[HTML][HTML] Leveraging gene correlations in single cell transcriptomic data
BACKGROUND: Many approaches have been developed to overcome technical noise in
single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for …
single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for …
scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating
cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless …
cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless …
Addressing the looming identity crisis in single cell RNA-seq
Single cell RNA-sequencing technology (scRNA-seq) provides a new avenue to discover
and characterize cell types, but the experiment-specific technical biases and analytic …
and characterize cell types, but the experiment-specific technical biases and analytic …
Memento: generalized differential expression analysis of single-cell RNA-seq with method of moments estimation and efficient resampling
Differential expression analysis of scRNA-seq data is central for characterizing how
experimental factors affect the distribution of gene expression. However, it remains …
experimental factors affect the distribution of gene expression. However, it remains …
Contrastive Learning for Robust Cell Annotation and Representation from Single-Cell Transcriptomics
L Andrekson, R Mercado - bioRxiv, 2024 - biorxiv.org
Batch effects are a significant concern in single-cell RNA sequencing (scRNA-Seq) data
analysis, where variations in the data can be attributed to factors unrelated to cell types. This …
analysis, where variations in the data can be attributed to factors unrelated to cell types. This …
scID: identification of transcriptionally equivalent cell populations across single cell RNA-seq data using discriminant analysis
The power of single cell RNA sequencing (scRNA-seq) stems from its ability to uncover cell
type-dependent phenotypes, which rests on the accuracy of cell type identification. However …
type-dependent phenotypes, which rests on the accuracy of cell type identification. However …
Trade-off between conservation of biological variation and batch effect removal in deep generative modeling for single-cell transcriptomics
Background Single-cell RNA sequencing (scRNA-seq) technology has contributed
significantly to diverse research areas in biology, from cancer to development. Since scRNA …
significantly to diverse research areas in biology, from cancer to development. Since scRNA …
Non-negative Independent Factor Analysis for single cell RNA-seq
Motivation Single cell RNA sequencing (scRNA-seq) enables transcriptional profiling at the
level of individual cells. With the emergence of high-throughput platforms datasets …
level of individual cells. With the emergence of high-throughput platforms datasets …
scDecorr-Feature decorrelation representation learning with domain adaptation enables self-supervised alignment of multiple single-cell experiments
Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular
heterogeneity in complex biological systems. However, analyzing and integrating scRNA …
heterogeneity in complex biological systems. However, analyzing and integrating scRNA …
Monet: An open-source Python package for analyzing and integrating scRNA-Seq data using PCA-based latent spaces
F Wagner - bioRxiv, 2020 - biorxiv.org
Single-cell RNA-Seq is a powerful technology that enables the transcriptomic profiling of the
different cell populations that make up complex tissues. However, the noisy and high …
different cell populations that make up complex tissues. However, the noisy and high …