English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneityin cells, which has given us insights into cell-cell communication, celldifferentiation, and differential gene expression. However, analyzing scRNA-seqdata is a challenge due to sparsity and the large number of genes involved.Therefore, dimensionality reduction and feature selection are important forremoving spurious signals and enhancing downstream analysis. Traditional PCA, amain workhorse in dimensionality reduction, lacks the ability to capturegeometrical structure information embedded in the data, and previous graphLaplacian regularizations are limited by the analysis of only a single scale.We propose a topological Principal Components Analysis (tPCA) method by thecombination of persistent Laplacian (PL) technique and L$_{2,1}$ normregularization to address multiscale and multiclass heterogeneity issues indata. We further introduce a k-Nearest-Neighbor (kNN) persistent Laplaciantechnique to improve the robustness of our persistent Laplacian method. Theproposed kNN-PL is a new algebraic topology technique which addresses the manylimitations of the traditional persistent homology. Rather than inducingfiltration via the varying of a distance threshold, we introduced kNN-tPCA,where filtrations are achieved by varying the number of neighbors in a kNNnetwork at each step, and find that this framework has significant implicationsfor hyper-parameter tuning. We validate the efficacy of our proposed tPCA andkNN-tPCA methods on 11 diverse benchmark scRNA-seq datasets, and showcase thatour methods outperform other unsupervised PCA enhancements from the literature,as well as popular Uniform Manifold Approximation (UMAP), t-DistributedStochastic Neighbor Embedding (tSNE), and Projection Non-Negative MatrixFactorization (NMF) by significant margins.

K-Nearest-Neighbors Induced Topological PCA for scRNA Sequence Data Analysis