单细胞RNA-seq数据聚类：细胞类型鉴定与特征研究综述
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“这篇论文是关于单细胞RNA测序数据聚类在细胞类型识别和特征鉴定中的应用的综述。文章作者来自香港城市大学计算机科学系，主要探讨了单细胞RNA-seq技术的进步如何促进高通量方式下的单细胞转录组学分析，以及无监督学习如数据聚类在识别和表征新细胞类型及基因表达模式中的核心作用。”
在过去的几年里，单细胞RNA测序（scRNA-seq）技术取得了显著的进步，它允许我们在单细胞水平上以高通量的方式进行大规模转录组学分析。这种技术的发展极大地推动了生物学研究，特别是在理解细胞异质性和揭示细胞分化路径方面。无监督学习，尤其是数据聚类，已成为识别和表征新型细胞类型的关键工具。
在该研究中，作者详细回顾了现有的单细胞RNA-seq数据聚类方法，深入剖析了各种方法的优势和限制。这些方法通常包括基于距离、密度、层次结构以及模型驱动的方法。作者强调了在进行聚类分析前对scRNA-seq数据进行预处理的重要性，这包括质量控制（去除低质量读段和异常细胞）、标准化（消除不同样本间的测序深度差异）以及降维（减少数据复杂性，提取关键特征）等步骤。
此外，作者还对一些流行的scRNA-seq聚类方法进行了性能比较实验，这些方法可能包括Seurat、Scanpy、Cell Ranger等。实验通常通过模拟数据或真实scRNA-seq数据集来评估聚类的准确性和稳定性，考察它们在发现细胞群、维持细胞类型结构以及识别稀有细胞类型方面的表现。
通过对这些方法的评估，作者指出，选择适当的聚类方法取决于实验设计、数据质量和研究目标。例如，有些方法可能在处理大量细胞时表现出色，而其他方法则可能更适合识别小规模数据集中的复杂结构。此外，作者也提到了未来的研究方向，如开发更适应scRNA-seq数据特性的新型聚类算法，以及结合生物学先验知识进行有监督或半监督学习的策略。
这篇综述提供了关于如何利用scRNA-seq数据聚类进行细胞类型识别和特征鉴定的全面指南，对于生物信息学领域的研究人员和生物医学科学家来说，是一份重要的参考文献。通过理解并应用文中介绍的方法和技术，科学家们能够更好地解析复杂组织的细胞组成，进一步推动我们对生命系统理解的边界。
Review of Single-cell RNA-seq Data Clustering
to a lower-dimensional space using dimension reduction
that can improve and refine the clustering results. In this
section, we review several commonly used dimension re-
duction methods including principal component analysis, t-
distributed stochastic neighbor embedding algorithm, deep
learning models, and others.
2.3.1. PCA
Principal Component Analysis (PCA) is a typical linear
projection method that projects a set of possibly correlated
variables into a set of linearly orthogonal variables (prin-
cipal components). Due to its conceptual simplicity and
efficiency, PCA has been widely used in single-cell RNA-
seq processing (Jiang et al., 2016a; Buettner et al., 2015;
Shalek et al., 2014; Usoskin et al., 2015; zurauskiene and
Yau, 2016; Kiselev et al., 2017). Notably, SC3 (Kiselev
et al., 2017) applied PCA to transform the distance matrices
as the input of consensus clustering; Shalek et al. (2014)
used PCA for single-cell RNA-seq data spanning several ex-
perimental conditions. In addition, some extended and im-
proved PCA-based methods have been developed including
pcaReduce (zurauskiene and Yau, 2016) which applied PCA
iteratively to provide low-dimensional principal component
representations; Usoskin et al. (2015) proposed an unbiased
iterative PCA-based process to identify distinct large-scale
expression data patterns. However, PCA cannot capture
the nonliner relationships between cells because of the high
levels of dropout and noise (Kiselev et al., 2019).
2.3.2. t-SNE
t-distributed Stochastic Neighbor Embedding (t-SNE)
is the most commonly used nonlinear dimension reduction
method which can uncover the relationships between cells.
t-SNE converts data point similarity into probability and
minimizes Kullback-Leibler divergence by gradient descent
until convergence. In single-cell RNA-seq data analysis, t-
SNE has become a cornerstone of dimension reduction and
visualization for high-dimensional single-cell RNA-seq data
(Linderman et al., 2019; Lin et al., 2017b; Butler et al., 2018;
Haghverdi et al., 2018; Ntranos et al., 2016; Prabhakaran
et al., 2016; Zeisel et al., 2015; Zhang et al., 2018; Li et al.,
2017). Especially, Linderman et al. (2019) developed a fast
interpolation-based t-SNE that dramatically accelerates the
processing and visualization of rare cell populations for large
datasets. Nonetheless, the limitations of t-SNE include the
loss function is non-convex which can lead to different local
optimality; the parameters in t-SNE are required to be tuned.
2.3.3. Deep lear ning models
In recent years, deep learning models (neural networks
and variational auto-encoders) have shown superior perfor-
mance in interpenetrating complex high-dimensional data.
SCNN (Lin et al., 2017a) tested various neural networks
architectures and incorporated prior biological knowledge
to obtain the reduced dimension representation of single
cell expression data. SCVIS (Ding et al., 2018) and VASC
(Wang and Gu, 2018) are both based on variational auto-
encoders which can capture nonlinear relationships between
cells and visualize the low-dimensional embedding in single-
cell gene expression data. Up to now, those methods demon-
strated superior ability of interpretation and compatibility on
high-dimensional single-cell RNA-seq data.
2.3.4. Other methods
In addition, there are also other dimensional reduction
methods such as CIDR (Lin et al., 2017b) applied principal
coordinate analysis that preserves the distance information
in low-dimension space from its high-dimension space; Seu-
rat (Butler et al., 2018) is a toolkit for analysis of single
cell RNA sequencing data and provides many dimension
reduction methods such as PCA and t-SNE. Uniform Mani-
fold Approximation and Projection (UMAP) (Mcinnes et al.,
2018) is a widely used technique for dimension reduction.
UMAP provides increased speed and better preservation of
data global structure for high dimensional datasets. It has
been verified that it outperforms t-SNE (Becht et al., 2019).
3. Clustering methods for single-cell RNA-seq
Diverse types of clustering methods have been devel-
oped for detecting cell types from single-cell RNA-seq data.
Those methods can be roughly classified into four cate-
gories including k-means clustering, hierarchical clustering,
community-detection-based clustering, and density-based
clustering. We review several computational applications
of those clustering methods with their strengths and limita-
tions. Table 1 illustrates the overview of the state-of-the-arts
clustering methods on single-cell RNA-seq data.
3.1. 𝑘-means clustering
𝑘-means clustering is the most popular clustering ap-
proach, which iteratively finds a predefined number of 𝑘
cluster centers (centroids) by minimizing the sum of the
squared Euclidean distance between each cell and its closest
centroid. In addition, it is suitable for large datasets since
it can scale linearly with the number of data points (Lloyd,
1982).
Several clustering tools based on 𝑘-means have been
developed for interpreting single-cell RNA-seq data. SAIC
(Yang et al., 2017) utilized an iterative 𝑘-means clustering to
identify the optimal subset of signature genes that separate
single cells into distinct clusters. pcaReduce (zurauskiene
and Yau, 2016) is a hierarchical clustering method while
it relies on 𝑘-means results as the initial clusters. RaceID
(Grün et al., 2015) applied 𝑘-means to unravel the hetero-
geneity of rare intestinal cell types (Tibshirani et al., 2001).
However, 𝑘-means clustering is an greedy algorithm
that may fail to find its global optimum; the predefined
number of clusters 𝑘 can affect the clustering results; and
another disadvantage is its sensitivity to outliers since it
tends to identify globular clusters, resulting in the failures
in detecting of rare cell types.
To overcome the above drawbacks, SC3 (Kiselev et al.,
2017) integrated individual 𝑘-means clustering results with
different initial conditions as the consensus clusters. RaceID2
S. Zhang et al. Page 3 of 12
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