Chapter 3 Feature selection | Basics of Single-Cell Analysis with Bioconductor
bioconductor.org · 3,993 words · saved by 1 readers
Chapter 3 Feature selection | Basics of Single-Cell Analysis with Bioconductor
Chapter 3 Feature selection | Basics of Single-Cell Analysis with Bioconductor Basics of Single-Cell Analysis with Bioconductor Chapter 3 Feature selection 3.1 Motivation We often use scRNA-seq data in exploratory analyses to characterize heterogeneity across cells. Procedures like clustering and dimensionality reduction compare cells based on their gene expression profiles, which involves aggregating per-gene differences into a single (dis)similarity metric between a pair of cells. The choice of genes to use in this calculation has a major impact on the behavior of the metric and the performa
saved by
related reading
- 10. Feature selection — Single-cell best practicessc-best-practices.org
- Quantifying the effect of experimental perturbations at single-cell resolutionnature.com
- Signature-scoring methods developed for bulk samples are not adequate for cancer single-cell RNA sequencing data | eLifeelifesciences.org
- scGen predicts single-cell perturbation responsesnature.com
- The art of using t-SNE for single-cell transcriptomicsnature.com
- Current best practices in single‐cell RNA‐seq analysis: a tutorial | Molecular Systems Biology | Springer Nature Linkembopress.org
- Minimum redundancy feature selection - Wikipediaen.wikipedia.org
- Advancing regulatory variant effect prediction with AlphaGenomenature.com
- X explains Z% of the variance in Y — LessWronglesswrong.com
- scVI — scvi-toolsdocs.scvi-tools.org
- A survey of best practices for RNA-seq data analysisgenomebiology.biomedcentral.com
- Cell-type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating culturesbiorxiv.org