Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data

Genomics, Proteomics & Bioinformatics - Tập 19 - Trang 267-281 - 2021
Qianhui Huang1, Yu Liu2, Yuheng Du1, Lana X. Garmire2
1Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
2Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48105, USA

Tài liệu tham khảo

Plass, 2018, Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics, Science, 360, eaaq1723, 10.1126/science.aaq1723 Cao, 2019, The single-cell transcriptional landscape of mammalian organogenesis, Nature, 566, 496, 10.1038/s41586-019-0969-x Tabula Muris Consortium, 2018, Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris, Nature, 562, 367, 10.1038/s41586-018-0590-4 Yu, 2016, Single-cell transcriptome study as big data, Genomics Proteomics Bioinformatics, 14, 21, 10.1016/j.gpb.2016.01.005 Mu, 2019, Deciphering brain complexity using single-cell sequencing, Genomics Proteomics Bioinformatics, 17, 344, 10.1016/j.gpb.2018.07.007 Zappia, 2018, Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database, PLoS Comput Biol, 14, e1006245, 10.1371/journal.pcbi.1006245 Zhu, 2018, GranatumX: a community engaging and flexible software environment for single-cell analysis, bioRxiv, 385591 Bacher, 2016, Design and computational analysis of single-cell RNA-sequencing experiments, Genome Biol, 17, 63, 10.1186/s13059-016-0927-y Rostom, 2017, Computational approaches for interpreting scRNA-seq data, FEBS Lett, 591, 2213, 10.1002/1873-3468.12684 Stuart, 2019, Comprehensive integration of single-cell data, Cell, 177, 1888, 10.1016/j.cell.2019.05.031 Kiselev, 2018, scmap: projection of single-cell RNA-seq data across data sets, Nat Methods, 15, 359, 10.1038/nmeth.4644 Aran, 2019, Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage, Nat Immunol, 20, 163, 10.1038/s41590-018-0276-y de Kanter, 2019, CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing, Nucleic Acids Res, 47, e95, 10.1093/nar/gkz543 Tan, 2019, SingleCellNet: a computational tool to classify single cell RNA-seq data across platforms and across species, Cell Syst, 9, 207, 10.1016/j.cels.2019.06.004 Boufea, 2020, scID uses discriminant analysis to identify transcriptionally equivalent cell types across single cell RNA-seq data with batch effect, iScience, 23, 100914, 10.1016/j.isci.2020.100914 Pliner, 2019, Supervised classification enables rapid annotation of cell atlases, Nat Methods, 16, 983, 10.1038/s41592-019-0535-3 Zhang, 2019, SCINA: a semi-supervised subtyping algorithm of single cells and bulk samples, Genes, 10, 531, 10.3390/genes10070531 Teschendorff, 2017, A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies, BMC Bioinformatics, 18, 105, 10.1186/s12859-017-1511-5 Rand, 1971, Objective criteria for the evaluation of clustering methods, J Am Stat Assoc, 66, 846, 10.1080/01621459.1971.10482356 Rosenberg, 2007, V-measure: a conditional entropybased external cluster evaluation measure, 410 Butler, 2018, Integrating single-cell transcriptomic data across different conditions, technologies, and species, Nat Biotechnol, 36, 411, 10.1038/nbt.4096 Arisdakessian, 2019, DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data, Genome Biol, 20, 211, 10.1186/s13059-019-1837-6 Zappia, 2017, Splatter: simulation of single-cell RNA sequencing data, Genome Biol, 18, 174, 10.1186/s13059-017-1305-0 Abdelaal, 2019, A comparison of automatic cell identification methods for single-cell RNA sequencing data, Genome Biol, 20, 194, 10.1186/s13059-019-1795-z Welch, 2019, Single-cell multi-omic integration compares and contrasts features of brain cell identity, Cell, 177, 1873, 10.1016/j.cell.2019.05.006 Korsunsky, 2019, Fast, sensitive and accurate integration of single-cell data with Harmony, Nat Methods, 16, 1289, 10.1038/s41592-019-0619-0 Johansen, 2019, scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data, Genome Biol, 20, 166, 10.1186/s13059-019-1766-4 Duren, 2018, Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations, Proc Natl Acad Sci U S A, 115, 7723, 10.1073/pnas.1805681115 Zeng, 2019, DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data, Nat Commun, 10, 4613, 10.1038/s41467-019-12547-1 Ortega, 2017, Using single-cell multiple omics approaches to resolve tumor heterogeneity, Clin Transl Med, 6, 46, 10.1186/s40169-017-0177-y Muraro, 2016, A single-cell transcriptome atlas of the human pancreas, Cell Syst, 3, 385, 10.1016/j.cels.2016.09.002 Lawlor, 2017, Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetes, Genome Res, 27, 208, 10.1101/gr.212720.116 Zheng, 2017, Massively parallel digital transcriptional profiling of single cells, Nat Commun, 8, 14049, 10.1038/ncomms14049 Newman, 2015, Robust enumeration of cell subsets from tissue expression profiles, Nat Methods, 12, 453, 10.1038/nmeth.3337 Venet, 2001, Separation of samples into their constituents using gene expression data, Bioinformatics, 17, S279, 10.1093/bioinformatics/17.suppl_1.S279 Abbas, 2009, Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus, PLoS One, 4, e6098, 10.1371/journal.pone.0006098 Houseman, 2012, DNA methylation arrays as surrogate measures of cell mixture distribution, BMC Bioinformatics, 13, 86, 10.1186/1471-2105-13-86 Zhu, 2017, Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization, PeerJ, 5, e2888, 10.7717/peerj.2888 Poirion, 2018, Using single nucleotide variations in single-cell RNA-seq to identify subpopulations and genotype-phenotype linkage, Nat Commun, 9, 4892, 10.1038/s41467-018-07170-5