Skip to content

Latest commit

 

History

History
212 lines (175 loc) · 27.7 KB

README.md

File metadata and controls

212 lines (175 loc) · 27.7 KB

SingleCellNotes

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:

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:

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:

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:

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

Atlases and compendia

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

Real time versus pseudo time

Visualization

Gene regulatory network inference

Reviews

Benchmark studies

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

Integration

Reviews

Methods

Spatial transcriptomics

Reviews

Technologies

Spatial reconstruction

Deconvolution

Cell-cell interaction

Perturbations

10x and BioLegend

Cell hashing

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

Multiome