RNA-seq papers
Bulk RNA-seq analysis
Introductory
- RNA-seq: a revolutionary tool for transcriptomics / Link
- From RNA-seq reads to differential expression results / Link
- IVT-seq reveals extreme bias in RNA sequencing / Link
Differential expression analysis
- Linear models and empirical bayes methods for assessing differential expressing in microarray experiments / Link
- voom: precision weights unlock linear model analysis tools for RNA-seq read counts / Link - limma-voom
- Differential expression analysis for sequence count data / Link - DESeq
Multiple testing and false discovery rate
Batch effects and effective experimental design
- Tackling the widespread and critical impact of batch effects in high-throughput data / Link
- Sequencing technology does not eliminate biological variability / Link
- Normalization of RNA-seq data using factor analysis of control genes or samples / Link
Gene enrichment analysis
- Gene enrichment analysis made simple / Link
Best practices
- A survey of best practices for RNA-seq data analysis / Link
Single-cell RNA-seq analysis
Introductory
- Defining cell types and states with single-cell genomics / Link
- Exponential scaling of single-cell RNA-seq in the past decade / Link
- The Human Cell Atlas: from vision to reality / Link
Normalization
- Pooling across cells to normalize single-cell RNA sequencing data with many zero counts / Link
- Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data / Link
- Droplet scRNA-seq is not zero-inflated / Link
- Performance assessment and selection of normalization procedures for single-cell RNA-seq / Link
Dimensionality reduction, visualization, clustering pseudotime
- A general and flexible method for signal extraction from single-cell RNA-seq data / Link
- Feature selection and dimension reduction for single-cell RNA-seq based on a multinomial model / Link
- Visualizing the structure of RNA-seq expression data with grades of membership models / Link
- A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000 Research. 2019. / Link
- The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells / Link
Batch effects and effective experimental design
- Missing data and technical variability in single-cell RNA-sequencing experiments / Link
- Batch effects and the effective design of single-cell gene expression studies / Link
- Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. / Link
Best practices
- Current best practices in single-cell RNA-seq analysis: a tutorial / Link
- Orchestrating single-cell analysis with Bioconductor / Link
Contact
Chiaowen Joyce Hsiao