transfer

Due to the high dimensionality and sparsity of the gene expression matrix in single-cell RNA-sequencing (scRNA-seq) data, coupled
with significant noise generated by shallow sequencing, it poses a great challenge for cell clustering methods. While numerous
computational methods have been proposed, the majority of existing approaches center on processing the target dataset itself.
This approach disregards the wealth of knowledge present within other species and batches of scRNA-seq data. In light of this, our
paper proposes a novel method named graph-based deep embedding clustering (GDEC) that leverages transfer learning across species
and batches. GDEC integrates graph convolutional networks, effectively overcoming the challenges posed by sparse gene expression
matrices. Additionally, the incorporation of DEC in GDEC enables the partitioning of cell clusters within a lower-dimensional space,
thereby mitigating the adverse effects of noise on clustering outcomes. GDEC constructs a model based on existing scRNA-seq datasets
and then applying transfer learning techniques to fine-tune the model using a limited amount of prior knowledge gleaned from the
target dataset. This empowers GDEC to adeptly cluster scRNA-seq data cross different species and batches. Through cross-species and
cross-batch clustering experiments, we conducted a comparative analysis between GDEC and conventional packages. Furthermore, we
implemented GDEC on the scRNA-seq data of uterine fibroids. Compared results obtained from the Seurat package, GDEC unveiled
a novel cell type (epithelial cells) and identified a notable number of new pathways among various cell types, thus underscoring the
enhanced analytical capabilities of GDEC.

单细胞RNA测序（single-cell RNA-sequencing，scRNA-seq）数据中的基因表达矩阵兼具高维度与稀疏性特征，加之浅测序产生的大量噪声，给细胞聚类方法带来了极大挑战。尽管目前已提出众多计算方法，但绝大多数现有研究均仅围绕目标数据集本身展开处理，该思路忽略了其他物种及不同批次scRNA-seq数据中蕴藏的海量知识。
鉴于此，本文提出一种名为基于图的深度嵌入聚类（graph-based deep embedding clustering，GDEC）的新型方法，该方法可实现跨物种与跨批次的迁移学习。GDEC整合了图卷积网络，能够有效破解稀疏基因表达矩阵带来的建模难题；此外，通过引入深度嵌入聚类（deep embedding clustering，DEC），GDEC可在低维空间中完成细胞簇的划分，进而削弱噪声对聚类结果的不利影响。
GDEC先基于现有scRNA-seq数据集构建基础模型，随后借助从目标数据集获取的有限先验知识，通过迁移学习技术对模型进行微调，使其能够高效完成跨物种、跨批次的scRNA-seq数据聚类任务。通过跨物种与跨批次聚类实验，本文对GDEC与传统分析工具包进行了对比分析。此外，我们将GDEC应用于子宫肌瘤的scRNA-seq数据中，相较于Seurat工具包的分析结果，GDEC不仅揭示了一种全新的细胞类型（上皮细胞），还在各类细胞中识别出大量此前未被发现的信号通路，充分彰显了GDEC更出色的数据分析能力。

搜集汇总

背景与挑战

背景概述

该数据集用于支持一种名为GDEC的图基深度嵌入聚类方法，该方法利用迁移学习来处理跨物种和批次的单细胞RNA测序数据，以解决其高维稀疏性和噪声问题。通过与Seurat等传统工具对比，GDEC在子宫肌瘤数据中成功识别出了新的上皮细胞类型和更多细胞间通路，展现了其更优的分析能力。