RNA-seq papers

Bulk RNA-seq analysis


  • 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

  • Statistical significance for genomewide studies / Link
  • False discover rate: A new deal / Link

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


  • 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


  • 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


Chiaowen Joyce Hsiao