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ISMB 2019 Tutorial on "Recent Advances in Statistical Methods and Computational Algorithms for Single-Cell Omics Analysis"

This tutorial is focused on advanced statistical and computational methods that are recently developed for single-cell omics data. It is intended for an audience with genomics/computational background, who are interested in cutting-edge developments of single-cell research, including both method development and application.

All tutorial materials and extra links are provided here.


Official schedule for this tutorial can be found here.

Course Instructors (equal contributors; alphabetical order)

Rhonda Bacher, University of Florida,

Yuchao Jiang, University of North Carolina-Chapel Hill,

Jingshu Wang, University of Chicago,

Please contact any of us regarding comments or questions.

Tutorial Feedback

If you attend our tutorial at ISMB 2019, please provide feedback via this survery:


List of Methods

Single-cell quality control

  • scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R (paper, software)

Single-cell normalization

  • SCnorm: robust normalization of single-cell RNA-seq data (paper, software)

  • scran: Pooling across cells to normalize single-cell RNA sequencing data with many zero counts (paper, software)

  • scTransform: Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (paper, software)

Single-cell visualization

  • t-SNE: t-Distributed Stochastic Neighbor Embedding (paper)

  • UMAP: Uniform Manifold Approximation and Projection (paper) software

Single-cell batch correction

  • mnnCorrect: Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors (paper, software)

  • multiCCA: (paper1, paper2, software)


  • SAVER: gene expression recovery for single-cell RNA sequencing (paper, software)

  • DCA: Single-cell RNA-seq denoising using a deep count autoencoder (paper, software)

  • scVI: Deep generative modeling for single-cell transcriptomics (paper, software)

  • SAVER-X: Transfer learning in single-cell transcriptomics improves data denoising and pattern discovery (paper, software)

Transfer learning

  • SAVER-X: see above

  • scGen: Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species (paper, software)

  • cTP-net: Surface protein imputation from single cell transcriptomes by deep neural networks (paper, software)

Single-cell pseudotime

Single-cell clustering

  • SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis (paper, software)

  • pcaReduce: hierarchical clustering of single cell transcriptional profiles (paper, software)

  • CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data (paper, software)

  • SNN-Cliq: Identification of cell types from single-cell transcriptomes using a novel clustering method. (paper, software)

  • SOUP: Semisoft clustering of single-cell data (paper, software)

  • SC3: consensus clustering of single-cell RNA-seq data (paper, software)

Single-cell differential features

  • SCDE: Bayesian approach to single-cell differential expression analysis (paper, software)

  • MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data (paper, software)

  • BASiCS: Bayesian Analysis of Single-Cell Sequencing Data (paper, software)

  • DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data (paper, software)

  • scDD: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments (paper, software)

  • DESCEND: Gene expression distribution deconvolution in single-cell RNA sequencing (paper, software)

Single-cell classification

  • SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species (paper, software)

  • ACTINN: Automated identification of Cell Types in Single Cell RNA Sequencing (paper, software)

Single-cell immune profiling

  • TraCeR: T cell fate and clonality inference from single-cell transcriptomes (paper,software)

  • VDJPuzzle: B-cell receptor reconstruction from single-cell RNA-seq (paper,software)

Single-cell epigenomics

  • SCRAT: Single-cell regulome data analysis (paper, software)

  • scABC: Unsupervised clustering and epigenetic classification of single cells (paper,software)

  • Destin: Toolkit for single-cell analysis of chromatin accessibility (paper,software)

Single-cell multimodal alignment

  • PECA: Modeling gene regulation from paired expression and chromatin accessibility data (paper,software)

  • MATCHER: Manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics (paper,software)

  • CCA (Seurat): Comprehensive Integration of Single-Cell Data (paper,software)

Single-cell cancer genomics

  • Canopy: Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing (paper,software)

  • MARATHON: Integrative pipeline for profiling DNA copy number and inferring tumor phylogeny (paper, software)

  • InferCNV: Inferring CNV from Single-Cell RNA-Seq (paper, software)

  • HoneyBADGER: Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data (paper, software)

  • Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants (paper, software)

  • SCOPE: A normalization and copy number estimation method for single-cell DNA sequencing (paper, software)

  • SCALE: Modeling allele-specific gene expression by single-cell RNA sequencing (paper, software)

  • Dendro: A normalization and copy number estimation method for single-cell DNA sequencing (paper, software)


Other tutorials and workflows


Specific reviews


Tutorials and code for ISMB 2019 short course on Recent Advances in Statistical Methods and Computational Algorithms for Single-Cell Omics Analysis






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