}^{{t}_{1}-{t}_{0}}$ and (y-axis) persistent Laplacian ${L}_{0}^{{t}_{1}-{t}_{0}}$ for binary functions on the graph representing high and low gene expression of a particular gene. The persistent Rayleigh quotient separates these genes based on relevance to the bifurcation: ${g}_{1}$ is expressed in all cell types, ${g}_{2}$ is expressed in the parent and one daughter cell type, ${g}_{3}$ is expressed only in both daughter cell types, ${g}_{4}$ is expressed only in one daughter cell type.

**Figure 6.**Geometry of cell space and gene space. (

**A**) Cell types in PBMC data [34]. (

**B**) UMAP of genes set in eigenscore space for eigenvectors 1–16. Genes (dots) are colour-coded for the logarithm of the norm of the vector in 16-dimensional eigenscore space. Genes with similar expression patterns in the PBMC single-cell data [34] plot close together in eigenscore space, and expression patterns vary continuously as we move through this space. The outward branches I–VI correspond to genes that are expressed highly on specific groups of cells.

**Figure 7.**Eigenscores compared to differential gene expression (DGE) on PBMC data set [34]. (

**A**) Comparative study of DGE ranking using Seurat clustering and a non-parametric Wilcoxon rank sum test (log of rank computed from adjusted p-value on x-axis) versus ranking by norm in eigenscore space (log of eigenscore rank of 16 lowest frequencies on y-axis). Example genes in top 100 for one ranking but not the other shown on the sides. (

**B**) Top 100 genes ranked by adjusted p-value in DGE marked on the eigenscore UMAP plot of genes from Figure 6. Two regions in the UMAP not found in the top of DGE are branch V from Figure 6B (T cell and lymphocyte genes that are expressed in larger groups of cells); branch VI (genes expressed in RRM2+ cluster that is not found by DGE). (

**C**) Quantitative comparison of gene ranks given by adjusted p-value in DGE versus norm in 16-dimensional eigenscore space.

**Figure 8.**(

**A**) UMAP of genes from T cell data set [38] in eigenscore space for eigenvectors 1–19, colour-coded for the logarithm of norm of the vector in 19-dimensional eigenscore space. Genes with similar expression group together and reveal substructure in the data set. Some genes have unique expression patterns not matched by other genes. Boxed genes represent a group of genes with similar expression whereas unboxed genes represent isolated gene behaviour. (

**B**) Top 20 genes ranked by norm in 1–19 dimensional eigenscore space.

**Figure 9.**Multiscale Laplacian scores of PBMC data set [34]. (

**A**) The graph of variation of information of community structures returned by 100 iterations of the Louvain algorithm at each Markov time. Local minima indicate stable community structures and, hence, scales of interest. The community structures at three such minima are shown by colourings of UMAP plots. (

**B**) Left: three scatter plots comparing the multiscale Laplacian scores of genes (grey dots) at successive times to one another (upper two) and of ${t}_{3}$ to the combinatorial Laplacian score (in all plots, axes are truncated). We highlight 6 genes of interest (annotated). Middle and Right: UMAP plots visualising the gene expression of six genes selected based on their MLS.

**Figure 10.**Multiscale Laplacian score of human T cell data set [38]. (

**A**) The graph of variation of information of community structures. Again, local minima indicate scales of interest. Community structures at three scales are picked out. (

**B**) (

**Left**): three scatter plots comparing the multiscale Laplacian scores of genes (grey dots) at successive times to one another (

**left**and

**middle**plot) and of ${t}_{3}$ to the combinatorial Laplacian score (in all plots, axes are truncated). We highlight 6 genes of interest (black dots; annotated). (

**Middle**and

**Right**): UMAP plots visualising the gene expression of six genes selected based on their MLS.

**Figure 11.**The persistent Rayleigh quotient separates genes by their role in a cell differentiation process. The PRQ is parameterised by birth (i) and death (j), each pair $(i,j)$ assigning a non-negative number to every gene. We plot these values for each gene for $(i=7,j=7)$ on the x-axis and $(i=2,j=7)$ on the y-axis on subfigure (

**C**). Selected for display (

**A**,

**B**,

**D**,

**E**) are top differentially expressed genes from [42] on the data from [39] (see Figure A4). Genes Tubb5, Mdk, and Igfbp1 are expressed in parent and one daughter cell lineage, hepatoblast to (

**A**) cholangiocyte or (

**B**) hepatocyte and lie above the diagonal. Genes Aldob and Mt2 are expressed in both daughter cell types but not in the parent cell type (

**D**), and they lie below the diagonal. Genes Ahsg and Fabp1 are only expressed in one daughter cell type (

**E**) and lie on the diagonal (compare with Figure 4).

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## Share and Cite

**MDPI and ACS Style**

Hoekzema, R.S.; Marsh, L.; Sumray, O.; Carroll, T.M.; Lu, X.; Byrne, H.M.; Harrington, H.A. Multiscale Methods for Signal Selection in Single-Cell Data. *Entropy* **2022**, *24*, 1116.
https://doi.org/10.3390/e24081116

**AMA Style**

Hoekzema RS, Marsh L, Sumray O, Carroll TM, Lu X, Byrne HM, Harrington HA. Multiscale Methods for Signal Selection in Single-Cell Data. *Entropy*. 2022; 24(8):1116.
https://doi.org/10.3390/e24081116

**Chicago/Turabian Style**

Hoekzema, Renee S., Lewis Marsh, Otto Sumray, Thomas M. Carroll, Xin Lu, Helen M. Byrne, and Heather A. Harrington. 2022. "Multiscale Methods for Signal Selection in Single-Cell Data" *Entropy* 24, no. 8: 1116.
https://doi.org/10.3390/e24081116