K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis
Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells,
which has given us insights into cell–cell communication, cell differentiation, and differential …
which has given us insights into cell–cell communication, cell differentiation, and differential …
PLPCA: persistent laplacian-enhanced PCA for microarray data analysis
Over the years, Principal Component Analysis (PCA) has served as the baseline approach
for dimensionality reduction in gene expression data analysis. Its primary objective is to …
for dimensionality reduction in gene expression data analysis. Its primary objective is to …
Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data
LL Hsu, AC Culhane - Scientific Reports, 2023 - nature.com
Effective dimension reduction is essential for single cell RNA-seq (scRNAseq) analysis.
Principal component analysis (PCA) is widely used, but requires continuous, normally …
Principal component analysis (PCA) is widely used, but requires continuous, normally …
Preprocessing of single cell RNA sequencing data using correlated clustering and projection
Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells,
which has given us insights into cell–cell communication, cell differentiation, and differential …
which has given us insights into cell–cell communication, cell differentiation, and differential …
Edge-group sparse PCA for network-guided high dimensional data analysis
Motivation Principal component analysis (PCA) has been widely used to deal with high-
dimensional gene expression data. In this study, we proposed an Edge-group Sparse PCA …
dimensional gene expression data. In this study, we proposed an Edge-group Sparse PCA …
Visualizing single-cell RNA-seq data with semisupervised principal component analysis
Z Liu - International journal of molecular sciences, 2020 - mdpi.com
Single-cell RNA-seq (scRNA-seq) is a powerful tool for analyzing heterogeneous and
functionally diverse cell population. Visualizing scRNA-seq data can help us effectively …
functionally diverse cell population. Visualizing scRNA-seq data can help us effectively …
A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder
Dimensionality reduction is crucial for the visualization and interpretation of the high-
dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving …
dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving …
Analyzing Single Cell RNA Sequencing with Topological Nonnegative Matrix Factorization
Single-cell RNA sequencing (scRNA-seq) is a relatively new technology that has stimulated
enormous interest in statistics, data science, and computational biology due to the high …
enormous interest in statistics, data science, and computational biology due to the high …
Accelerated dimensionality reduction of single-cell RNA sequencing data with fastglmpca
Motivated by theoretical and practical issues that arise when applying Principal component
analysis (PCA) to count data, Townes et al. introduced “Poisson GLM-PCA”, a variation of …
analysis (PCA) to count data, Townes et al. introduced “Poisson GLM-PCA”, a variation of …
Dimensionality reduction of single-cell RNA-seq data
GC Linderman - RNA Bioinformatics, 2021 - Springer
Dimensionality reduction is a crucial step in essentially every single-cell RNA-sequencing
(scRNA-seq) analysis. In this chapter, we describe the typical dimensionality reduction …
(scRNA-seq) analysis. In this chapter, we describe the typical dimensionality reduction …