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Single cell multimodal data

Kelly Eckenrode edited this page Sep 14, 2020 · 65 revisions

Journal club on single-cell multimodal data technology and analysis



Paper Date Presenter materials produced
The secret life of cells 2020-05-4 Kelly Eckenrode slides video
none 2020-05-11 none ---
Integrative single-cell analysis 2020-05-18 Dominik Szabo slides video
Computational methods for single-cell omics across modalities 2020-05-25 Dario Righelli slides video
CiteFuse enables multi-modal analysis of CITE-seq data 2020-06-01 Isaac Virshup slides video
Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity 2020-06-08 Zhiyuan Hu slides video
N/A 2020-06-15 Cancelled
Joint profiling of chromatin accessibility and gene expression in thousands of single cells 2020-06-22 Davide Risso slides video
Eleven grand challenges in the single-cell data science Challenges 1-3 2020-06-29 Group discussion Meeting notes
Special presentation: ""scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles" 2020-07-06 At special time: 10AM EDT/ 4PM CET Suoqin Jin video
Eleven grand challenges in the single-cell data science 2020-07-13 Simone Tiberi video
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data 2020-07-20 Robert Castelo video
2020-07-27 cancelled for BioC2020
"Single-cell multimodal omics: the power of many" 2020-09-14 Rajiv Tripathi video

Article ideas

These are just suggestions to help people find articles, but definitely feel free to choose others not listed here.

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Nature Methods January 2020 special issue

  1. Method of the Year 2019: Single-cell multimodal omics. Nat. Methods 2020, 17:1.
  2. Eisenstein M: The secret life of cells. Nat. Methods 2020, 17:7--10.
  3. Schier AF: Single-cell biology: beyond the sum of its parts. Nat. Methods 2020, 17:17--20.
  4. Efremova M, Teichmann SA: Computational methods for single-cell omics across modalities. Nat. Methods 2020, 17:14--17.
  5. Zhu C, Preissl S, Ren B: Single-cell multimodal omics: the power of many. Nat. Methods 2020, 17:11--14.

Review papers

  1. Navin NE: The first five years of single-cell cancer genomics and beyond. Genome Res. 2015, 25:1499--1507.
  2. Hu Y, An Q, Sheu K, Trejo B, Fan S, Guo Y: Single Cell Multi-Omics Technology: Methodology and Application. Front Cell Dev Biol 2018, 6:28.
  3. Macaulay IC, Ponting CP, Voet T: Single-Cell Multiomics: Multiple Measurements from Single Cells. Trends Genet. 2017, 33:155--168.
  4. Amezquita RA, Lun ATL, Becht E, Carey VJ, Carpp LN, Geistlinger L, Marini F, Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pagès H, Smith ML, Huber W, Morgan M, Gottardo R, Hicks SC: Orchestrating single-cell analysis with Bioconductor. Nat. Methods 2020, 17:137--145.
  5. Trapnell C: Defining cell types and states with single-cell genomics. Genome Research 2015, 25:1491-1498.
  6. Leonavicius K, Nainys J, Kuciauskas D and Mazutis L: Multi-omics at single-cell resolution: comparison of experimental and data fusion approaches. Current opinion in biotechnology 2019, 55, 159-166.
  7. Stuart T and Satija R: Integrative single-cell analysis. Nature Reviews Genetics 2019, 20:257-272.

Technologies for RNA + DNA

  1. Mateo LJ, Murphy SE, Hafner A, Cinquini IS, Walker CA, Boettiger AN: Visualizing DNA folding and RNA in embryos at single-cell resolution. Nature 2019, 568:49--54.
  2. Bronner IF, Lorenz S: Combined Genome and Transcriptome (G&T) Sequencing of Single Cells. In Single Cell Methods: Sequencing and Proteomics. edited by Proserpio V New York, NY: Springer New York; 2019:319--362.
  3. Macaulay IC, Haerty W, Kumar P, Li YI, Hu TX, Teng MJ, Goolam M, Saurat N, Coupland P, Shirley LM, Smith M, Van der Aa N, Banerjee R, Ellis PD, Quail MA, Swerdlow HP, Zernicka-Goetz M, Livesey FJ, Ponting CP, Voet T: G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 2015, 12:519.
  4. Dey SS, Kester L, Spanjaard B, Bienko M, van Oudenaarden A: Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 2015, 33:285--289.

Technologies for RNA + protein

  1. Peterson VM, Zhang KX, Kumar N, Wong J, Li L, Wilson DC, Moore R, McClanahan TK, Sadekova S, Klappenbach JA: Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 2017, 35:936--939.
  2. Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, Satija R, Smibert P: Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 2017, 14:865--868.

Technologies for RNA + epigenetics

  1. Angermueller C, Clark SJ, Lee HJ, Macaulay IC, Teng MJ, Hu TX, Krueger F, Smallwood S, Ponting CP, Voet T, Kelsey G, Stegle O, Reik W: Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 2016, 13:229--232.
  2. Cao J, Cusanovich DA, Ramani V, Aghamirzaie D, Pliner HA, Hill AJ, Daza RM, McFaline-Figueroa JL, Packer JS, Christiansen L and Steemers FJ: Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 2018, 361:1380-1385.

Technologies for DNA + epigenetics

  1. Li G, Liu Y, Zhang Y, Kubo N, Yu M, Fang R, Kellis M, Ren B: Joint profiling of DNA methylation and chromatin architecture in single cells. Nat. Methods 2019, 16:991--993.

Technologies for 3+ modes

  1. Hou Y, Guo H, Cao C, Li X, Hu B, Zhu P, Wu X, Wen L, Tang F, Huang Y, Peng J: Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 2016, 26:304--319.

Technologies for spatial

  1. Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E, Vanderburg CR, Welch J, Chen LM, Chen F and Macosko EZ: Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 2019., 363:1463-1467.
  2. Stickels RR, Murray E, Kumar P, Li J, Marshall JL, Di Bella D, Arlotta P, Macosko EZ and Chen F: Sensitive spatial genome wide expression profiling at cellular resolution. bioRxiv 2020.