Single-Cell Transcriptomics Bioinformatics and Computational Challenges
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Aaron, 2016, Pooling across cells to normalize single-cell RNA sequencing data with many zero counts, Genome Biol., 17, 75, 10.1186/s13059-016-0947-7
Amir, 2013, viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia, Nat. Biotechnol., 31, 545, 10.1038/nbt.2594
Anders, 2010, Differential expression analysis for sequence count data, Genome Biol., 11, R106, 10.1186/gb-2010-11-10-r106
Anders, 2014, HTSeq—a python framework to work with high-throughput sequencing data, Bioinformatics, 31, 166, 10.1093/bioinformatics/btu638
Andrews, 2010, FastQC: a quality control tool for high throughput sequence data
Balasubramanian, 2002, The isomap algorithm and topological stability, Science, 295, 7, 10.1126/science.295.5552.7a
Barron, 2016, Identifying and removing the cell-cycle effect from single-cell rna-sequencing data. arXiv:1605.04492
Bendall, 2014, Single-cell trajectory detection uncovers progression and regulatory coordination in human b cell development, Cell, 157, 714, 10.1016/j.cell.2014.04.005
Beyer, 1999, When Is ‘Nearest Neighbor’ Meaningful?, DATABASE Theory–ICDT'99, 217, 10.1007/3-540-49257-7_15
Bolger, 2014, Trimmomatic: a flexible trimmer for illumina sequence data, Bioinformatics, 30, 2114, 10.1093/bioinformatics/btu170
Bose, 2015, Scalable microfluidics for single cell rna printing and sequencing, Genome Biol., 16, 120, 10.1186/s13059-015-0684-3
Bray, 2016, Near-optimal probabilistic RNA-seq quantification, Nat. Biotechnol., 34, 525, 10.1038/nbt.3519
Brennecke, 2013, Accounting for technical noise in single-cell RNA-seq experiments, Nat. Methods, 10, 1093, 10.1038/nmeth.2645
Buettner, 2015, Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells, Nat. Biotechnol., 33, 55, 10.1038/nbt.3102
Buettner, 2012, A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst, Bioinformatics, 28, i626, 10.1093/bioinformatics/bts385
Campbell, 2015, Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell rna-seq profiles, bioRxiv, 27219, 10.1101/027219
Chandramohan, 2013, Benchmarking RNA-Seq quantification tools, Engineering In Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, 647, 10.1109/EMBC.2013.6609583
Ching, 2016, Pan-Cancer analyses reveal long intergenic non-coding rnas relevant to tumor diagnosis, subtyping and prognosis, EBioMedicine, 7, 62, 10.1016/j.ebiom.2016.03.023
Cox, 2010, SolexaQA: at-a-glance quality assessment of illumina second-generation sequencing data, BMC Bioinformatics, 11, 485, 10.1186/1471-2105-11-485
der Maaten, 2008, Visualizing data using T-SNE, J. Mach. Learn. Res., 9, 2579
Dey, 2015, Integrated genome and transcriptome sequencing of the same cell, Nat. Biotechnol., 33, 285, 10.1038/nbt.3129
Diaz, 2016, SCell: integrated analysis of single-cell RNA-Seq data, Bioinformatics, 32, 2219, 10.1093/bioinformatics/btw201
Ding, 2015, Normalization and noise reduction for single cell RNA-Seq experiments, Bioinformatics, 31, 2225, 10.1093/bioinformatics/btv122
Dobin, 2015, Mapping RNA-seq reads with STAR, Curr. Protoc. Bioinform., 51, 11.14.1, 10.1002/0471250953.bi1114s51
Engström, 2013, Systematic evaluation of spliced alignment programs for RNA-seq data, Nat. Methods, 10, 1185, 10.1038/nmeth.2722
Fan, 2016, Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis, Nat. Methods, 13, 241, 10.1038/nmeth.3734
Finak, 2015, MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data, Genome Biol., 16, 278, 10.1186/s13059-015-0844-5
Fonseca, 2014, RNA-Seq gene profiling-a systematic empirical comparison, PloS ONE, 9, e107026, 10.1371/journal.pone.0107026
Freeman, 2016, Single-Cell RNA-seq reveals activation of unique gene groups as a consequence of stem cell-parenchymal cell fusion, Sci. Rep., 6, 23270, 10.1038/srep23270
Gao, 2016, Integrative single-cell transcriptomics reveals molecular networks defining neuronal maturation during postnatal neurogenesis, Cereb. Cortex, 10.1093/cercor/bhw040
Grün, 2015, Design and analysis of single-cell sequencing experiments, Cell, 163, 799, 10.1016/j.cell.2015.10.039
Guo, 2015, SINCERA: a Pipeline for Single-Cell RNA-Seq profiling analysis, PLoS Comput. Biol., 11, e1004575, 10.1371/journal.pcbi.1004575
Haghverdi, 2015, Diffusion maps for high-dimensional single-cell analysis of differentiation data, Bioinformatics, 31, 2989, 10.1093/bioinformatics/btv325
Han, 2014, Co-detection and sequencing of genes and transcripts from the same single cells facilitated by a microfluidics platform, Sci. Rep., 4, 6485, 10.1038/srep06485
Handel, 2016, Assessing similarity to primary tissue and cortical layer identity in induced pluripotent stem cell-derived cortical neurons through single-cell transcriptomics, Hum. Mol. Genet, 25, 989, 10.1093/hmg/ddv637
Hartuv, 2000, A clustering algorithm based on graph connectivity, Inf. Process. Lett., 76, 175, 10.1016/S0020-0190(00)00142-3
Hou, 2016, Single-Cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas, Cell Res., 26, 304, 10.1038/cr.2016.23
Ilicic, 2016, Classification of low quality cells from single-cell RNA-seq data, Genome Biol., 17, 29, 10.1186/s13059-016-0888-1
Islam, 2014, Quantitative single-Cell RNA-Seq with unique molecular identifiers, Nat. Methods, 11, 163, 10.1038/nmeth.2772
Jaitin, 2014, Massively parallel Single-Cell RNA-Seq for marker-free decomposition of tissues into cell types, Science, 343, 776, 10.1126/science.1247651
Ji, 2016, TSCAN: pseudo-time reconstruction and evaluation in Single-Cell RNA-Seq analysis, Nucl. Acids Res, 44, e117, 10.1093/nar/gkw430
Jiang, 2016, GiniClust: detecting rare cell types from single-cell gene expression data with gini index, Genome Biol., 17, 144, 10.1186/s13059-016-1010-4
Jiang, 2016, Quality control of Single-Cell RNA-seq by SinQC, Bioinformatics, 10.1093/bioinformatics/btw176
Johnson, 2007, Adjusting batch effects in microarray expression data using empirical bayes methods, Biostatistics, 8, 118, 10.1093/biostatistics/kxj037
Katayama, 2013, SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization, Bioinformatics, 29, 2943, 10.1093/bioinformatics/btt511
Katrib, 2016, Radiotranscriptomics: a synergy of imaging and transcriptomics in clinical assessment, Quant. Biol., 4, 1, 10.1007/s40484-016-0061-6
Kharchenko, 2014, Bayesian approach to single-cell differential expression analysis, Nat. Methods, 11, 740, 10.1038/nmeth.2967
Kim, 2015, HISAT: a fast spliced aligner with low memory requirements, Nat. Methods, 12, 357, 10.1038/nmeth.3317
Kim, 2013, TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions, Genome Biol., 14, R36, 10.1186/gb-2013-14-4-r36
Kim, 2015, Single-Cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells, Genome Biol., 16, 127, 10.1186/s13059-015-0692-3
Kimmerling, 2016, A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages, Nat. Commun., 7, 10220, 10.1038/ncomms10220
Kumar, 2014, Deconstructing transcriptional heterogeneity in pluripotent stem cells, Nature, 516, 56, 10.1038/nature13920
Kvastad, 2015, Single cell analysis of cancer cells using an improved RT-MLPA method has potential for cancer diagnosis and monitoring, Sci. Rep., 5, 16519, 10.1038/srep16519
Leek, 2014, Svaseq: removing batch effects and other unwanted noise from sequencing data, Nucleic Acids Res, 42, 10.1093/nar/gku864
Leng, 2016, OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data, Bioinformatics, 32, 1408, 10.1093/bioinformatics/btw004
Leng, 2015, Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments, Nat. Methods, 12, 947, 10.1038/nmeth.3549
Levine, 2015, Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis, Cell, 162, 184, 10.1016/j.cell.2015.05.047
Li, 2011, RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome, BMC Bioinformatics, 12, 323, 10.1186/1471-2105-12-323
Li, 2009, The sequence alignment/map format and SAMtools, Bioinformatics, 25, 2078, 10.1093/bioinformatics/btp352
Li, 2013, Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-seq data, Stat. Methods Med. Res., 22, 519, 10.1177/0962280211428386
Liao, 2013, featurecounts: an efficient general purpose program for assigning sequence reads to genomic features, Bioinformatics, 10.1093/bioinformatics/btt656
Lohr, 2014, Whole exome sequencing of circulating tumor cells provides a window into metastatic prostate cancer, Nat. Biotechnol., 32, 479, 10.1038/nbt.2892
Love, 2014, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol., 15, 1, 10.1101/002832
Macaulay, 2015, G&T-Seq: parallel sequencing of single-cell genomes and transcriptomes, Nat. Methods, 12, 519, 10.1038/nmeth.3370
Marco, 2014, Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape, Proc. Natl. Acad. Sci., 111, E5643, 10.1073/pnas.1408993111
Martin, 2011, Cutadapt removes adapter sequences from high-throughput sequencing reads, EMBnet. J., 17, 10, 10.14806/ej.17.1.200
Meyer, 2016, Dnmt3a haploinsufficiency transforms Flt3-ITD myeloproliferative disease into a rapid, spontaneous, and fully-penetrant acute myeloid leukemia, Cancer Discov, 6, 501, 10.1158/2159-8290.CD-16-0008
Miyamoto, 2015, RNA-seq of single prostate CTCs implicates noncanonical wnt signaling in antiandrogen resistance, Science, 349, 1351, 10.1126/science.aab0917
Moignard, 2015, Decoding the regulatory network of early blood development from single-cell gene expression measurements, Nat. Biotechnol., 33, 269, 10.1038/nbt.3154
Navin, 2011, Tumour evolution inferred by single-cell sequencing, Nature, 472, 90, 10.1038/nature09807
Ntranos, 2016, Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts, bioRxiv, 17, 112, 10.1186/s13059-016-0970-8
Patel, 2014, Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma, Science, 344, 1396, 10.1126/science.1254257
Petropoulos, 2016, Single-cell RNA-seq reveals lineage and x chromosome dynamics in human preimplantation embryos, Cell, 165, 1012, 10.1016/j.cell.2016.03.023
Pettit, 2014, Identifying cell types from spatially referenced single-cell expression datasets, PLoS Comput Biol, 10, e1003824, 10.1371/journal.pcbi.1003824
Pierson, 2015, ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis, Genome Biol., 16, 1, 10.1186/s13059-015-0805-z
Pollen, 2014, Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex, Nat. Biotechnol., 32, 1053, 10.1038/nbt.2967
Prabhakaran, 2016, Dirichlet process mixture model for correcting technical variation in single-cell gene expression data, Proceedings of The 33rd International Conference on Machine Learning, 1070
Ramsköld, 2012, Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells, Nat. Biotechnol., 30, 777, 10.1038/nbt.2282
Robinson, 2010, edgeR: a bioconductor package for differential expression analysis of digital gene expression data, Bioinformatics, 26, 139, 10.1093/bioinformatics/btp616
Rotem, 2015, Single-Cell ChIP-seq reveals cell subpopulations defined by chromatin state, Nat. Biotechnol., 33, 1165, 10.1038/nbt.3383
Satija, 2015, Spatial reconstruction of single-cell gene expression data, Nat. Biotechnol., 33, 495, 10.1038/nbt.3192
Schurch, 2016, How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?, RNA, 22, 839, 10.1261/rna.053959.115
Shekhar, 2014, Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE), Proc. Natl. Acad. Sci.U.S.A., 111, 202, 10.1073/pnas.1321405111
Shin, 2015, Single-Cell RNA-Seq with waterfall reveals molecular cascades underlying adult neurogenesis, Cell Stem Cell, 17, 360, 10.1016/j.stem.2015.07.013
Tang, 2010, Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-seq analysis, Cell Stem Cell, 6, 468, 10.1016/j.stem.2010.03.015
Tenenbaum, 2000, A global geometric framework for nonlinear dimensionality reduction, Science, 290, 2319, 10.1126/science.290.5500.2319
Ting, 2014, Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells, Cell Rep., 8, 1905, 10.1016/j.celrep.2014.08.029
Tirosh, 2016, Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq, Science, 352, 189, 10.1126/science.aad0501
Trapnell, 2014, Pseudo-temporal ordering of individual cells reveals dynamics and regulators of cell fate decisions, Nat. Biotechnol., 32, 381, 10.1038/nbt.2859
Trapnell, 2009, TopHat: discovering splice junctions with RNA-seq, Bioinformatics, 25, 1105, 10.1093/bioinformatics/btp120
Trapnell, 2010, Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation, Nat. Biotechnol., 28, 511, 10.1038/nbt.1621
Travers, 2015, Non-coding yet non-trivial: a review on the computational genomics of lincRNAs, BioData Min., 8, 44, 10.1186/s13040-015-0075-z
Treutlein, 2014, Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq, Nature, 509, 371, 10.1038/nature13173
Tsafrir, 2005, Sorting points into neighborhoods (SPIN): data analysis and visualization by ordering distance matrices, Bioinformatics, 21, 2301, 10.1093/bioinformatics/bti329
Vallejos, 2015, BASiCS: Bayesian analysis of single-cell sequencing data, PLoS Comput. Biol., 11, e1004333, 10.1371/journal.pcbi.1004333
Vu, 2016, Beta-poisson model for single-cell RNA-seq data analyses, Bioinformatics, 32, 2128, 10.1093/bioinformatics/btw202
Wang, 2016, Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning, bioRxiv., 52225, 10.1101/052225
Wang, 2012, Multiple graph regularized protein domain ranking, BMC Bioinformatics, 13, 307, 10.1186/1471-2105-13-307
Wang, 2010, MapSplice: accurate mapping of RNA-seq reads for splice junction discovery, Nucleic Acids Res., 38, e178, 10.1093/nar/gkq622
Welch, 2016, SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data, Genome Biol., 17, 106, 10.1186/s13059-016-0975-3
Wu, 2016, GMAP and GSNAP for genomic sequence alignment: enhancements to speed, accuracy, and functionality, Stat. Genomics Methods Protoc, 1418, 283, 10.1007/978-1-4939-3578-9_15
Xu, 2015, Identification of cell types from single-cell transcriptomes using a novel clustering method, Bioinformatics, 31, 1974, 10.1093/bioinformatics/btv088
Yan, 2013, Single-cell RNA-seq profiling of human preimplantation embryos and embryonic stem cells, Nat. Struct. Mol. Biol., 20, 1131, 10.1038/nsmb.2660
Yang, 2013, HTQC: a fast quality control toolkit for illumina sequencing data, BMC Bioinformatics, 14, 33, 10.1186/1471-2105-14-33
Zeisel, 2015, Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq, Science, 347, 1138, 10.1126/science.aaa1934
Zhang, 2011, BIGpre: a quality assessment package for next-generation sequencing data, Genomics, Proteomics Bioinformatics, 9, 238, 10.1016/S1672-0229(11)60027-2
Zhu, 2016, Constructing 3D interaction maps from 1D epigenomes, Nat. Commun., 7, 10812, 10.1038/ncomms10812