Supervised capacity preserving mapping: a clustering guided visualization method for scRNA-seq data.
Supervised capacity preserving mapping: a clustering guided visualization method for scRNA-seq data.
复制标题
保存映射的监督能力：用于SCRNA-SEQ数据的聚类指导性可视化方法。
DOI：
10.1093/bioinformatics/btac131
复制
发表时间：
2022-04-28
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
scRNA-seq技术的快速发展使我们能够在细胞水平上大规模探索转录组。最近，已经开发了各种计算方法来分析scRNAseq数据，例如聚类和可视化。然而，目前的可视化方法，包括t-SNE和UMAP，是有限的准确性，呈现不同的功能状态的人口的几何关系的挑战。大多数可视化方法都是无监督的，从聚类结果或给定的标签中遗漏了信息。这导致对真正功能状态之间距离的不准确描述。特别地，UMAP和t-SNE对于保持全局几何结构不是最优的。它们可能导致一个矛盾，即在嵌入维度中距离近的簇实际上在原始维度中更远。此外，UMAP和t-SNE不能跟踪聚类的方差。通过t-SNE和UMAP的嵌入，聚类的方差不仅与真实方差相关，而且与样本大小成正比。我们提出了supCPM，一个强大的监督可视化方法，分离不同的集群，保持全局结构和跟踪集群的方差。与六种基于合成数据集和真实的数据集的可视化方法相比，supCPM在保持全局几何结构和数据方差方面表现出了较好的性能.总体而言，supCPM提供了一个增强的可视化管道，以帮助解释功能转变并准确描述种群隔离。R包和源代码可以在https://zenodo.org/record/5975977#.YgqR1PXMJjM上获得。 补充数据可在Bioinformatics在线获得。
The rapid development of scRNA-seq technologies enables us to explore the transcriptome at the cell level on a large scale. Recently, various computational methods have been developed to analyze the scRNAseq data, such as clustering and visualization. However, current visualization methods, including t-SNE and UMAP, are challenged by the limited accuracy of rendering the geometric relationship of populations with distinct functional states. Most visualization methods are unsupervised, leaving out information from the clustering results or given labels. This leads to the inaccurate depiction of the distances between the bona fide functional states. In particular, UMAP and t-SNE are not optimal to preserve the global geometric structure. They may result in a contradiction that clusters with near distance in the embedded dimensions are in fact further away in the original dimensions. Besides, UMAP and t-SNE cannot track the variance of clusters. Through the embedding of t-SNE and UMAP, the variance of a cluster is not only associated with the true variance but also is proportional to the sample size. We present supCPM, a robust supervised visualization method, which separates different clusters, preserves the global structure and tracks the cluster variance. Compared with six visualization methods using synthetic and real datasets, supCPM shows improved performance than other methods in preserving the global geometric structure and data variance. Overall, supCPM provides an enhanced visualization pipeline to assist the interpretation of functional transition and accurately depict population segregation. The R package and source code are available at https://zenodo.org/record/5975977#.YgqR1PXMJjM. Supplementary data are available at Bioinformatics online.
登录
查看更多内容
影响因子：
46.9
作者：
Becht, Etienne;McInnes, Leland;Newell, Evan W.
通讯作者：
Newell, Evan W.
影响因子：
64.5
作者：
Levine JH;Simonds EF;Bendall SC;Davis KL;Amir el-AD;Tadmor MD;Litvin O;Fienberg HG;Jager A;Zunder ER;Finck R;Gedman AL;Radtke I;Downing JR;Pe'er D;Nolan GP
通讯作者：
Nolan GP
影响因子：
48
作者：
Sandberg, Rickard
通讯作者：
Sandberg, Rickard
影响因子：
48
作者：
Kiselev, Vladimir Yu;Kirschner, Kristina;Hemberg, Martin
通讯作者：
Hemberg, Martin
影响因子：
8
作者：
Barshan, Elnaz;Ghodsi, Ali;Jahromi, Mansoor Zolghadri
通讯作者：
Jahromi, Mansoor Zolghadri