Key Papers, Reviews & Benchmarks

A curated reading list spanning cross-cutting foundations and every module topic, with head-to-head method comparisons flagged so you can find them fast.

A reading list, not required reading. Papers tagged (benchmark) compare methods head-to-head — start there when you need to choose a tool rather than learn one. Each topic links to its workshop chapter; every chapter’s “Going further” section points back here.

Start here — cross-cutting foundations

Read these few first; each builds on the previous and frames the rest of the field.

Paper	Why it matters
Tang et al. 2009, Nature Methods	The first single-cell RNA-seq experiment. Useful historical context.
Macosko et al. 2015, Cell	Drop-seq — the droplet-based throughput leap.
Zheng et al. 2017, Nature Communications	The 10x Chromium platform paper; describes the chemistry the workshop dataset uses.
Luecken & Theis 2019, Mol Sys Bio	“Current best practices in single-cell RNA-seq analysis” — still essential.
Kharchenko 2021, Nature Methods	“The triumphs and limitations of computational methods for scRNA-seq” — a reality check.
Heumos et al. 2023, Nat Rev Genet	Companion paper to the sc-best-practices book; high-level overview.
Lähnemann et al. 2020, Genome Biology	“Eleven grand challenges in single-cell data science”.

By module topic

Overview & best practices — Chapter 0

Luecken & Theis 2019, Mol Syst Biol — the foundational end-to-end best-practices tutorial.
Heumos et al. 2023, Nat Rev Genet — best practices across modalities; companion to the sc-best-practices book.
Lähnemann et al. 2020, Genome Biology — “Eleven grand challenges in single-cell data science”.
Kharchenko 2021, Nature Methods — the triumphs and limitations of computational scRNA-seq methods.
Ziegenhain et al. 2017, Molecular Cell — head-to-head comparison of six scRNA-seq protocols (benchmark).

Preprocessing: QC & normalization — Chapter 1

Lun et al. 2019, Genome Biology — EmptyDrops; distinguishing real cells from empty droplets.
Young & Behjati 2020, GigaScience — SoupX; quantifying and removing ambient RNA.
Xi & Li 2021, Cell Systems — benchmark of nine doublet-detection methods (benchmark).
Hafemeister & Satija 2019, Genome Biology — sctransform; regularized NB normalization.
Ahlmann-Eltze & Huber 2023, Nature Methods — comparison of normalization/transformation choices (benchmark).

Dimensionality reduction & clustering — Chapter 2

Interactive explainer: Understanding UMAP (Google PAIR) — turn the n_neighbors/min_dist knobs and see what UMAP does and doesn’t preserve. The best cure for over-interpreting an embedding; start here if UMAP/t-SNE ever made your head spin.
Tutorial: Understanding UMAP: a guide to dimensionality reduction (DataCamp) — a written, step-by-step walkthrough of the method and its parameters.
Becht et al. 2019, Nature Biotechnology — UMAP for single-cell visualization.
Kobak & Berens 2019, Nature Communications — “The art of using t-SNE”.
Kobak & Linderman 2021, Nature Biotechnology — initialization, not the algorithm, preserves global structure.
Kiselev et al. 2019, Nat Rev Genet — challenges in unsupervised clustering.
Duò et al. 2018, F1000Research — performance evaluation of 14 clustering methods (benchmark).
Yu et al. 2022, Genome Biology — estimating the number of cell types (benchmark).

Markers & manual annotation — Chapter 3

Clarke et al. 2021, Nature Protocols — guidelines for annotating single-cell maps (manual + automated).
Soneson & Robinson 2018, Nature Methods — benchmark of 36 DE methods used for marker detection (benchmark).
Marker compendia: PanglaoDB and CellMarker 2.0.

Reference-based annotation — Chapter 4

Aran et al. 2019, Nature Immunology — SingleR; reference-based annotation.
Hao et al. 2021, Cell — Seurat v4 / Azimuth reference mapping.
Domínguez Conde et al. 2022, Science — CellTypist; cross-tissue immune atlas.
Abdelaal et al. 2019, Genome Biology — comparison of 22 automatic cell-identification methods (benchmark).
Pasquini et al. 2021, CSBJ — review of 32 automated annotation methods.
Tools: Azimuth, CellTypist.

Multi-sample integration — Chapter 5

Korsunsky et al. 2019, Nature Methods — Harmony; the recommended first integration tool.
Stuart, Butler et al. 2019, Cell — Seurat v3 anchor-based integration.
Lopez et al. 2018, Nature Methods — scVI; variational autoencoder for single-cell data.
Xu et al. 2021, Mol Syst Biol — scANVI; semi-supervised integration + annotation.
Tran et al. 2020, Genome Biology — benchmark of 14 batch-correction methods (benchmark).
Luecken et al. 2022, Nature Methods — benchmarking atlas-level data integration (benchmark).

Differential expression (bulk + pseudobulk) — Chapter 6

Love, Huber & Anders 2014, Genome Biology — DESeq2.
Robinson et al. 2010, Bioinformatics — edgeR.
Law et al. 2014, Genome Biology — limma-voom.
Rapaport et al. 2013, Genome Biology — bulk DE-method benchmark (benchmark).
Soneson & Robinson 2018, Nature Methods — single-cell DE benchmark (benchmark).
Squair et al. 2021, Nature Communications — why pseudobulk is the right default.
Crowell et al. 2020, Nature Communications — muscat; pseudobulk DE in practice.

Functional analysis: GO, GSEA, pathways — Chapter 7

Subramanian et al. 2005, PNAS — the original GSEA paper.
Khatri et al. 2012, PLoS Comput Biol — “Ten years of pathway analysis”; the field review.
Wu et al. 2021, The Innovation — clusterProfiler 4.0.
Korotkevich et al. 2021, bioRxiv — fgsea; fast preranked GSEA.
Gillespie et al. 2022, Nucleic Acids Res — the Reactome pathway knowledgebase.
Geistlinger et al. 2021, Brief Bioinform — gold-standard benchmark of enrichment methods (benchmark).

Differential abundance — Chapter 8

Dann et al. 2022, Nature Biotechnology — Milo; DA on k-NN neighbourhoods.
Büttner et al. 2021, Nature Communications — scCODA; Bayesian compositional analysis.
Mangiola et al. 2023, PNAS — sccomp; joint composition + variability modeling.
Yi et al. 2024, Genome Biology — benchmark of six differential-abundance methods (benchmark).

Co-expression networks (WGCNA) — Chapter 13

Zhang & Horvath 2005, Stat Appl Genet Mol Biol — the weighted co-expression framework.
Langfelder & Horvath 2008, BMC Bioinformatics — the WGCNA R package.
van Dam et al. 2018, Brief Bioinform — review of gene co-expression analysis (guilt-by-association).
Morabito et al. 2023, Cell Reports Methods — hdWGCNA; the single-cell/spatial adaptation.

Trajectory, velocity & cell–cell communication — Chapter 14

Saelens et al. 2019, Nature Biotechnology — comparison of 45 trajectory-inference methods (benchmark).
Trapnell et al. 2014, Nature Biotechnology — Monocle; pseudotemporal ordering. See also Monocle3.
Street et al. 2018, BMC Genomics — Slingshot; lineage/pseudotime.
La Manno et al. 2018, Nature — RNA velocity (foundational); see also scVelo.
Bergen et al. 2020, Nature Biotechnology — scVelo; dynamical-model velocity.
Armingol et al. 2021, Nat Rev Genet and Armingol et al. 2024, Nat Rev Genet — cell–cell communication reviews.
Dimitrov et al. 2022, Nature Communications — LIANA; CCC meta-resource. Jin et al. 2021, Nature Communications — CellChat.

ATAC-seq & chromatin accessibility — Chapter 15

Buenrostro et al. 2013, Nature Methods — the original ATAC-seq method.
Klemm, Shipony & Greenleaf 2019, Nat Rev Genet — chromatin accessibility and the regulatory epigenome (review).
Stuart et al. 2021, Nature Methods — Signac.
Granja et al. 2021, Nat Genet — ArchR.
De Rop et al. 2024, Nature Biotechnology — systematic benchmark of scATAC protocols (benchmark).
Luo et al. 2024, Genome Biology — benchmark of scATAC computational methods (benchmark).

Spatial transcriptomics — Chapter 16

Ståhl et al. 2016, Science — the method that launched array-based spatial transcriptomics (Visium’s basis).
Rao et al. 2021, Nature — exploring tissue architecture using spatial transcriptomics (review).
Moses & Pachter 2022, Nature Methods — the “museum” of spatial transcriptomics (review).
Bressan et al. 2023, Science — “The dawn of spatial omics” (review).
Palla et al. 2022, Nature Methods — squidpy; scalable spatial-omics analysis.
Visium / Stereo-seq / MERFISH platform docs.

Multi-omics & deep-learning foundations

Wolf, Angerer & Theis 2018, Genome Biology — Scanpy; the Python toolkit.
Argelaguet et al. 2021, Genome Biology — multi-omics factor analysis (MOFA+).
Lopez et al. 2018, Nature Methods — scVI; foundation for scvi-tools.
Tabula Sapiens consortium 2022, Science — a reference human atlas; a model for atlas construction.