Single-cell Related Papers

This site contains candidate single-cell related papers which may be discussed later. Welcome to supplement.

Review

• Stubbington, Michael JT, et al. "Single-cell transcriptomics to explore the immune system in health and disease." Science 358.6359 (2017): 58-63. [link]

Methods

• Kharchenko, Peter V., Lev Silberstein, and David T. Scadden. "Bayesian approach to single-cell differential expression analysis." Nature methods 11.7 (2014): 740-742. [link]

• Fan, Jean, et al. "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis." Nature methods 13.3 (2016): 241-244. [link]

• Qiu, Xiaojie, et al. "Reversed graph embedding resolves complex single-cell trajectories." Nature 201 (2017): 7.[link]

• Rajaram, Satwik, et al. "Sampling strategies to capture single-cell heterogeneity." Nature methods 14.10 (2017): 967.[link]

• Aibar, S., et al. (2017). "SCENIC: single-cell regulatory network inference and clustering." Nat Methods. [link]

• Kiselev, Vladimir Yu, et al. "SC3: consensus clustering of single-cell RNA-seq data." Nature methods (2017). [link]

• Qiu, Xiaojie, et al. "Single-cell mRNA quantification and differential analysis with Census." Nature methods 14.3 (2017): 309-315. [link]

• Bacher, Rhonda, et al. "SCnorm: robust normalization of single-cell RNA-seq data." Nature Methods 14.6 (2017): 584-586. [link]

• Roth, Andrew, et al. "Clonal genotype and population structure inference from single-cell tumor sequencing." Nature methods 13.7 (2016): 573-576. [link]

• Shun H. Yip, Panwen Wang, Jean-Pierre A. Kocher, Pak Chung Sham, Junwen Wang; Linnorm: improved statistical analysis for single cell RNA-seq expression data, Nucleic Acids Research (2017) [link]

• Zhe Sun, Ting Wang, Ke Deng, Xiao-Feng Wang, Robert Lafyatis, Ying Ding, Ming Hu, Wei Chen; DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data, Bioinformatics (2017) [link]

• Haghverdi, Laleh, et al. "Diffusion pseudotime robustly reconstructs lineage branching." Nature methods 13.10 (2016): 845-848. [link]

Technology and applications

• Svensson, Valentine, et al. "Power analysis of single-cell RNA-sequencing experiments." Nature methods (2017).[link]

• Datlinger, Paul, et al. "Pooled CRISPR screening with single-cell transcriptome readout." Nature methods 14.3 (2017): 297-301. [link]

• Liu, Z., et al. (2017). "Single-cell transcriptomics reconstructs fate conversion from fibroblast to cardiomyocyte." Nature advance online publication. [link]

• Enge, Martin, et al. "Single-Cell Analysis of Human Pancreas Reveals Transcriptional Signatures of Aging and Somatic Mutation Patterns." Cell (2017). [link]

• van den Brink, S. C., et al. (2017). "Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations." Nat Meth 14(10): 935-936. [link]

• Zheng, Chunhong, et al. "Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing." Cell 169.7 (2017): 1342-1356. [link]

Biorxiv

• Daniel A, Single cell transcriptional profiling reveals cellular diversity, communication, and sexual dimorphism in the mouse heart. Biorxiv [link]

• Giovanni Iacono, et al. bigSCale: An Analytical Framework for Big-Scale Single-Cell Data. Biorxiv [link]

• Jiarui Ding, et al. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Biorxiv [link]

• Peter V. Kharchenko, et al. Accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments. Biorxiv [link]

• Donghyung Lee, et al. A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes. Biorxiv [link]