Abstract
The analysis of singlecell RNA sequencing data is of great importance in health research. It challenges data scientists, but has enormous potential in the context of personalized medicine. The clustering of single cells aims to detect different subgroups of cell populations within a patient in a datadriven manner. Some comparison studies denote singlecell consensus clustering (SC3), proposed by Kiselev et al. (Nat Methods 14(5):483–486, 2017), as the best method for classifying singlecell RNA sequencing data. SC3 includes Laplacian eigenmaps and a principal component analysis (PCA). Our proposal of unsupervised adapted singlecell consensus clustering (adaSC3) suggests to replace the linear PCA by diffusion maps, a nonlinear method that takes the transition of single cells into account. We investigate the performance of adaSC3 in terms of accuracy on the data sets of the original source of SC3 as well as in a simulation study. A comparison of adaSC3 with SC3 as well as with related algorithms based on further alternative dimension reduction techniques shows a quite convincing behavior of adaSC3.
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1 Introduction
Personalized medicine based on genomic data promises the precise and individualized treatment of diseases using information from a patient’s genome (Cho et al. 2012). There is tremendous research interest in this field, especially with regard to cancer. Hereby it is of interest to determine the different stages of cancer as well as the understanding of the complex development of organs for instance, by analyzing the single cells that are obtained from the singlecell RNA sequencing. Datadriven approaches have led to projects such as The Human Cell Atlas (2020), which aims to establish an interpretable structure for the different cell types of single cells and serves as an orientation for the study of diseases. The mission of the Human Cell Atlas is “(t)o create comprehensive reference maps of all human cells—the fundamental units of life—as a basis for both understanding human health and diagnosing, monitoring, and treating disease.” Based on the genetic profiles of these singlecell RNA sequencing data, an unsupervised classification allows a datadriven distinction of intra and intertumoral heterogeneities as well as the determination of different pathways during the development (Duò et al. 2018). The approach of singlecell consensus clustering (SC3) by Kiselev et al. (2017) has gained much attention, not only due to its superior performance in the comparison study of Duò et al. (2018). SC3 is also explicitly tailored to single cell data. Nevertheless, the original incorporation of the linear dimension reduction of principal component analysis may offer a potential for improvement. Following Bendall et al. (2014) and Buettner and Theis (2012), for single cells the transition from one state to another is a nonlinear continuous process. Therefore, we propose to replace the PCA of the SC3 method by diffusion maps (Haghverdi et al. 2015), resulting in a new unsupervised algorithm, which we call adapted singlecell consensus clustering (adaSC3). The use of diffusion maps is not only motivated by the biological behavior of single cells but is also supported empirically: First, diffusion maps allow for a natural modeling of the transition of single cells by Markov processes. Secondly, according to Haghverdi et al. (2015), when applying diffusion maps to single cell data, they also seem to perform best compared to other nonlinear transformation methods such as independent component analysis, Kernel PCA, Isomap, or Hessian Local Linear Embedding.
Our paper ist structured as follows: We first give an introduction into singlecell RNA sequencing data in Sect. 2. In Sect. 3, we present the methodological background, starting in Sect. 3.1 with the proposed adapted singlecell consensus clustering (adaSC3), as well as some related competing methods. In Sect. 3.2, we focus on the special suitability of diffusion maps, included in our framework of adaSC3. Analyzing some characteristic singlecell RNA sequencing data sets, introduced already in Sect. 2, adaSC3 is compared to its competing methods in Sect. 4. The performance of adaSC3 and its competing methods is further evaluated with partitionwise simulation data in Sect. 5. Section 6 concludes with a brief discussion and outlook.
2 Singlecell RNA sequencing data (ScRNASeq data)
The health and development of living organisms can heavily be impacted by the kind and activity of their genes, referred to as gene expression. With the technique of singlecell RNA sequencing (scRNAseq) introduced by Tang et al. (2009), it is possible to measure the gene expression for single cells. In scRNAseq, genomic profiles are measured in terms of read counts, that is the number of small sequences (“reads”) that result from a cell’s RNA that can be identified as belonging to a particular gene. A data set comprising N cells and G genes will hence be a \(N \times G\) matrix containing nonnegative integers (including zero). The scRNAseq data of Biase et al. (2014), Deng et al. (2014), Goolam et al. (2016) and Yan et al. (2013), provided by the Hemberg Group of the Sanger Institute (2020), contain read counts of single cells of a mouse or a human with different cell states that are passed during differential transcription for targeting the analysis of cell division in a pedigree. Gene expression is stochastic, and often the reads follow different distributions for different cell types. Another topic of interest is the examination of having reached varying pathway stages. For example, the experiments of Treutlein et al. (2014) were carried out to investigate cell transition during lung development. In detail, these experiments aim to analyze the development of the distal lung epithelium of the mouse based on the different transcriptional states.
The data sets shown in Table 1 had been used for the evaluation of SC3 by Kiselev et al. (2017). Fulfilling the reproducibility and the sample size criterion for the unsupervised classification leads to the data situation^{Footnote 1} described in Table 1. Except for the data set of Biase et al. (2014), the distributions of cell types are quite unbalanced encompassing between 80 and 269 single cells.
3 Methods
In this section, we first present the proposed adapted singlecell consensus clustering (adaSC3), which allows, by replacing PCA with diffusion maps, to take the varying pathways and their different transcriptional states into account. Furthermore, we consider several SC3like approaches that later serve as competitors including different transformation techniques.
In the second part of this section, we look more closely at diffusion maps, which allow an appropriate embedding of the complex data structure of single cells in the transformation step of adaSC3.
3.1 Unsupervised adapted singlecell consensus clustering (adaSC3) and its competitors
Singlecell consensus clustering aims at classifying the gene expression of single cells in an unsupervised way such that groups are determined in a datadriven manner for detecting new subgroups or confirming manually determined cell types. The classification process subdivides the cell population with regard to the homogeneity of the genetic profile into subgroups of single cells, which represent, for example, different stages of a disease or of a development process within a patient or within a mouse. The original SC3 is implemented in the software R (R Core Team 2020) and can be described as a pipeline consisting of several transformation steps including an automatic dimension reduction, resulting in a clustering respecting all combinations. For the construction of the adapted singlecell consensus clustering (adaSC3) and its competitors, we rely on the same principle framework as SC3 but consider different transformations.^{Footnote 2} The concrete procedure of adaSC3 consists of the following steps:

1.
Preprocessing As a result of the scRNAseq process, one obtains the gene expression matrix E containing the read counts of N single cells and G genes. As a preprocessing step, the original matrix E is reduced by a gene filter, as proposed in the original work, that aims to exclude rare and omnipresent genes.^{Footnote 3} This leads to the expression matrix \(E'\) of dimension \({N \times G'}\).

2.
Calculation of distance matrix D Based on the expression matrix \(E'\), the Euclidean distance matrix is constructed for each pairwise single cell combination. Furthermore, two measures of dissimilarity are applied on the logtransformed data of \(E'\) using the Pearson and the Spearman correlation, respectively. For the sake of simplicity, the obtained distance and dissimilarity matrices will each be referred to as distance matrix D.

3.
Transformation technique T For each of the obtained distance matrices D, we apply Laplacian eigenmaps^{Footnote 4}, introduced by Belkin and Niyogi (2003) as proposed in the original SC3. In addition, we apply diffusion maps, described in more detail below, instead of the originally proposed principal component analysis. To each of the six combinations, consisting of three different distance matrices D and two transformation techniques, an eigenvalue decomposition is applied. This leads in total to \(3 \times 2\) = 6 different eigenvalue decompositions, resulting each in respectively \(N1\) eigenvectors \(\psi _1, \ldots , \psi _{N1}\) with their ordered eigenvalues \(1 > \lambda _1 \ge \cdots \ge ~\lambda _{N1}\).

4.
Consensus clustering In accordance to SC3, we adopt the automatic selection of the number of eigenvectors to be considered. For each eigenvalue decomposition, a kmeans clustering, with k being deterministic, representing the number of categories of the underlying cell types as proposed in the original paper of Kiselev et al. (2017), is conducted. The automatic selection of eigenvectors of the described scenario starts incorporating the first eigenvector until the rounded integer of the 4% quantile of the set \(\{1, \ldots , N\}\). The subsequent clusterings include each one further eigenvector until the 7% quantile is reached for including the maximal range of eigenvectors for the last clustering of each combination. The result of each kmeans clustering m is summarized in a consensus matrix \(\mathcal {C}\), indicating the relative frequency of how often a pair of single cells is grouped together over all \(n_m\) clusterings. Based on the obtained consensus matrix \(\mathcal {C}\), a final completelinkage clustering is performed. It aims to achieve higher performance and a more robust result for the classification of single cells, leading to the final grouping of k subgroups. The quality of clustering is evaluated expost by the Adjusted Rand Index (ARI) as proposed in the original work of Kiselev et al. (2017).
Apart from the original SC3 that includes a PCA and Laplacian eigenmaps as transformation techniques, we construct further competing algorithms following the same principle as of adaSC3. Instead of diffusion maps and Laplacian eigenmaps, we propose additional algorithms leading to two different types of constructions, differing in the number of incorporated transformations. The first construction only uses one single transformation technique T in Step 3 of adaSC3. We therefore analyze the influence of the nonlinear manifolds of isomaps (IM), locally linear embedding (LLE) as well as the multidimensional scaling (MDS), in addition to the transformation of the principal component analysis (PCA), Laplacian eigenmaps (LE), and diffusion maps (DM), each on their own.^{Footnote 5} The second type of construction consists of the combination of each mentioned transformation T with Laplacian eigenmaps. This leads to the algorithms named by the incorporated transformations, resulting in LE + IM, LE + LLE and LE + MDS, in addition to the original SC3 (PCA + LE) and adaSC3 (DM+LE).
3.2 Diffusion maps
In the following, we describe the motivation of embedding the complex structure of single cells during transition into an appropriate global nonlinear manifold, using diffusion maps. As stated in the introduction of Angerer et al. (2016), diffusion maps allow the reconstruction of the different states that are connected via different transitions. One possible transition is the mutation of one single cell into another. For the following construction of diffusion maps, we only consider the transition of one single cell into another within one step. Another decisive fact is the robustness of diffusion maps to noise. Furthermore, with the normalization, described in the following, diffusion maps are able to detect lowly represented cell types. Coifman and Lafon (2006) provide the general framework of diffusion maps that can be adapted to single cells following (Angerer et al. 2016) that is presented in the next steps. For the construction of diffusion maps, consider two states \(x, y \in \varOmega \), with \(\varOmega \) as the appropriate state space. x and y represent single cells; their gene expressions, measured by count data, lead to the pairwise distance D(x, y).

1.
For each choice of the distance measure D, each point (single cell) is considered as a node of a symmetric graph with weight function \(K_D\)
$$\begin{aligned} K_D(x, y) = \exp \left(  \frac{D(x,y)}{2\alpha ^2}\right) , \end{aligned}$$indicating the affinity of a pair of single cells with scale parameter \(\alpha \), reflecting the best size of the included neighborhood.^{Footnote 6}

2.
In the following, we construct the core of a transition kernel of a Markov chain
$$\begin{aligned} P_D(x, y) = \frac{K_D(x,y)}{Z(x)}, \text {with} Z(x) = \sum _{y \in \varOmega } K_D(x,y)\,. \end{aligned}$$ 
3.
With a density interpretation of the upper term, the following density normalized transition probability matrix
$$\begin{aligned} \tilde{P}_D(x, y) = \frac{1}{\tilde{Z}(x)} \frac{K_D(x,y)}{Z(x)Z(y)}, \text {with} \tilde{Z}(x) = \sum _{y \in \varOmega \setminus x} \frac{K_D(x,y)}{Z(x)Z(y)} \end{aligned}$$can be obtained. As the research question consists of mapping the differentiation behavior of single cells, we are only interested in the transition between single cells. Thus, the diagonal of \(\tilde{P}_D(x, y)\) is set to zero, and the normalization is adapted appropriately, summing up only the gene expression of differing pairs of single cells with \(y \ne x\).

4.
Based on the normalized matrix \(\tilde{P}_D\), indicating the transition of one state to another by an ergodic Markovian diffusion process, the aimed transformation is obtained.
4 Results
In this section, we evaluate the clustering performance of adaSC3 and its competitors. The accuracy of combining each of the mentioned transformations in combination with Laplacian eigenmaps is illustrated in Table 2.
AdaSC3 leads in three out of five cases (Deng et al., Treutlein et al. and Yan et al.) to better clustering results, compared to the original SC3, and it is identical in the case of the data set of Biase et al.. Concerning the competing algorithms, the combinations of LE with IM and MDS tend to deliver worse results compared to adaSC3. However, LE + LLE achieves two times the best performing classification but fails extremely in the case of the Treutlein et al. data set. The slightly worse performance of adaSC compared to SC3 concerning the complete Goolam et al. data set of Table 2 should not be overinterpreted as the resampling results based on leaving out each single cell once, we reach considerably higher performance compared to SC3. Furthermore, we can state that adaSC3 delivers the highest overall performance concerning both the resampling study as well as using only one transformation technique as illustrated in the Supplementary Material. We therefore consider our proposal as generally the best approach among its competitors, of SC3 and its related approaches, based on the benchmarking data sets. This result is especially surprising as the scRNAseq data sets were originally used for determining the proposed default settings of SC3, such as e.g. the automatic choice for the lower dimension.
5 Simulation data
The classification accuracy of the simulation data is investigated in the same way as the scRNAseq data. We are interested in the consensus clustering accuracy of two simulation groups, which are constructed with different ranges of distribution parameters describing the read counts. With shifted parameter ranges, one can consider the simulation groups as representing a healthy and a diseased population. According to the literature, the use of a zeroinflated negative binomial (ZINB) distribution is recommended as the most adequate approximate distribution for modeling the read counts of single cells. It allows larger variability of read counts compared to the former used Poisson distribution (see e.g. Wagner et al. 2013). Based on the constructed simulation data following a (generalized version of a) ZINB distribution for the expression of each gene, we will investigate the influence of various parameters describing each gene for all possible group partitions for a fixed total number N of single cells.
5.1 Construction of simulation data
The ZINB distribution (Kleiber and Zeileis 2016) is a mixture between a negative binomial probability mass function and a point mass at zero. For generating ZINB distributed simulation data we use the R package emdbook (Bolker B, Bolker Maintainer Ben and Imports, MASS 2020), which is based on a generalization of the negative binomial (NB) distribution with parameters \(\mu \) and \(\phi \) for the nonzero inflated part.^{Footnote 7} The parameter \(\mu \) is a continuous positive real value, describing the mean. The dispersion parameter \(\phi \) represents the shape parameter of the gamma distribution underlying the generalization of the NB. The fraction of zeroinflation is taken into account by the parameter \(\pi \).
In the following scenarios, we investigate the influence of different parameters of the distribution family. In order to mimic a realistic situation, the scRNAseq data of Kolodziejczyk et al. (2015) is taken for estimating the parameters of a ZINB distribution^{Footnote 8} and allow the construction of ranges for each parameter looking at the shifted quantiles of the estimates of the parameters \(\mu \) and \(\phi \). This leads to the parameter ranges \(\varvec{\mathcal {M}}^{(1)}\) and \(\varvec{\varPhi }^{(1)}\) for cell population 1 and \(\varvec{\mathcal {M}}^{(2)}\) and \(\varvec{\varPhi }^{(2)}\) for cell population 2. The parameter range \(\varvec{\Pi }\) for \(\pi \) is set to be the same for both populations.^{Footnote 9} Thus, the simulated read counts of each gene follow a ZINB(\(\mu _1, \phi _1, \pi _1\)) distribution for simulation group 1 and a ZINB(\(\mu _2, \phi _2, \pi _2\)) for simulation group 2, according to the following scenarios:
\(\varvec{\cdot }\) Simulation scenario (a) for different ranges of \(\mu \):
\(\mu _1 \in \varvec{\mathcal {M}}^{(1)}\) and \(\mu _2 \in \varvec{\mathcal {M}}^{(2)},\) \(\phi _1 =\phi _2 \in \varvec{\varPhi }^{(2)},\) \(\pi _1 =\pi _2 \in \varvec{\Pi }\)
\(\varvec{\cdot }\) Simulation scenario (b) for different ranges of \(\phi \):
\(\mu _1 = \mu _2 \in \varvec{\mathcal {M}}^{(2)},\) \(\phi _1 \in \varvec{\varPhi }^{(1)}\) and \(\phi _2 \in \varvec{\varPhi }^{(2)},\) \(\pi _1= \pi _2 \in \varvec{\Pi }\)
\(\varvec{\cdot }\) Simulation scenario (c) for different ranges of \(\mu \) and \(\phi \):
\(\mu _1 \in \varvec{\mathcal {M}}^{(1)}\) and \(\mu _2 \in \varvec{\mathcal {M}}^{(2)},\) \(\phi _1 \in \varvec{\varPhi }^{(1)}\) and \(\phi _2 \in \varvec{\varPhi }^{(2)},\) \(\pi _1 = \pi _2 \in \varvec{\Pi }\)
\(\varvec{\cdot }\) Simulation scenario (d) for the same range of \(\mu \) and \(\phi \):
\(\mu _1 = \mu _2 \in \varvec{\mathcal {M}}^{(2)},\) \(\phi _1 = \phi _2 \in \varvec{\varPhi }^{(2)},\) \(\pi _1 = \pi _2 \in \varvec{\Pi }\)
For each of the simulation scenarios (a) to (d), we sample \(N_1\) times out of ZINB(\(\mu _1\) \(\phi _1\), \(\pi _1)\) and \(N_2\) times out of ZINB(\(\mu _2\), \(\phi _2\), \(\pi _2\)) such that, for comparison purposes, the genespecific parameters remain the same when generating all possible partitions of \(N_1{:}N_2\) (with \(N_1 \,{+}\, N_2 \,{=} \,N\)), starting with \(1{:}(N1)\) until \((N1){:}1\), with \(N \,{=}\, 50\) for the respective scenario. In order to obtain simulation data with the dimension \(N \times G\) for each partition, we repeat this procedure 200 times. Thus, read counts of \(G = 200\) genes are generated with the new parameter values drawn uniformly from the respective intervals.
5.2 Clustering performance based on simulation data
The following plots show how the group partition (xaxis) of a combination of two transformation methods influences the clustering accuracy, measured by the Adjusted Rand Index (ARI) (yaxis). In the ideal case, the accuracy is 1 for each of the partitions, which would indicate that the classification perfectly corresponds to the underlying group allocation. This criterion is best met for adaSC3, not only in the case of using only one transformation technique (see Supplementary Material), but also in combination of those with Laplacian eigenmaps (LE) for simulation scenarios (a) to (c). Simulation (d) serves as a reference where no difference in the genespecific parameters was simulated and no accurate grouping should be detected. Each partition of each scenario is repeated 10 times and the accuracy of the respective clustering results is visualized by boxplots. Results of simulation scenario (c) and (d) can be found in the Supplementary Material.
5.2.1 Simulation scenario (a): variation in expectation parameter \(\mu \)
In the case of differing parameter \(\mu \) represented in scenario (a), adaSC3 seems to perform best among the combined methods (see Fig. 1) as well as compared to each method on its own. It can also be seen that the inter quantile range of boxplots have the tendency to be shorter for adaSC3 and reach higher ARI values compared to its competitors. Therefore, we conclude in general that our approach generates a more efficient clustering allocation. Furthermore, it should also be noted that the performance of the method adaSC3 seems to be better when more single cells are part of simulation group 1 compared to simulation group 2. This might allow the interpretation that the detection of true positives achieves higher accuracy compared to the detection of false negatives, given the diseased population has on average higher gene expression.
5.2.2 Simulation scenario (b): variation in size parameter \(\phi \)
For partitionwise created simulation data differing in the parameter \(\phi \) one can state, referring to Fig. 2, that apart from adaSC3 the combination of LE + LLE performs quite well, too. However, this method needs more partitions before it starts detecting the difference in the simulation groups and fails drastically earlier compared to adaSC3. For approximately balanced data, LE + LLE often leads to worse results. The tendency that the clustering performance depends on the partitions can be confirmed over all methods for simulation scenarios (a) to (c), in which adaSC3 is affected the less.
With regard to scenario (c), the simulated differences of both parameters \(\mu \) and \(\phi \) lead to a quite accurate classification for most methods with an overall superiority of adaSC3, representing the scenario being the closest to the reality. For the simulation design with no difference in the simulation groups, the allocation of single cells is as expected and represents random allocation.
6 Conclusions
The approach of adapted singlecell consensus clustering (adaSC3) is tailored to the clustering of single cells. It reflects the biological structure of single cells by including diffusion maps with the aim to respect the transition process of the underlying data. Indeed, the inclusion of diffusion maps instead of the originally proposed PCA led to a better clustering performance. We consider adaSC3 to be the best method compared to all investigated competitors, both in the analyzed scRNAseq data as well as in the simulation study. This motivates further research that takes into account the biological basis of the data before constructing or combining some methods, as this could be rewarded both in terms of interpretation and accuracy, as shown in this paper.
Based on the discovery that balanced data seems to be detected correctly with higher quality, the distribution of classified classes could be taken into account for an unsupervised evaluation. Furthermore, studies of additional scRNAseq data and further simulations are needed to reinforce the results of this paper. This is especially due to the fact that adaSC3 was evaluated on the same scRNAseq data used for the development of the original SC3 method. This makes the overall superiority of adaSC3 over SC3 even more surprising, while on the other hand some bias of these data sets favoring SC3like methods cannot be excluded.
Notes
Since in the SC3 framework, as described in the transformation step of adaSC3 later, components in higher dimensions are chosen randomly, it is important to focus on data sets with a small amount of single cells, in order to keep the analysis replicable. Moreover, for providing the same data situation as Kiselev et al. (2017), we had to adapt the data set of Biase et al. (2014) such that the number of single cells corresponds to the data description of the original SC3 paper.
AdaSC3 and its competitors are also implemented in R and are available from the first author upon request.
Genes with an expression value of \(>2\) in less than 6% of the cells as well as genes with a positive expression value in more than 94% of the single cell population are excluded.
That are implemented as a spectral embedding in the Python software library Scikitlearn (Pedregosa et al. 2011) as the best size of the neighborhood for the aimed embedding.
For the construction of IM and LLE, Kayo (2006) proposes to use for IM and LLE the same estimate for the optimal neighborhood size, implemented by the R function calc_k of the R package lle (Diedrich et al. 2012). Isomaps are then constructed using the R package \(\mathtt {vegan}\) (Oksanen et al. 2019); the locally linear embedding further relies on the R package \(\mathtt {lle}\) (Diedrich et al. 2012) and MDS is based on the R package \(\mathtt {stats}\) (R Core Team 2020).
The estimation of \(\alpha \) relies on the methods implemented in the R package destiny (Angerer et al. 2016).
Details explaining the generalization of the negative binomial distribution function based on a mixture of Poisson distributions with gamma distributed Poisson rates can be found e.g. in Fuetterer et al. (2019). They investigate the influence of different heterogeneity degrees of count data using simulation data as well as up and downwardly distorted measurements via the ZINB distribution describing the case of measurements tending to lower read counts and upper read counts.
The manual construction of two cell populations rely on the differentially cultured murine embryonic stem cell populations “2i” and “serum” for each of the 38.616 genes.
The constructed parameter ranges are part of the Supplementary Material.
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Acknowledgements
We are very grateful to the two anonymous referees and the editors for their stimulating and constructive comments. We also want to thank Florian Pfisterer for discussions about diffusion maps. Furthermore, the public data sharing of the scRNAsequencing data by the Hemberg Group of the Sanger Institute is gratefully acknowledged. In addition, the first author is very thankful to the LMUMentoring program, connecting young researchers with experienced researchers and providing financial support.
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Fuetterer, C., Augustin, T. & Fuchs, C. Adapted singlecell consensus clustering (adaSC3). Adv Data Anal Classif 14, 885–896 (2020). https://doi.org/10.1007/s11634020004281
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DOI: https://doi.org/10.1007/s11634020004281
Keywords
 Diffusion maps
 Nonlinear embedding
 Singlecell consensus clustering
 Simulation data
 Singlecell RNA sequencing data