Overview of single-cell RNA-seq technologies
- Power analysis of single-cell RNA-sequencing experiments
- Exponential scaling of single-cell RNA-seq in the past decade
- A curated database reveals trends in single-cell transcriptomics
- Bonus: Comparative Analysis of Single-Cell RNA Sequencing Methods
Pre-processing, quantification and demultiplexing
- Modular and efficient pre-processing of single-cell RNA-seq
- soupX, CellBender
- souporcell: Robust clustering of single cell RNAseq by genotype and ambient RNA inference without reference genotypes
- Bonus papers: soupX, CellBender
Properties of the data
- Missing data and technical variability in single-cell RNA-sequencing experiments
- Droplet scRNA-seq is not zero-inflated
- Zeros in scRNA-seq data: good or bad? How to embrace or tackle zeros in scRNA-seq data analysis?
Best practice
- Current best practices in single-cell RNA-seq analysis: a tutorial
- Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data
Batch correction
- A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples
- Benchmarking atlas-level data integration in single-cell genomics
- A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Cell type annotations (machine learning)
- MARS: discovering novel cell types across heterogeneous single-cell experiments
- Query to reference single-cell integration with transfer learning
eQTL analysis
- Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis
- Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation