RETRACTED: Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization

Abstract

Nonlinear dimensionality reduction (NLDR) methods such as t-Distributed Stochastic Neighbour Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) have been widely used for biological data exploration, especially in single-cell analysis. However, the existing methods have drawbacks in preserving data’s geometric and topological structures. A high-dimensional data analysis method, called Panoramic manifold projection (Panoramap), was developed as an enhanced deep learning framework for structure-preserving NLDR. Panoramap enhances deep neural networks by using cross-layer geometry-preserving constraints. The constraints constitute the loss for deep manifold learning and serve as geometric regularizers for NLDR network training. Therefore, Panoramap has better performance in preserving global structures of the original data. Here, we apply Panoramap to single-cell datasets and show that Panoramap excels at delineating the cell type lineage/hierarchy and can reveal rare cell types. Panoramap can facilitate trajectory inference and has the potential to aid in the early diagnosis of tumors. Panoramap gives improved and more biologically plausible visualization and interpretation of single-cell data. Panoramap can be readily used in single-cell research domains and other research fields that involve high dimensional data analysis.

RETRACTED: Wang et al. Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization. Int. J. Mol. Sci. 2022, 23, 7775

“…Sci . Editorial Office journal retracts the article “Panoramic Manifold Projection (Panoramap) for Single-cell Data Dimensionality Reduction and Visualization” [ 1 ] cited above.…”

“…Following publication, concerns were brought to the attention of the publisher by the originally listed authors, Yongjie Xu, Zelin Zang, Lirong Wu, and Ziqing Li, regarding the accuracy of the authorship, their permission to publish, and the authenticity of their email addresses as contained within this article [ 1 ].…”

“…Adhering to our complaints procedure, an investigation was conducted by the Editorial Office and Editorial Board that confirmed that the above mentioned persons (1) were unaware of the submission; (2) did not consent to be listed as authors; (3) were not responsible for the experimental results on the single-cell data as published; and (4) the email addresses, that were provided during the submission process, were not created or owned by the above mentioned persons.…”