Lectures

Lecture 0

• Course Logistics

Lecture 2

• DNA Sequencing

• DNA Sequencing Fact Sheet

• Coming of age: ten years of next-generation sequencing technologies (Goodwin et al. 2016, Nature Reviews Genetics)

• DNA sequencing at 40: past, present and future (Shendure et al. 2017 Nature)

• Must Watch Video (How DNA is copied)

• Must Watch Video (Sequencing by Synthesis)

• RNA-seq: Introduction

• RNA sequencing data: hitchhiker's guide to expression analysis (Van Den Berge, Koen, et al.  2019)

• RNA-Seq: a revolutionary tool for transcriptomics (Wang et al. 2009, Nature Reviews Genetics)

• RNA sequencing: the teenage years (Stark et al. 2019, Nature Reviews Genetics)

Lecture 4

• Single-cell Sequencing: Pre-processing

• Current best practices in single-cell RNA-seq analysis: a tutorial (Luecken and Theis 2019)

• Computational Methods for Single-Cell RNA Sequencing (Brian et al. 2020)

• Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (Townes et al. 2019, Genome Biology)

• Accounting for technical noise in single-cell RNA-seq experiments (Brennecke et al. 2013, Nature Methods)

• Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data (Yip et al. 2019, Briefings in Bioinformatics)

• Decontamination of ambient RNA in single-cell RNA-seq with DecontX (Yang et al. 2020, Genome Biology)

• Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data (Xi and Li 2021, Cell Systems)

• pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools (Germain et al. 2020, Genome Biology)

• SampleQC: robust multivariate, multi-cell type, multi-sample quality control for single-cell data (Macnair and Robinson 2023, Genome Biology)

• Lectures 7 and 8

• Single-cell Sequencing: Dimension Reduction

• Dimensionality Reduction: A Comparative Review (Maaten and Postma 2009)

• A survey of dimensionality reduction techniques (Vargas et al. 2014)

• Diffusion maps for high-dimensional single-cell analysis of differentiation data (Haghvardi et al. 2015)

• Variational Inference: A Review for Statisticians (Blei et al. 2016)

• Deep generative modeling for single-cell transcriptomics (Lopez et al. 2018) 

• Auto-Encoding Variational Bayes (Kingma and Welling 2014)

• Autoencoders, Unsupervised Learning, and Deep Architectures (Baldi 2012)

• scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses (Wang et al. 2021, Nature Communications) - Optional Reading

Lecture 10

• Single-cell Sequencing: Integration

• Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors (Haghverdi et al. 2018)

• Comprehensive Integration of Single-Cell Data (Stuart et al. 2019)

• A test metric for assessing single-cell RNA-seq batch correction (Buttner et al. 2019)

• Fast, sensitive and accurate integration of single-cell data with Harmony (Korsunsky et al. 2019)

• Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models (Xu et al. 2021)

• Benchmarking atlas-level data integration in single-cell genomics (Luecken et al. 2022)

Lecture 12

• Spatial Transcriptomics

• Exploring tissue architecture using spatial transcriptomics (2021)

• Museum of spatial transcriptomics (2022)

• Statistical and machine learning methods for spatially resolved transcriptomics data analysis (2022)

• Computational Approaches and Challenges in Spatial Transcriptomics (2023)

• Cell type Deconvolution

• Robust decomposition of cell type mixtures in spatial transcriptomics (Cable et al. 2021, Nature Biotech)

• Cell2location maps fine-grained cell types in spatial transcriptomics (Kleshchevnikov et al. 2022, Nature Biotech)

• DestVI identifies continuums of cell types in spatial transcriptomics data (Lopez et al. 2022, Nature Biotech)

• SPICEMIX enables integrative single-cell spatial modeling of cell identity (Chidester et al. 2023, Nature Genetics)

• Benchmarking of cell type deconvolution pipelines for transcriptomics data (Cobos et al. 2023)

Lecture 14

• Cell-cell Communication Inference

• Deciphering cell–cell interactions and communication from gene expression (Armingol et al. 2020)

• CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes (Efremova et al. 2020)

• Inference and analysis of cell-cell communication using CellChat (Jin et al. 2021)

• NicheNet: modeling intercellular communication by linking ligands to target genes (Browaeys et al. 2019)

• LIANA+: an all-in-one cell-cell communication framework (Dimitrov et al. 2024)

Lecture 16

• Read Alignment

• Alignment of Next-Generation Sequencing Reads (Reinert et al. 2015, Annual Review of Genomics and Human Genetics)

• Alignment of whole genomes (Delcher et al. 1999, Nucleic Acids Research) 

• Suffix Tree Code

• A tutorial on Burrows-Wheeler indexing methods part 1

• Suffix Array Code