README.md

SingleCellNotes

• Journal club
• General
• Courses
• Tutorials
• Atlases and compendia
• Trajectory inference
  • RNA velocity
  • Real time versus pseudo time
• Visualization
• Gene regulatory network inference
  • Reviews
  • Benchmark studies
  • Methods
  • Simulators
  • Applications
• Integration
  • Reviews
  • Methods
• Spatial transcriptomics
  • Reviews
  • Technologies
  • Spatial reconstruction
  • Deconvolution
  • Cell-cell interaction
• Perturbations
• 10x and BioLegend
  • Cell hashing
    • Examples
    • Literature
  • Multiome

Journal club

Week 1: April 22, 2021 (Perry). SCENIC: Aibar et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083-1086. R code, Python code. Follow-up reading:

• A scalable SCENIC workflow for single-cell gene regulatory network analysis: Van de Sande et al. Nat Protoc 15, 2247–2276 (2020). GitHub

Week 2: May 6, 2021 (Huub). A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens: Bouhaddou et al. PLoS Comput Biol. 2018 Mar 26;14(3):e1005985. Follow-up reading:

• Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model: Fröhlich et al. Cell Syst. 2018 Dec 26;7(6):567-579.e6.

Week 3: May 20, 2021 (Utkarsh):

• GENIE3: Huynh-Thu et al. Inferring Regulatory Networks from Expression Data Using Tree-Based Methods(2010). PLOS ONE 5(9): e12776. GitHub
• dynGENIE3: Huynh-Thu et al. dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data. Sci Rep 8, 3384 (2018). GitHub

Week 4: June 3, 2021 (Aldo). SCODE: Matsumoto et al. SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation, Bioinformatics, Volume 33, Issue 15, 2017, Pages 2314–2321. Code

Week 5: June 17, 2021 (Wessel). Ridge estimation of network models from time‐course omics data: Miok et al. Biom J. 2019 Mar;61(2):391-405. R code. Follow-up reading:

• Ridge estimation of inverse covariance matrices from high-dimensional data: Van Wieringen et al., 2016. Computational Statistics & Data Analysis, 103, pp.284-303. R code
• Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time‐course omics data: Miok et al. Biom J. 2017 Jan;59(1):172-191.

Week 6: October 7, 2021 (Utkarsh). Scribe: Qiu et al. Inferring causal gene regulatory networks from coupled single-cell expression dynamics using Scribe. Cell Syst. 2020;10(3):265-274.e11. Code

Week 7. scPred: Alquicira-Hernandez et al. scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biology, 20:264 (2019). Code. Follow-up reading:

• A comparison of automatic cell identification methods for single-cell RNA sequencing data: Abdelaal et al. Genome Biology, 20:194 (2019).

Week 8. Beyond Predictions in Neural ODEs: Identification and Interventions: Aliee et al. arXiv (2021) 2106.12430.

General

• awesome-single-cell: List of software packages (and the people developing these methods) for single-cell data analysis, including RNA-seq, ATAC-seq, etc.
• scRNA-tools: A catalogue of tools for analysing single-cell RNA sequencing data.
• A curated database of of single-cell transcriptomics studies.
• Single-cell reading list: A curated selection of blog posts and papers on single-cell data analysis.
• Single Cell Portal: Featuring 295 studies and 11,739,593 cells (and counting).
• Single Cell Genomics Day: Each year the lab of Rahul Satija organizes a one-day workshop highlighting recent developments in the field.

Courses

• Analysis of single cell RNA-seq data: A long-running course from the Cambridge Bioinformatics training unit (Martin Hemberg and others). See also their GitHub repository. This course at the Broad Institute is based on it and offers some interesting extensions (on CITE-Seq for example).
• MGC/BioSB Course - Single Cell Analysis: This course covers the practicalities of single-cell sample prep and analysis with a particular focus on single-cell RNA-seq libraries.

Tutorials

• Orchestrating Single-Cell Analysis with Bioconductor: Very comprehensive on-line book that teaches you how to make use of cutting-edge Bioconductor tools to process, analyze, visualize, and explore scRNA-seq data. The companion paper can be found here.
• Current best practices in single‐cell RNA‐seq analysis: a tutorial: A highly readable tutorial by Malte Luecken and Fabian Theis. There is a companion GitHub repository with the scripts.
• Seurat vignettes: Seurat remains the most comprehensive R toolkit for single genomics analysis accompanied by a large collection of vignettes.
• Scanpy tutorials: Scanpy is a scalable Python toolkit for analyzing single-cell gene expression data and comes with a rich set of tutorials.
• Kallisto|bustools: Google Colab notebooks on working with kallisto in combination with bustools.

Atlases and compendia

• The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors: The Tabula Sapiens Consortium, Stephen R Quake (2021) bioRxiv 2021.07.19.452956. Data
• Single-nucleus cross-tissue molecular reference maps to decipher disease gene function: Eraslan et al. (2021) bioRxiv 2021.07.19.452954.
• Single Cell Portal
• PangloaDB

Trajectory inference

• single-cell-pseudotime: Overview of single-cell RNA-seq pseudotime estimation algorithms.
• dynmethods: A collection of 55 trajectory inference methods. To run any of these methods, interpret the results and visualise the trajectory, see the dyno package.

RNA velocity

• RNA velocity—current challenges and future perspectives: Bergen et al. Mol Syst Biol (2021)17:e10282.

Real time versus pseudo time

• Lineage tracing on transcriptional landscapes links state to fate during differentiation: Weinreb et al. Science. 2020;367(6479):eaaw3381.

Visualization

• Comparison of visualization tools for single-cell RNAseq data : Cakir et al. NAR Genomics and Bioinformatics, 2(3):lqaa052, 2020.

Gene regulatory network inference

Reviews

• Gene regulatory network inference in the era of single-cell multi-omics: Badia-i-Mompel et al. Nature Reviews Genetics (2023). https://doi.org/10.1038/s41576-023-00618-5.
• Gene regulatory network inference in single-cell biology: Akers et al. Current Opinion in Systems Biology, Volume 26, 2021, Pages 87-97.
• A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data: Nguyen et al. Briefings in Bioinformatics, bbaa190 (2020).
• Integration of single-cell multi-omics for gene regulatory network inference: Hu et al. Computational and Structural Biotechnology Journal. 2020;18:1925-1938.
• Network Inference from Single-Cell Transcriptomic Data: Todorov et al. (2019) Network Inference from Single-Cell Transcriptomic Data. In: Sanguinetti G., Huynh-Thu V. (eds) Gene Regulatory Networks. Methods in Molecular Biology, vol 1883. Humana Press, New York, NY.
• Mapping gene regulatory networks from single-cell omics data: Fiers et al. Briefings in Functional Genomics, Volume 17, Issue 4, July 2018, Pages 246–254.
• Learning regulatory models for cell development from single cell transcriptomic data: Babtie et al. Current Opinion in Systems Biology 5 (2017): 72-81.

Benchmark studies

• Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data: Pratapa et al. Nat Methods. 2020;17(2):147-154.
• Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data: Chen et al. BMC Bioinformatics 19, 232 (2018).

Methods

• locaTE: Zhang et al. (2023) Dynamical information enables inference of gene regulation at single-cell scale. bioRxiv 2023.01.08.523176. Code
• scGeneRAI: Keyl et al. (2023) Single-cell gene regulatory network prediction by explainable AI. Nucleic Acids Research, gkac1212 Code
• dynDeepDRIM: Xu et al. (2022) dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data/ Briefings in Bioinformatics, Volume 23, Issue 6, bbac424.
• SINGE: Deshpande et al. (2022) Network inference with Granger causality ensembles on single-cell transcriptomic data. Cell Reports, Volume 38, Issue 6. Code
• locCSN: Wang et al. (2021) Constructing local cell-specific networks from single-cell data. PNAS, 118 (51) e2113178118. Code
• DeepSEM: Shu et al. (2021) Modeling gene regulatory networks using neural network architectures. Nature Computational Science, 1:491–501. Code
• scPADGRN: Zheng et al. (2020) scPADGRN: A preconditioned ADMM approach for reconstructing dynamic gene regulatory network using single-cell RNA sequencing data. PLoS Comput Biol 16(7): e1007471. Code
• GRISLI: Aubin-Frankowski et al. Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference, Bioinformatics, btaa576, 2020. Code
• Scribe: Qiu et al. Inferring causal gene regulatory networks from coupled single-cell expression dynamics using Scribe. Cell Syst. 2020;10(3):265-274.e11. Code
• WASABI: Bonnaffoux et al. WASABI: a dynamic iterative framework for gene regulatory network inference. BMC Bioinformatics 20, 220 (2019).
• M&NEM: Pirkl et al. Single cell network analysis with a mixture of Nested Effects Models, Bioinformatics, Volume 34, Issue 17, 2018, Pages i964–i971. Code
• AR1MA1 - VBEM: Sanchez-Castillo et al. A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data, Bioinformatics, Volume 34, Issue 6, 2018, Pages 964–970. Code
• SINCERITIES: Gao et al. SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles, Bioinformatics, Volume 34, Issue 2, 2018, Pages 258–266. Code
• SCENIC: Aibar et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083-1086. R code, Python code
• SCODE: Matsumoto et al. SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation, Bioinformatics, Volume 33, Issue 15, 2017, Pages 2314–2321. Code
• inferenceSnapshot: Ocone et al. Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data, Bioinformatics, Volume 31, Issue 12, 2015, Pages i89–i96. Code
• SCNS. Moignard et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat Biotechnol. 2015;33(3):269-276. Code

Simulators

• dyngen: Cannoodt et al. (2021) Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells. Nat Commun 12, 3942. Code
• SERGIO: Dibaeinia et al. SERGIO: A single-cell expression simulator guided by gene regulatory networks. Cell Syst. 2020;11(3):252-271.e11. Code
• BEELINE: Evaluation framework built on top of dynverse and BoolODE; used in Pratapa et al. (see above)

Applications

• Predicting gene regulatory networks from cell atlases: Fønss Møller et al. Life Science Alliance (2020), 3:(11), e202000658.

Integration

Reviews

• Computational principles and challenges in single-cell data integration: Argelaguet et al. Nat Biotechnol (2021).

Methods

• scArches: Mapping single-cell data to reference atlases by transfer learning: Lotfollahi et al. Nat Biotechnol (2021). Code
• Seurat v4: Integrated analysis of multimodal single-cell data: Hao et al. Cell, 184(13): 3573-3587 (2021).
• Efficient and precise single-cell reference atlas mapping with Symphony: Kang et al. (2021) bioRxiv 2020.11.18.389189. Code
• Fast, sensitive and accurate integration of single-cell data with Harmony: Korsunsky et al. Nature Methods 16 (2019): 1-8. Code
• Seurat v3: Comprehensive integration of single-cell data: Stuart et al. Cell 177.7 (2019): 1888-1902. Code
• LIGER: Single-cell multi-omic integration compares and contrasts features of brain cell identity: Welch et al. Cell 177.7 (2019): 1873-1887. Code. See also: Iterative single-cell multi-omic integration using online learning: Gao et al. Nat Botechnol 39, 1000–1007 (2021).

Spatial transcriptomics

Reviews

• Advances in spatial transcriptomic data analysis: Dries et al. Genome Research (2021).
• Spatial omics and multiplexed imaging to explore cancer biology: Lewis et al. Nat Methods (2021).
• Exploring tissue architecture using spatial transcriptomics: Rao et al. Nature 596, 211–220 (2021).
• Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics: Longo et al. Nature Review Genetics (2021).
• Uncovering an organ’s molecular architecture at single-cell resolution by spatially resolved transcriptomics: Liao et al. Trends Biotechnol. 2020:S0167-7799(20)30140-2.
• From whole-mount to single-cell spatial assessment of gene expression in 3D: Waylen et al. Commun Biol 3, 602 (2020).
• Deciphering cell–cell interactions and communication from gene expression: Armingol et al. Nat Rev Genet (2020).

Technologies

• Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution: Rodriques et al. Science. 2019;363(6434):1463-1467.

Spatial reconstruction

• Perler: Model-based prediction of spatial gene expression via generative linear mapping: Okochi et al. (2020). bioRxiv 2020.05.21.107847.
• novoSpaRc: Gene expression cartography: Nitzan et al. Nature 576.7785 (2019): 132-137. Code
• DistMap: The Drosophila embryo at single-cell transcriptome resolution: Karaiskos et al. Science 358.6360 (2017): 194-199. Code
• High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin: Achim et al. Nat Biotechnol 33, 503–509 (2015).
• Seurat: Spatial reconstruction of single-cell gene expression data: Satija et al. Nat Biotechnol 33, 495–502 (2015). Code

Deconvolution

• DestVI: Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation: Lopez et al. (2021) bioRxiv 2021.05.10.443517. Code
• stereoscope: Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography: Andersson et al. Communications Biology 3:565 (2020).
• SPOTlight: Seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes: Elosua et al. (2020) bioRxiv 2020.06.03.131334. Code

Cell-cell interaction

• Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data : Dimitrov et al. (2022) Nat Commun 13, 3224. See LIANA for their benchmark framework (including lots of resources and reimplementations)
• Integrated intra- and intercellular signaling knowledge for multicellular omics analysis: Türei et al. Mol Syst Biol (2021) 17:e9923. Web
• SpaOTsc: Inferring spatial and signaling relationships between cells from single cell transcriptomic data: Cang et al. Nature Communications 11.1 (2020): 1-13. Code
• CSOmap: Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly: Ren et al. Cell Res 30, 763–778 (2020).
• CellChat: Inference and analysis of cell-cell communication using CellChat: Jin et al. (2020). bioRxiv 2020.07.21.214387. Code, Web

Perturbations

• Machine learning for perturbational single-cell omics: Ji et al. Cell Systems 12(6) 522-537 (2021).

10x and BioLegend

• Chromium Next GEM Single Cell 3’ Reagent Kits v3.1 (Dual Index)

Cell hashing

• Satija Lab: CITE-seq & Cell Hashing protocol
• TotalSeq™-B or -C with 10x Feature Barcoding Technology
• TotalSeq™-B0251 anti-human Hashtag 1 Antibody: What should be my sequencing depth be for my ADT/HTO library? We recommend a sequencing depth of 5,000 reads/cell. If you are using more than 100 TotalSeq™ antibodies, you should increase to 10,000 reads/cell. For HTOs alone, 500 reads/cell is sufficient.
• Cell Multiplexing Oligo Labeling for Single Cell RNA Sequencing Protocols with Feature Barcode technology
• Can I perform shallow sequencing on 3’ Cell Multiplexing libraries to assess the quality of my CellPlex data?: For accurate CMO tag assignment, we recommend >1,000 usable reads/cell. This can typically be achieved by sequencing to our general recommended depth of 5,000 raw reads/cell.

Examples

• High-dimensional single-cell analysis identifies cellular signatures associated with response to vedolizumab therapy in ulcerative colitis: A total of 400,000 cells from each sample (biopsy and PBMC) were ... labeled with barcoded antibodies (also known as cell hashing) using TotalSeq oligo-conjugated hashtag antibodies (Biolegend) ... The cDNA libraries were sequenced at 70,000 reads/cell and HTO libraries at 5,000 reads/cell.
• Dictionary of immune responses to cytokines at single-cell resolution: Cell hashing was used to combine multiple samples ... with TotalSeq antibodies (BioLegend anti-mouse hashtags ...) ... The pooled libraries were paired-end sequenced on a NovaSeq S4 platform targeting an average sequencing depth of 20,000 reads per cell for gene expression libraries, and on a NovaSeq S4 or SP platform targeting 5,000 reads per cell for hashtag libraries.
• An atlas of cells in the human tonsil: Each sample was split into seven aliquots with equal numbers of cells ... To each aliquot, a specific TotalSeq-A antibody-oligo conjugate ... was added ... Finally, sequencing of HTO and GEX libraries was carried out on a NovaSeq 6000 sequencer (Illumina) using the following sequencing conditions: 28 bp (Read 1) + 8 bp (i7 index) + 0 bp (i5 index) + 89 bp (Read 2), to obtain approximately 2,000 and >20,000 paired-end reads per HTO and cell, respectively.

Literature

• Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq
• A risk-reward examination of sample multiplexing reagents for single cell RNA-Seq: Only paper I could find that actually benchmarks demultiplexing using Cell Ranger multi
• hadge: a comprehensive pipeline for donor deconvolution in single-cell studies
• Demuxafy: improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods

Multiome

• Cell Ranger ARC