
==== Front
Brief Bioinform
Brief Bioinform
bib
Briefings in Bioinformatics
1467-5463
1477-4054
Oxford University Press

10.1093/bib/bbae436
bbae436
Problem Solving Protocol
AcademicSubjects/SCI01060
scHyper: reconstructing cell–cell communication through hypergraph neural networks
Li Wenying School of Mathematics and System Science, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi, Xinjiang 830017, China

Wang Haiyun School of Mathematics and System Science, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi, Xinjiang 830017, China

https://orcid.org/0000-0002-8486-744X
Zhao Jianping School of Mathematics and System Science, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi, Xinjiang 830017, China

https://orcid.org/0000-0003-3024-1705
Xia Junfeng School of Mathematics and System Science, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi, Xinjiang 830017, China
Institute of Physical Science and Information Technology, Anhui University, No. 111 Jiulong Road, Shushan District, Hefei, Anhui 230601, China

https://orcid.org/0000-0002-3399-7260
Sun Xiaoqiang School of Mathematics, Sun Yat-sen University, No. 135 Xingang Xi Road, Haizhu District, Guangzhou, Guangdong 510275, China

Corresponding authors. E-mail: jpzhao@xju.edu.cn; E-mail: jfxia@ahu.edu.cn; E-mail: sunxq6@mail.sysu.edu.cn
9 2024
13 9 2024
13 9 2024
25 5 bbae43626 5 2024
14 7 2024
07 8 2024
© The Author(s) 2024. Published by Oxford University Press.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

Cell–cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive global network view of multilayered communications. In this context, we propose scHyper, a new method that can infer intercellular communications from a global network perspective and identify the potential impact of all cells, ligand, and receptor expression on the communication score. scHyper designed a new way to represent tripartite relationships, by extracting a heterogeneous hypergraph that includes the source (ligand expression), the target (receptor expression), and the relevant ligand–receptor (L-R) pairs. scHyper is based on hypergraph representation learning, which measures the degree of match between the intrinsic attributes (static embeddings) of nodes and their observed behaviors (dynamic embeddings) in the context (hyperedges), quantifies the probability of forming hyperedges, and thus reconstructs the cell–cell communication score. Additionally, to effectively mine the key mechanisms of signal transmission, we collect a rich dataset of multisubunit complex L-R pairs and propose a nonparametric test to determine significant intercellular communications. Comparing with other tools indicates that scHyper exhibits superior performance and functionality. Experimental results on the human tumor microenvironment and immune cells demonstrate that scHyper offers reliable and unique capabilities for analyzing intercellular communication networks. Therefore, we introduced an effective strategy that can build high-order interaction patterns, surpassing the limitations of most methods that can only handle low-order interactions, thus more accurately interpreting the complexity of intercellular communications.

intercellular communications
global network
hypergraph neural networks
deep learning
single-cell RNA-seq
Talent Program of Xinjiang Autonomous Region-Youth Outstanding Talent and Youth Innovative Talent 2023TSYCCX0104 National Natural Science Foundation of China 10.13039/501100001809 62362062 62273364 Xinjiang Key Laboratory of Applied Mathematics XJDX1401
==== Body
pmcIntroduction

In multicellular organisms, intercellular communications are crucial factors in maintaining normal physiological functions and regulating the progression of diseases [1]. For example, intercellular communications coordinates important biological processes such as tissue homeostat [2, 3], neural transmission [4, 5], immune responses [6, 7], growth, and homeostasis within the body [8]. Intercellular communications across multiple cell types and tissues depend on complex interactions between secreted ligands and cell surface receptors [9]. Recently, with the widespread application of single-cell RNA sequencing (scRNA-seq) technology, the deciphering of cell-to-cell crosstalk based on ligand–receptor (L-R) pairs [9–12].

To identify cell–cell communications from scRNA-seq data, several computational tools have been developed based on L-R pairs. These tools can be broadly divided into statistical-based, network-based, spatial information-based, and tensor-based methods [13]. Statistical-based methods, such as SingleCellSignalR [14], CellPhoneDB [15] CellChat [16], and ICELLNET [17], use a statistical test to detect significantly enriched L-R interactions (LRIs) between two cell types. Network-based methods construct a complex network model to determine LRIs, including Connectome [18], NATMI [19], scMLNet [20], CytoTalk [21], NicheNet [22], and Holonet [23]. Spatial information–based methods, such as Giotto [24], SpaOTsc [25], PearlST [26], and SpaTalk [27], dissect LRIs by combining spatial information. Tensor-based methods, such as Tensor-cell2cell [28] and scTensor [29], use tensor decomposition-based methods to dissect context-driven LRIs and multi-to-multi LRIs.

The above computational methods effectively infer intercellular communications based on ligands and receptors expression. However, there are still limitations in inferring cell–cell communications based on the expression strength or specificity of L-R pairs. Firstly, these methods all implicitly assume a linear modeling approach, such as using L-R pairs’ co-expression (sum, mean, or product) [15, 17–19], differential expression [30, 31], and expression correlation [32] to determine the LRIs between two cell types, so that the communication score can be viewed as a linear combination of two gene expression levels. Therefore, most methods may only capture simple linear relationships and poorly describe complex nonlinear relationships or multipathway signal transmission situations. More importantly, these methods usually assume that intercellular communications consist of a one-to-one relationship between two cell types, such as CellChat [16] and SingleCellSignalR [14], respectively, using the law of mass action and regularized scoring to simulate the interaction between two cell clusters. Therefore, while each pair of intercellular communications can be analyzed individually, aggregating all these interactions can form a more comprehensive communication network that can depict the full picture of how multiple cell types interact through different signaling pathways. However, most existing methods lack a global network perspective and are unable to effectively describe this complex communication network among multiple cell types. Moreover, there are currently few deep learning frameworks for predicting intercellular communications from scRNA-seq data, which will provide a unique insight into intercellular communications prediction.

In this paper, we developed scHyper, which aims to decode context-driven intercellular communications from scRNA-seq data by constructing hypergraph networks and applying hypergraph neural network models [33].

(i) scHyper integrates the Cellinker [34] and CellChatDB [16] to obtain rich L-R interaction relationships and signaling pathway annotations.

(ii) scHyper constructs intercellular communications networks in the form of hypergraphs, analyzing intercellular communications from a global perspective.

(iii) scHyper uses hypergraph neural network models to learn intercellular communications patterns and capture nonlinear relationships and complex interaction patterns.

(iv) scHyper implements nonparametric tests to evaluate the statistical significance of interactions between cell types and thus identifies intercellular communications relationships that are biologically significant.

Materials and methods

Construction of intercellular communications tensor

Firstly, Given an scRNA-seq gene expression matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $X\in{R}^{I\times K}$\end{document}, where I is the number of cells \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${C}_i,i=1,2,\cdots, I$\end{document}, K is the number of genes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${G}_k,k=1,2,\cdots, J$\end{document}, and M is the number of cell types in the collection \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${U}_m,m=1,2,\cdots, M$\end{document}, which contains \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${n}_m$\end{document} cells, and cell type labels can be specified by the user’s prior analysis, such as gene expression clustering.

Secondly, an average expression matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $Y\in{R}^{M\times K}$\end{document} with M cell types and K genes can be constructed, where M is the number of average vectors for all cell types. Y is obtained from X as follows:

(1) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*} Y= ZWX, \end{equation*}\end{document}

where Z is an \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $M\times M$\end{document} diagonal matrix, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $W\in{R}^{M\times I}$\end{document} converts the cell-level matrix X to the cell type-level matrix Y, and the elements \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${Z}_{m,m}$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${W}_{m,i}$\end{document} are:

(2) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*}\kern-3.3pc {Z}_{m,m}=\frac{1}{n_m}, \end{equation*}\end{document}

(3) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*} {W}_{m,i}=\left\{\begin{array}{@{}c}1,{C}_i\in{U}_m\\{}0,{C}_i\notin{U}_m\end{array},\right. \end{equation*}\end{document}

Finally, we screen all effective L-R pairs by extracting the intersection of genes given by the L-R database and scRNA-seq data. For a specific L-R pair in the database, if both the ligand and receptor are present in the scRNA-seq data, the length-M vector of the ligand and receptor genes will be extracted. For the J effective L-R pairs extracted, an \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $M\times M$\end{document} matrix is calculated and incrementally stored. The stacked \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $M\times M$\end{document} matrices will be formed as a third-order tensor, ultimately representing the tensor of sending cell types, L-R pairs, and receiving cell types.

(4) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*} {\mathrm{\chi}}_{:j:}={y}_{L_j}\circ{y}_{R_j}, \end{equation*}\end{document}

(5) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*} {y}_{i,{L}_j}=\sqrt[h1]{L_{j,1}\cdots{L}_{j,h1}},{y}_{i,{R}_j}=\sqrt[h2]{R_{j,1}\cdots{R}_{j,h2}}, \end{equation*}\end{document}

Here, the vector \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${y}_{L_j}=[{y}_{1,{L}_j},\cdots, {y}_{M,{L}_j}]$\end{document} and the vector \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${y}_{R_j}=[{y}_{1,{R}_j},\cdots, {y}_{M,{R}_j}]$\end{document} represent the average expression levels of ligand L and receptor R in all cell types for the j-th pair of L-R complexes, respectively. Inspired by the multisubunit ligand and receptor calculation method in CellChat [16], we approximate the expression level of ligand L with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${h}_1$\end{document} subunits through its geometric mean, which means that the expression of any subunit at zero will render the ligand inactive. Similarly, we calculate the expression level of receptor R with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${h}_2$\end{document} subunits.

Hypergraph network forming

For the hypergraph, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $G=\left(V,E\right)$\end{document}, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $V=\{{v}_i,i=1,2,3\}$\end{document} represents the set of nodes in the hypergraph, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $E=\{{e}_i=({v}_1^i,\cdots, {v}_n^i)\}$\end{document} represents the set of hyperedges, where each hyperedge \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $e\in E$\end{document} can connect two or more nodes. We construct the hypergraph network using the intercellular communications tensor \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathrm{\chi}$\end{document}. In this network, the node set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $V=\left({v}_1,{v}_2,{v}_3\right)$\end{document} only involves three types:

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ {v}_1=\left({v}_1^1,\cdots, {v}_1^{M1}\right), {v}_2=\left({v}_2^1,\cdots, {v}_2^{J_1}\right), {v}_3=\left({v}_3^1,\cdots, {v}_3^{M2}\right),$$\end{document}

which represent the types of sending cell types, L-R pairs, and receiving cell types, respectively. Each hyperedge in the hyperedge set E connects three nodes

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ {e}_i=\left({v}_1^i,{v}_2^i,{v}_3^i\right), $$\end{document}

representing the co-expression of the ligand of the sending cell type and the specific receptor of the receiving cell type. If \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\chi}_{i,j,k}>0$\end{document}, a hyperedge \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $e=\left({v}_1,{v}_2,{v}_3\right)$\end{document} is formed, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\mathrm{\chi}}_{i,j,k}$\end{document} will be used as the weight of the hyperedge. If \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\mathrm{\chi}}_{:j:}=0$\end{document}, it means that ligand \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${L}_j$\end{document} and receptor \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${R}_j$\end{document} cannot co-express in all cell types, so the j-th L-R pair will not be a node in the hypergraph network. The same rule applies to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\mathrm{\chi}}_{i\colon\colon }$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\mathrm{\chi}}_{\colon\colon k}$\end{document}. Ultimately, the effective nodes in the hypergraph network include \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${M}_1$\end{document} sending cell types, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${J}_1$\end{document} L-R pairs, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${M}_2$\end{document} receiving cell types. Meanwhile, the adjacency matrix A is constructed based on the hypergraph structure:

(6) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*} A=H{H}^T-{D}_v, \end{equation*}\end{document}

where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $H\in{R}^{\mid V\mid \times \mid E\mid }$\end{document} with entries \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $h\left(v,e\right)=1$\end{document} if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $v\in e$\end{document} and 0 otherwise. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${D}_v$\end{document} is the diagonal matrix containing the node degree\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $d(v)={\sum}_{e\in E}h\left(v,e\right)$\end{document}, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $A\left({v}_i,{v}_j\right)$\end{document} represents the number of times that nodes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${v}_i$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${v}_j$\end{document} appear together on the same hyperedge.

In order to make the nodes with similar neighborhoods have similar latent representations, the node feature \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\overrightarrow{x}}_i$\end{document} is generated by the encoder based on the neighborhood structure \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${A}_i$\end{document} of node \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${v}_i$\end{document} . For the heterogeneous hypergraph network, considering the special characteristics of different node types, unique latent spaces are learned for each heterogeneous type, with its own autoencoder model [33]. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\hat{A}}_i$\end{document} is the output of the decoder, and the loss function for all types of nodes is:

(7) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*} Los{s}_{enc}=\sum_{t=1}^3{\left|\mathit{\operatorname{sign}}\left({A}_i^t\right)\odot \left({A}_i^t-{\hat{A}}_i^t\right)\right|}_F^2, \end{equation*}\end{document}

Reconstructed intercellular communications score by hypergraph neural network

scHyper applies hypergraph representation learning models [33] to model high-order interaction patterns from the hypergraph constructed from scRNA-seq data. We mainly predict the probability of nodes in V forming a hyperedge or the attributes associated with that hyperedge, in order to reconstruct the cell–cell communication tensor. The model’s input is triplet \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\left({v}_1,{v}_2,{v}_3\right)$\end{document} consisting of sending cell types, L-R pairs, and receiving cell types. Each node in the triplet is generated by a position-wise feed-forward with one fully connected layer to produce “static embeddings”: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\left({s}_1,{s}_2,{s}_3\right)$\end{document}. The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${s}_i$\end{document} of node\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${v}_i$\end{document}is independent of the given triplet and reflects the general topological properties of the given hypergraph node. In addition, the triplet as a whole is processed by a multihead attention mechanism to produce “dynamic embeddings”: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\left({d}_1,{d}_2,{d}_3\right)$\end{document}. The definition of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${d}_i$\end{document} depends on the features of all nodes in the triplet, which reflect the specific attributes of node \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${v}_i$\end{document} in a particular hyperedge.

Next, the Hadamard power of the difference between the static and dynamic embeddings \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${d}_i-{s}_i$\end{document} is passed through the fully connected layer with sigmoid as the activation function to produce \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\hat{y}}_i$\end{document} and then average over all outputs. This triplet represents the predicted probability of the hyperedge:

(8) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*} \hat{y}=\frac{1}{3}\sum_{i=1}^3{\hat{y}}_i=\frac{1}{3}\sum_{i=1}^3\sigma \left( FC\left[{\left({d}_i-{s}_i\right)}^{\circ 2}\right]\right), \end{equation*}\end{document}

where FC is the fully connected layer.

The total loss function of the model is the sum of the classification loss and the encoder loss. In scHyper, the hypergraph is any triplet \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\left({v}_i,{v}_j,{v}_k\right)$\end{document} that is minimized by the binary classification loss (cross entropy) of predicting the score \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\hat{y}$\end{document} and the target score y between the difference, where the hyperedge corresponding to the triplet is considered a positive sample and the rest of the nonhyperedge is considered a negative sample. This indicates the probability that the sending cell type \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${v}_i$\end{document} communicates with the receiving cell type \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${v}_j$\end{document} about the L-R pair \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${v}_j$\end{document}. The classification loss is:

(9) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{align*} Los{s}_{class}=& -\sum_{i,j,k}y\left({v}_i,{v}_j,{v}_k\right){log}\,\hat{y}\left({v}_i,{v}_j,{v}_k\right)+\left[1-y\left({v}_i,{v}_j,{v}_k\right)\right]\nonumber\\&{log}\left[1-\hat{y}\left({v}_i,{v}_j,{v}_k\right)\right], \end{align*}\end{document}

Determining the statistical significance of interactions between cell types

We reconstructed the intercellular communications scores between cell types for specific L-R pairs and implemented a nonparametric test to reduce the false positive rate. Under the null hypothesis, there is no L-R pairs–mediated interaction between cell types, thus determining significant intercellular communications in all combinations. We collected all nonhyperedges in the hypergraph network, i.e. the L-R pairs that have no communication score between two cell types, to obtain the null hypothesis distribution. Nonhyperedges are considered to be L-R pairs that do not participate in the corresponding interaction between cell types. Next, we calculated the quantile of the hyperedge under the null hypothesis distribution and set it as the original P-value. The threshold for all datasets is set to .05.

(10) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{equation*} p- value=\frac{\left\{{\hat{y}}^{(q)}\ge{\hat{y}}_{ijk}\right\}}{Q},q=1,2,\cdots, Q. \end{equation*}\end{document}

Specifically, Q is the number of nonhyperedges. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\hat{y}}_q$\end{document}is the predicted score of the nonhyperedge in the q-th position, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\hat{y}}_{ijk}$\end{document} is the predicted value of the communication score between cell types i and j mediated by the j-th L-R pair. The expression \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\hat{y}}^{(q)}\ge{\hat{y}}_{ijk}$\end{document} within the {} is a condition used to calculate the number of nonhyperedges that satisfy the condition.

Results and discussion

Overview of scHyper

The core algorithm and intercellular communications model of scHyper are shown in Fig. 1. scHyper facilitates the inference of intercellular interactions and signaling pathway analysis by integrating Cellinker [34] and CellChatDB [16] (Fig. 1A). scHyper requires gene expression data from cells and cell labels specifying cell types as input (Fig. 1B). After receiving the input data, scHyper reconstructs intercellular communications through the following three modules for each subsequent analysis, performing the following steps:

Figure 1 Overview of scHyper. (A) Integration of L-R database, with L-R complexes accounting for 21% and signal pathway annotations for 42%. (B) Generation of intercellular communications tensor from scRNA-Seq matrix, cell type labels, and L-R database, treating intercellular communications as a hypergraph. (C) Prediction of intercellular communications scores using hypergraph neural network and inference of biologically significant cellular communications using a nonparametric test. (D) Visualization of intercellular communications through circle plots, chord plot, bubble plot, heatmap, and alluvial plot.

(i) Preprocess the input data by normalizing the gene expression values for each cell and taking the logarithm of the data matrix to reduce the skewness of the data. Based on the scRNA-seq data, obtain a gene expression matrix at the level of cell types. Combine the L-R database to extract the corresponding two or more column vectors (considering that ligands or receptors are multisubunit) from the matrix for each L-R pair. For each L-R pair, calculate the interaction matrix between cell types, constructing the intercellular communications tensor (Fig. 1B). Although the intercellular communications for each pair of cells can be analyzed separately based on gene co-expression, the multiway interaction further increases the complexity of the network. To consider the global information of the network, we regard the tensor as a hypergraph and construct a graph containing three node types (sending cell type, L-R pair, and receiving cell type), where the edge weight is the fraction of the corresponding tensor element (Fig. 1B).

(ii) The adjacency matrix of hypergraph network is learned through the training of autoencoder to use the features of three types of nodes as inputs. The hypergraph neural network [33] learns the “static embedding” \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\overrightarrow{s}$\end{document} to provide the intrinsic features of nodes, and learns the “dynamic embedding” \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\overrightarrow{d}$\end{document} to consider all node features in triples, thus capturing the interactions between nodes in the context of triples and quantifying the degree to which the behavior of a node in a triple deviates from its expected behavior based on its inherent properties, with a larger difference indicating a higher probability of forming a hyperedge in the hypergraph. We treat the probability of forming a hyperedge as the reconstruction score of intercellular communications (Fig. 1C). To break the traditional random permutation significance test method for cell type labels, which reduces the false positive rate, we implemented a nonparametric test to identify significant interactions between cell types.

(iii) scHyper presents the analysis results with rich visualizations for easy comprehension, including circle plot, chord plot, bubble plot, heatmap, and alluvial plot (Fig. 1D).

Comparison of scHyper with other tools

Methods that are similar to scHyper include CellChat [16], Tensor-cell2cell [28], and scTensor [29]. CellChat is a probability matrix of intercellular communications probability constructed from the original scores (a third-order tensor). Although CellChat performs non-negative matrix factorization (NMF) on the second-order modality-aggregated matrix data of the third-order tensor to detect global intercellular communications patterns, CellChat reduces the tensor rank (from the third to the second). Additionally, CellChat fundamentally calculates the communication strength between two cell types using the original scores, and such scores do not take into account the global intercellular communications network. The other two methods, Tensor-cell2cell and scTensor, both decode context-driven intercellular communications by performing different non-negative tensor decompositions. Tensor-cell2cell considers both intercellular communications and intercellular communications background (disease states, organism life stages, and tissue microenvironments) and performs CANDECOMP/PARAFAC-type non-negative tensor decomposition on high-order intercellular communications tensors. scTensor extracts triadic relationships (including ligand expression, receptor expression, and related L-R pairs) and performs non-negative Tucker2 decomposition (NTD-2) to mainly detect hypergraphs with multi–multi relationships. In addition to the effectiveness of modeling high-order intercellular communications, the main differences between scHyper and tensor-cell2cell and scTensor may include the following. First, the models are different; Tensor-cell2cell and scTensor use tensor decomposition models to model intercellular communications networks as static data, while scHyper uses hypergraph neural networks to process biological networks as dynamic and heterogeneous data, particularly suitable for modeling complex high-order relationships between nodes in graph-structured data. The second difference is in their learning ability and model complexity; tensor decomposition models may be limited in their ability to model high-order relationships due to the complexity and representational power of the model, while hypergraph neural networks can more flexibly capture the complex relationships between nodes, particularly in modeling high-order interactions between multiple nodes. This difference can be an advantage or a disadvantage. A higher model complexity can effectively capture the high-order relationships between nodes, but it may increase the computation time.

Figure 2 scHyper was compared with CellChat, CellPhoneDB, iTALK, SingleCellSignalR, MDIC3, and scTenifoldXct. (A) Comparison of the literature support rate of scHyper and the other tools. (B) Heatmap of the Jaccard index in assessing interactions between cell types. (C) Box plot of the AUPRC values of the methods across all CAGE data sets. (D) Heatmap of the ranking-based average similarity index (RSI) for each pair of seven methods on all datasets.

We compared scHyper with the performance of other known methods (CellChat, CellPhoneDB, iTALK, SingleCellSignalR, MDIC3 [35], and scTenifoldXct [36]) to evaluate their accuracy and effectiveness in identifying intercellular communications. First, we calculated the literature support rate for the aforementioned methods using human skin data [37]. scHyper, CellChat, CellPhoneDB, iTALK, SingleCellSignalR, and MDIC3 identified 14, 24, 23, 13, 21, and 21 intercellular communications, respectively (Fig. 2A; Table S1 available online at http://bib.oxfordjournals.org/). scTenifoldXct has identified all intercellular interactions, and the assessment of significance based on literature support rate is deemed to be of little value; therefore, in this context, we do not use the algorithm that includes the literature support rate for scTenifoldXct. Through literature research, we found that 92.56% (13/14) of the communications identified by scHyper were supported by literature (Fig. 2A), which has a higher literature support rate than other methods: CellChat (45.83%), CellPhoneDB (43.48%), iTALK (53.85%), SingleCellSignalR (66.67%), and MDIC3 (38.10%) (Fig. 2A). We determined the Jaccard index for intercellular communications between any two methods. The heatmap shows that scHyper has a higher similarity to CellChat, CellPhoneDB, SingleCellSignalR, and MDIC3 in assessing interactions between cell types (Fig. 2B). Second, we benchmarked the seven methods using cell type–specific CAGE data as a pseudo-gold standard [38]. In the CAGE data, the LR interaction with the expressions of ligand and receptor in the respective cell types both >10 TPM was defined as a positive sample, otherwise as a false sample [9]. Three sets of scRNA-seq data of PBMC acquired from the 10x Genomics website (pbmc3k, pbmc68k, and pbmcMultiome) were used as input for each of the seven methods to infer intercellular LR interactions. We found that scHyper’s AUPRC values are higher than those of other methods (Fig. 2C; Fig. S1 available online at http://bib.oxfordjournals.org/). Lastly, we used six scRNA-seq data sets as input data for the seven methods to infer LR interactions between pairwise combinatorial cell types (Fig. S2 available online at http://bib.oxfordjournals.org/). To investigate the consistency of the overlapped LR interactions, we introduced a rank-based similarity index (RSI) to examine whether the overlapped LR interactions between two methods have a consistent ranking (according to their LR scores) [38]. The results indicated that their ranking of scHyper and CellChat and iTALK was relatively consistent (Fig. 2D). The above results show that there is a significant difference in the performance of different tools. Nevertheless, scHyper still has some comparability and accuracy.

Postulating interactions between cells in human tumors

Chemoresistance is a key factor in cancer-related deaths [39], and previous studies have shown that key components of the tumor microenvironment, such as the extracellular matrix, are favorable to the growth and metastasis of tumor cells [40, 41]. This is crucial for the development of tumors and the emergence of chemoresistance. Therefore, we applied scHyper to the scRNA-seq dataset of metastatic ovarian cancer [39, 42] to evaluate potential cell–cell communication between the stress cancer cells (EOC-C7) and stromal cells and immune cells, including mesothelial cells, endothelial cells, and three types of cancer-associated fibroblasts (CAFs), T cells, macrophages, B cells, natural killer (NK) cells, plasmacytoid DCs (pDC), plasma cells, two types of dendritic cells (DC-1 and DC-2), innate lymphoid cells (ILC), and mast cells.

There is increasing evidence that CAFs play a critical role in cancer pathogenesis [43–46]. Compared to immune cells, stromal cells are the main receivers of signals from stress cancer cells, and, at the same time, stress cancer cells receive signals more frequently from stromal cells (Fig. 3A and B). This suggests that chemotherapy may be related to stress responses in both cancer cells and the extracellular matrix, driving paracrine positive feedback loops. The pathway enrichment analysis indicates that the signals of cancer cells, stromal cells, and immune cells are mainly enriched in positive regulation of inflammatory response, cell adhesion, cell migration, angiogenesis, and the VEGFA-VEGFR2 signaling pathway (Fig. 3E). It is reported that these pathways play a crucial role in the development, spread, and resistance to cancer treatment [47–50]. Therefore, scHyper can help solve the interaction between cancer cells and the tumor microenvironment.

Figure 3 A case study on the application of scHyper in metastatic ovarian cancer. (A) Circle plot of stressed cancer cells (EOC-C7) and stromal cells. (B) Heatmap of signal transmission from stressed cancer cells to stromal cells. The color of the points reflects the communication probability, and the size of the points represents the calculated P-value. Blank areas indicate zero communication probability. (C) Circle plot of stressed cancer cells (EOC-C7) and immune cells. (D) Heatmap of signal transmission from stressed cancer cells to immune cells. (E) Pathway enrichment analysis of tumor cells, immune cells, and stromal cells.

Determining intercellular communications among immune cells in uveitis

To illustrate the wide application of scHyper, we showcased its ability to identify intercellular communications in immune cells. Immune intercellular communications mainly occur through molecules such as cytokines, chemokines, and cell surface receptors, to regulate immune responses and immune functions [51]. Studies have shown that chemokines (CCL2, CXCL10, CXCL8, CXCL9, CCL3, CCL20, and CCL4) are enriched in aqueous compared with plasma during active uveitis [52]. Therefore, we applied scHyper to scRNA-seq of aqueous immune cells in human uveitis. We excluded sparse cell types pDC, plasma cells, and proliferating cells from the interaction and evaluated the potential intercellular communications of chemokines in eight immune cell types, including B cells (B), CD4 T cells (CD4 T), CD8 T cells (CD8 T), regulatory T cells (RegT), unconventional T cells (Unc T), macrophages (Mac), conventional dendritic cells (cDC), and natural killer cells (NK).

Multiple studies have shown that macrophages are the main effector cells causing tissue damage in uveitis [53]. As shown in Fig. 4A and Fig. S3A available online at http://bib.oxfordjournals.org/, compared to other cell types, macrophages (Mac) receive and transmit signals to other immune cells more significantly, suggesting that macrophages seem to play a central role in this chemokine signaling network. In comparison to the paracrine communication from macrophages to other immune cells, macrophages’ autocrine communication has a richer chemokine signal transduction (Fig. S3A available online at http://bib.oxfordjournals.org/). To further determine the role of macrophages in the chemokine signal network, scHyper co-identified 15 autocrine and paracrine communication pathways from macrophages to immune cells, in which macrophages could transmit six chemokine signals, including CXCL9, CXCL10, CCL2, CCL3, CCL20, and CCL4 (Fig. S3B available online at http://bib.oxfordjournals.org/). scHyper also identified 15 pathways from immune cells to macrophages, in which macrophages expressed most of the receptors for chemokines (such as CCR1, CCR5, and CXCR3), thus completing chemokine signal transduction (Fig. S3C available online at http://bib.oxfordjournals.org/). These intercellular communications may lead to the migration, activation, and inflammation regulation of immune cells, thereby affecting the development and pathological process of uveitis [52–56]. These analyses suggest that macrophages play a crucial role in recruiting inflammatory cells to the eye during human uveitis by expressing abundant chemokines and important receptors for chemokines.

Figure 4 A case study on the application of scHyper in uveitis. (A) Alluvial plot shows intercellular communications between immune cells. The cell types expressing ligand genes are on the left, and the cell types expressing receptor genes are on the right. The thickness of the connecting bands indicates the number of L-R interactions between the two cell types. (B) The chord plot shows the significant signaling pathways CXCL (left) and CCL (right) in signal transduction between immune cells. The arrowheads point to the receiving cells, and the line colors match the node colors indicating the sending cells. (C) The relative contributions of each L-R to the overall communication network of the CXCL and CCL signaling pathways, which is the ratio of the inferred network’s total communication probability to the communication probability of the CXCL or CCL signaling pathway. (D) The network diagram shows the predicted L-R interactions between the macrophage ligand and other immune cells expressing receptors. The cell types are indicated by the color of the icons. The oval icons represent ligands, and the rectangle icons represent receptors.

We found that there are some known signaling pathways in the chemokine signal network of immune cells [57, 58], including CXCL and CCL signaling pathways (Fig. 4B). The distribution of CXCL and CCL chemokines among immune cells is consistent with that shown in Fig. 4C. A minority of immune cells, (such as Mac, B, and cDC) transmit CXCL signals, while most immune cells transmit CCL signals, leading to a richer cell–cell signaling and communication through the CCL pathway (Fig. 4B). It is noteworthy that in all known L-R pairs, CCL signaling is dominated by chemokine CCL4 and receptor CCR5, while CXCL signaling is dominated by chemokine CXCL10 and receptor CXCR3 (Fig. 4C). scHyper has built a comprehensive map of the chemokine signaling network in immune cells, determining potential signaling between immune cells–expressed CCL4 and receptors CCR5 and CCR1 (Fig. 4D) and similarly between macrophage CXCL10 and CXCL9 and multiple aqueous immune cells via the receptor CXCR3 (Fig. 4D). These results show that scHyper can effectively infer the key intercellular communications of chemokines in immune cells. The mechanism of immune cell migration activation caused by chemokine enrichment, especially the CCL and CXCL signaling pathways, may be beneficial for revealing broadly applicable therapeutic targets for uveitis.

Figure 5 A case study on the application of scHyper in LS and NL skin of patients with atopic dermatitis. (A) Circle plot of the LS skin. The size of the circles is proportional to the number of cells of each cell type, and the width of the edges represents the communication probability. (B) Comparison of the important L-R pairs involved in inflammation signaling in the NL and LS skin. Inflammatory fibroblasts (Inflam FIB) signal to dendritic cells (Inflam DC, cDC1, cDC2, and LC) and T cells (TC and NKT). (C) Difference in the overall communication score within the inferred network between the NL and LS skin. The overall communication score of a signaling network is calculated by aggregating all communication probabilities within the network. The top four signaling pathway is enriched in the LS skin, while the bottom two signaling pathway is enriched in the NL skin. (D) Distribution of the ligands involved in the enriched signaling pathways in the LS skin. (E) Alluvial plot shows the flow of information in the enriched signaling pathways in the LS skin, showing the correspondence between signaling pathways and cell populations. (F) Relative contribution of each L-R pair in the enriched signaling pathways of the overall communication network in the LS skin.

Joint learning of lesional and normal skin

Atopic dermatitis (AD) is an inflammatory skin disease that involves immune cells and abnormal epidermis and is prevalent in the population [37]. To identify the ability of scHyper to recognize different cell states and intercellular communications, we used scHyper to study signal changes between two different skin conditions. The data were derived from lesional (LS) and normal (NL) skin of AD patients [37]. Both datasets included seven cell types, including inflammatory fibroblasts (Inflam FIB), inflammatory dendritic cells (Inflam DC, cDC1, cDC2, and LC), and inflammatory T cells (TC and NKT). We separately inferred the intercellular communications networks in LS and NL skin (Fig. 5A, Fig. S4A available online at http://bib.oxfordjournals.org/), and we found that intercellular communications activity in LS skin was concentrated in the process of signal transmission from inflammatory fibroblasts to inflammatory immune cells, including dendritic cells and T cells (Fig. 5A), which was consistent with the results of previous immunofluorescence staining [37]. We used bubble plots to visualize the L-R pairs of signal transmission from inflammatory fibroblasts to inflammatory immune cells, and the significant L-R pairs in LS skin were clearly more numerous, indicating that the important L-R pairs in LS skin also contribute to signal transmission from inflammatory fibroblasts to dendritic cells and T cells (Fig. 5B).

By comparing the overall communication probability between LS and NL skin, we identified four signaling pathways that were highly active in the diseased skin, including THBS, COL1A1, PTN, and FGF, and two pathways were active in NL skin, including CLEC and LIGHT (Fig. 5C). These pathways may play an important role in the progression of the disease [16, 59–61]. The specific ligands involved in the four pathways specific to LS skin are expressed specifically in inflammatory fibroblasts (Fig. 5D), and these pathways are enriched in the communication activities of fibroblasts transmitting signals to immune cells in the inflammatory process (Fig. 5E). Although these ligands show specific expression in inflammatory fibroblasts in NL skin conditions (Fig. S4B available online at http://bib.oxfordjournals.org/), scHyper combined with the overall communication network of NL skin did not identify these ligands as significant, indicating that scHyper has the ability to identify the specific intercellular communications between the same tissue in different environmental conditions. Additionally, Syndecan-4 (SDC4), which is expressed on dendritic cells and activated T cells, plays a critical role in dendritic cell movement and has been shown to be a potential target for activated T cells driving disease [62]. In the AD environment, SDC4 is significantly expressed in dendritic cells and T cells, enabling inflammatory immune cells to respond to signals from inflammatory fibroblasts, including THBS, COLLAGEN, and PTN signals (Fig. 5F, Fig. S4C available online at http://bib.oxfordjournals.org/). These signals can regulate cell–matrix interactions and participate in inflammation and immune regulation. In summary, the combined analysis of scHyper for human LS and NL skin can identify major signal changes that may drive the pathogenesis of the disease.

Conclusion

Describing the complex communication networks among various cell types, as well as deciphering context-driven intercellular communications, remains a fundamental challenge. Here, we propose scHyper, a strategy based on hypergraph neural networks that reconstructs the original intercellular communications to reveal potential context-dependent communication relationships. scHyper is an effective method that constructs hypergraph networks for intercellular communications and utilizes hypergraph neural networks to reconstruct high-order interactions to identify context-dependent communication relationships and one of the few methods that use deep learning frameworks to explore intercellular communications. To the best of our knowledge, only a few existing tools, such as Tensor-cell2cell [28] and scTensor [29], model high-order interactions in intercellular communications. Compared to similar methods, scHyper utilizes hypergraph neural networks to process dynamic and heterogeneous data and can better capture the complex high-order relationships between nodes. scHyper can easily identify meaningful biological insights. To explore more biologically meaningful interactions from scRNA-seq data, we constructed a comprehensive set of L-R pairs, including information on L-R multisubunit complexes and signaling pathway annotations, enabling scHyper to effectively infer intercellular communications and signal transduction under physiological and pathological conditions. Additionally, we performed a nonparametric test to identify significant interactions between cell types, thereby reducing the false positive rate. For example, a comparison analysis with other scRNA-seq-based tools for inferring intercellular communications showed that scHyper identified fewer intercellular communications events than other tools, but had a higher accuracy.

Although scHyper has shown excellent performance in intercellular communications analysis, there are still some limitations. First, due to the complexity of hypergraph neural networks, scHyper may be computationally intensive, especially when processing large-scale data. Second, while performing well on a specific dataset, the generalization ability of scHyper still needs to be further validated and improved to adapt to different types of cellular data and experimental scenarios. In the future, we will continue to improve and optimize the scHyper method. On the one hand, we will strive to improve the algorithm’s efficiency and reduce the computational cost to better handle large-scale data. On the other hand, we will further validate and optimize the generalization ability of scHyper and explore its suitability for different types of data sets and experimental scenarios. Additionally, we will explore combining other data analysis techniques and domain knowledge to further enhance the performance and applicability of the scHyper method in intercellular communications analysis. In summary, the scHyper method proposed in this study has demonstrated significant advantages in intercellular communications analysis and has great development potential. By continuously improving and optimizing it, scHyper will provide more reliable and effective analysis tools for intercellular communications research, driving the progress of cell biology and disease research.

Key Points

We propose a global network analysis framework based on heterogeneous hypergraphs and developed new techniques that integrate hypergraph representation learning and nonparametric statistical testing for analyzing intercellular communications in single-cell RNA sequencing data.

In contrast to traditional tools that only measure communication scores between two cell types for specific L-R pairs, our approach constructs heterogeneous graphs and applies hypergraph representation learning to capture complex higher-order interaction patterns between cells, which enables us to identify the potential impact of all cells, ligand, and receptor expression pairs on communication scores.

Nonparametric tests are employed to determine the statistical significance of intercellular communications, aiding in the discovery of biologically meaningful communication relationships.

Supplementary Material

Supplementary_Data_bbae436

Supplementary_data_bbae436

Funding

This work was supported by grants from the Talent Program of Xinjiang Autonomous Region-Youth Outstanding Talent and Youth Innovative Talent (2023TSYCCX0104), the National Natural Science Foundation of China (62362062 and 62273364), and Xinjiang Key Laboratory of Applied Mathematics (NO. XJDX1401).

Conflict of interest: None declared.

Data availability

This paper analyzes existing, publicly available data. The pbmc data can be accessed from the following URL: pbmc3k [https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc3k], pbmc68k [https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0], and pbmcMultiome [https://support.10xgenomic  s.com/single-cell-multiome-atac-gex/datasets/1.0.0/pbmc_granulocyte_sorted_10k?]. The metastatic ovarian cancer dataset can be accessed through GEO: GSE165897. The aqueous immune cell data of human uveitis patients can be accessed through GEO: GSE229166. Human skin data can be accessed through GEO: GSE147424.

Code

The Python package of scHyper is freely available from https://github.com/Lwyonly/scHyper.
==== Refs
References

1. Armingol E , OfficerA, HarismendyO. et al.  Deciphering cell–cell interactions and communication from gene expression. Nat Rev Genet  2020;22 :71–88.33168968
2. Steinhart Z , AngersS. Wnt signaling in development and tissue homeostasis. Development  2018;145 :dev146589.
3. Zoeller T , SchneiderA, KleimeyerC. et al.  Silencing of TGFβ signalling in microglia results in impaired homeostasis, nature. Communications  2018;9 :1–13.
4. Klein R , PasterkampRJ. Recent advances in inter-cellular interactions during neural circuit assembly. Curr Opin Neurobiol  2021;69 :25–32.33383489
5. Suzuki T , HasegawaE, NakaiY. et al.  Formation of neuronal circuits by interactions between neuronal populations derived from different origins in the drosophila visual center. Cell Rep  2016;15 :499–509.27068458
6. Cyster JG , AllenCDC. B cell responses: cell interaction dynamics and decisions. Cell  2019;177 :524–40.31002794
7. Luissint A-C , ParkosCA, NusratA. Inflammation and the intestinal barrier: leukocyte-epithelial cell interactions, cell junction remodeling, and mucosal repair. Gastroenterology  2016;151 :616–32.27436072
8. Shahbazi MN , SiggiaED, Zernicka-GoetzM. Self-organization of stem cells into embryos: a window on early mammalian development. Science  2019;364 :948–51.31171690
9. Ramilowski JA , GoldbergT, HarshbargerJ. et al.  A draft network of ligand-receptor-mediated multicellular signalling in human. Nat Commun  2015;6 , 6 :7866, 7.10.1038/ncomms8866.26198319
10. Almet AA , CangZ, JinS. et al.  The landscape of cell–cell communication through single-cell transcriptomics. Curr Opin Syst Biol  2021;26 :12–23.33969247
11. Dimitrov D , TüreiD, Garrido-RodriguezM. et al.  Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data. Nat Commun  2022;13 :13.10.1038/s41467-022-30755-0.35013168
12. Yuan GC , CaiL, ElowitzM. et al.  Challenges and emerging directions in single-cell analysis. Genome Biol  2017;18 :18.10.1186/s13059-017-1218-y.28126036
13. Liu Z , SunD, WangC. Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information. Genome Biol  2022;23 :23.10.1186/s13059-022-02783-y.35027082
14. Colinge J , LacroixM, FauC. et al.  SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res  2020;48 :e55–5.32196115
15. Efremova M , Vento-TormoM, TeichmannSA. et al.  CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat Protoc  2020;15 :1484–506.32103204
16. Jin S , Guerrero-JuarezCF, ZhangL. et al.  Inference and analysis of cell-cell communication using CellChat. Nat Commun  2021;12 :12.10.1038/s41467-021-21246-9.33397888
17. Noël F , Massenet-RegadL, Carmi-LevyI. et al.  Dissection of intercellular communication using the transcriptome-based framework ICELLNET. Nat Commun  2021;12 :12.10.1038/s41467-021-21244-x.33397888
18. Raredon MSB , YangJC, GarritanoJ. et al.  Computation and visualization of cell-cell signaling topologies in single-cell systems data using connectome. Sci Rep  2022;12 :12.10.1038/s41598-022-07959-x.34996907
19. Hou R , DenisenkoE, OngHT. et al.  Predicting cell-to-cell communication networks using NATMI. Nat Commun  2020;11 :11.10.1038/s41467-020-18873-z.31896763
20. Cheng JY , ZhangJ, WuZD. et al.  Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19. Brief Bioinform  2021;22 :988–1005.33341869
21. Hu YX , PengT, GaoL. et al.  CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data. Sci Adv  2021;7 :7.10.1126/sciadv.abf1356.
22. Browaeys R , SaelensW, SaeysY. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat Methods  2020;17 :159–62.10.1038/s41592-019-0667-5.31819264
23. Li HC , MaTX, HaoMS. et al.  Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics. Brief Bioinform  2023;24 :24.10.1093/bib/bbad359.
24. Dries R , ZhuQ, EngCHL. et al.  Giotto, a toolbox for integrative analysis and visualization of single-cell spatial transcriptomic data. Genome Biol  2021;22 :78.
25. Cang ZX , NieQ. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat Commun  2020;11 :11.10.1038/s41467-020-15968-5.31896763
26. Wang H , ZhaoJ, NieQ. et al.  Dissecting spatiotemporal structures in spatial Transcriptomics via diffusion-based adversarial learning. Research  2024;7 :0390.10.34133/research.0390.
27. Shao X , LiCY, YangHH. et al.  Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk. Nat Commun  2022;13 :13.10.1038/s41467-022-32111-8.35013168
28. Armingol E , BaghdassarianHM, MartinoC. et al.  Context-aware deconvolution of cell-cell communication with Tensor-cell2cell. Nat Commun  2022;13 :13.10.1038/s41467-022-31369-2.35013168
29. Tsuyuzaki K , IshiiM, NikaidoI. Sctensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data. BMC bioinformatics  2023;24 :420.37936079
30. Cillo AR , KuertenCHL, TabibT. et al.  Immune landscape of viral- and carcinogen-driven head and neck cancer. Immunity  2020;52 :183–199.e9.10.1016/j.immuni.2019.11.014.31924475
31. Wang Y , WangR, ZhangS. et al.  iTALK: an R package to characterize and illustrate intercellular communication. bioRxiv  2019;507871.
32. Cohen M , GiladiA, GorkiA-D. et al.  Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell  2018;175 :1031–1044.e18.10.1016/j.cell.2018.09.009.30318149
33. Zhang R , ZouY, MaJ. Hyper-SAGNN: a self-attention based graph neural network for hypergraphs. ICLR, 2020. https://openreview.net/forum?id=ryeHuJBtPH.
34. Zhang Y , LiuT, WangJ. et al.  Cellinker: a platform of ligand-receptor interactions for intercellular communication analysis. Bioinformatics  2021;37 :2025–32.
35. Liu Y , ZhangY, ChangX. et al.  MDIC3: Matrix decomposition to infer cell-cell communication. Patterns (N Y)  2024;5 :100911.38370122
36. Yang Y , LiG, ZhongY. et al.  scTenifoldXct: a semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs. Cell Systems  2023;14 :302–311.e304.36787742
37. He HL , SuryawanshiH, MorozovP. et al.  Single-cell transcriptome analysis of human skin identifies novel fibroblast subpopulation and enrichment of immune subsets in atopic dermatitis. J Allergy Clin Immunol  2020;145 :1615–28.32035984
38. Luo J , DengM, ZhangX. et al.  ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methods. Genome Res  2023;33 :1788–805.37827697
39. Zhang K , ErkanEP, JamalzadehS. et al.  Longitudinal single-cell RNA-seq analysis reveals stress-promoted chemoresistance in metastatic ovarian cancer. Sci Adv  2022;8 :eabm1831.
40. Anderson NM , SimonMC. The tumor microenvironment. Curr Biol  2020;30 :R921–5.32810447
41. Bussard KM , MutkusL, StumpfK. et al.  Tumor-associated stromal cells as key contributors to the tumor microenvironment. Breast Cancer Res  2016;18 :84.
42. Hwang S-M , AwasthiD, JeongJ. et al.  Transgelin 2 guards T cell lipid metabolic programming and anti-tumor function. Res Sq  2023.
43. Galbo PM , ZangXX, ZhengDY. Molecular features of cancer-associated fibroblast subtypes and their implication on cancer pathogenesis, prognosis, and immunotherapy resistance. Clin Cancer Res  2021;27 :2636–47.33622705
44. Gunaydin G . CAFs interacting with TAMs in tumor microenvironment to enhance tumorigenesis and immune evasion. Front Oncol  2021;11 :668349.
45. Kaminska K , SzczylikC, BieleckaZF. et al.  The role of the cell-cell interactions in cancer progression. J Cell Mol Med  2015;19 :283–96.25598217
46. Yoshida GJ . Regulation of heterogeneous cancer-associated fibroblasts: the molecular pathology of activated signaling pathways. J Exp Clin Cancer Res  2020;39 :112.
47. Harjunpää H , AsensML, GuentherC. et al.  Cell adhesion molecules and their roles and regulation in the immune and tumor microenvironment. Front Immunol  2019;10 :1078.
48. Nikolaou S , MacheskyLM. The stressful tumour environment drives plasticity of cell migration programmes, contributing to metastasis. J Pathol  2020;250 :612–23.32057095
49. Siveen KS , PrabhuK, KrishnankuttyR. et al.  Vascular endothelial growth factor (VEGF) signaling in tumour vascularization: potential and challenges. Curr Vasc Pharmacol  2017;15 :339–51.28056756
50. Wang XR , BoveAM, SimoneG. et al.  Molecular bases of VEGFR-2-mediated physiological function and pathological role. Front Cell Dev Biol  2020;8 :599281.
51. Huse M . Mechanical forces in the immune system. Nat Rev Immunol  2017;17 :679–90.28757604
52. Lin JB , PeppleKL, ConcepcionC. et al.  Aqueous macrophages contribute to conserved CCL2 and CXCL10 gradients in uveitis. Ophthalmol Sci  2024;4 :100453–3.38650614
53. Pichi F , CarraiP, SrivastavaSK. et al.  Genetic of uveitis. Int Ophthalmol  2016;36 :419–33.26453112
54. Jin J , ZhaoQJ. Emerging role of mTOR in tumor immune contexture: impact on chemokine-related immune cells migration. Theranostics  2020;10 :6231–44.32483450
55. Lee RW , NicholsonLB, SenHN. et al.  Autoimmune and autoinflammatory mechanisms in uveitis. Semin Immunopathol  2014;36 :581–94.24858699
56. Turner MD , NedjaiB, HurstT. et al.  Cytokines and chemokines: at the crossroads of cell signalling and inflammatory disease. BBA-Mol Cell Res  2014;1843 :2563–82.
57. Du LP , KijlstraA, YangPZ. Immune response genes in uveitis. Ocul Immunol Inflamm  2009;17 :249–56.19657978
58. Wallace GR , CurnowSJ, WlokaK. et al.  The role of chemokines and their receptors in ocular disease. Prog Retin Eye Res  2004;23 :435–48.15219876
59. Mei FF , LiuJJ, WuJT. et al.  Collagen peptides isolated from Salmo salar and Tilapia nilotica skin accelerate wound healing by altering cutaneous microbiome colonization via upregulated NOD2 and BD14. J Agric Food Chem  2020;68 :1621–33.31967468
60. Mirzoeva S , TongX, BridgemanBB. et al.  Apigenin inhibits UVB-induced skin carcinogenesis: the role of Thrombospondin-1 as an anti-inflammatory factor. Neoplasia  2018;20 :930–42.30118999
61. Song YH , ZhuYT, DingJ. et al.  Distribution of fibroblast growth factors and their roles in skin fibroblast cell migration. Mol Med Rep  2016;14 :3336–42.27572477
62. Polte T , PetzoldS, BertrandJ. et al.  Critical role for syndecan-4 in dendritic cell migration during development of allergic airway inflammation. Nat Commun  2015;6 :7554.
