Computational Systems Biology Methods and Protocols.7z

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changes and characterizing heterogeneity in
single-cell RNA sequencing data. Genome
Biol 16.https://doi.org/10.1186/S13059-
015-0844-5


  1. Delmans M, Hemberg M (2016) Discrete
    distributional differential expression ((DE)-
    E-3) – a tool for gene expression analysis of
    single-cell RNA-seq data. BMC Bioinformat-
    ics 17. https://doi.org/10.1186/S12859-
    016-0944-6

  2. Korthauer KD, Chu LF, Newton MA, Li Y,
    Thomson J, Stewart R, Kendziorski C (2016)
    A statistical approach for identifying differen-
    tial distributions in single-cell RNA-seq
    experiments. Genome Biol 17(1):222.
    https://doi.org/10.1186/s13059-016-
    1077-y

  3. Korthauer KD, Chu L-F, Newton MA, Li Y,
    Thomson J, Stewart R, Kendziorski C (2016)
    scDD: a statistical approach for identifying
    differential distributions in single-cell RNA-
    seq experiments. bioRxiv 2016:035501

  4. Jia C, Kelly D, Kim J, Li M, Zhang N (2017)
    Accounting for technical noise in single-cell
    RNA sequencing analysis. bioRxiv
    2017:116939

  5. Svensson V, Teichmann SA, Stegle O (2017)
    SpatialDE-identification of spatially variable
    genes. bioRxiv 2017:143321

  6. Grun D, Lyubimova A, Kester L, Wiebrands
    K, Basak O, Sasaki N, Clevers H, van Oude-
    naarden A (2015) Single-cell messenger RNA
    sequencing reveals rare intestinal cell types.
    Nature 525(7568):251.https://doi.org/10.
    1038/nature14966

  7. Zeisel A, Munoz-Manchado AB, Codeluppi
    S, Lonnerberg P, La Manno G, Jureus A,
    Marques S, Munguba H, He LQ, Betsholtz
    C, Rolny C, Castelo-Branco G, Hjerling-Lef-
    fler J, Linnarsson S (2015) Cell types in the
    mouse cortex and hippocampus revealed by
    single-cell RNA-seq. Science 347
    (6226):1138–1142. https://doi.org/10.
    1126/science.aaa1934

  8. Pierson E, Yau C (2015) ZIFA: dimensional-
    ity reduction for zero-inflated single-cell gene
    expression analysis. Genome Biol 16.https://
    doi.org/10.1186/S13059-015-0805-Z

  9. Angerer P, Haghverdi L, Buttner M, Theis FJ,
    Marr C, Buettner F (2016) destiny: diffusion
    maps for large-scale single cell data in R. Bio-
    informatics 32(8):1241–1243. https://doi.
    org/10.1093/bioinformatics/btv715

  10. Xu C, Su ZC (2015) Identification of cell
    types from single-cell transcriptomes using a
    novel clustering method. Bioinformatics 31
    (12):1974–1980.https://doi.org/10.1093/
    bioinformatics/btv088

  11. Marco E, Karp RL, Guo GJ, Robson P, Hart
    AH, Trippa L, Yuan GC (2014) Bifurcation
    analysis of single-cell gene expression data
    reveals epigenetic landscape. Proc Natl Acad
    Sci U S A 111(52):E5643–E5650.https://
    doi.org/10.1073/pnas.1408993111

  12. Leng N, Chu LF, Barry C, Li Y, Choi J, Li
    XM, Jiang P, Stewart RM, Thomson JA,
    Kendziorski C (2015) Oscope identifies oscil-
    latory genes in unsynchronized single-cell
    RNA-seq experiments. Nat Methods 12
    (10):947–950. https://doi.org/10.1038/
    Nmeth.3549

  13. Ji ZC, Ji HK (2016) TSCAN: pseudo-time
    reconstruction and evaluation in single-cell
    RNA-seq analysis. Nucleic Acids Res 44(13).
    https://doi.org/10.1093/nar/gkw430

  14. Specht AT, Li J (2017) LEAP: constructing
    gene co-expression networks for single-cell
    RNA-sequencing data using pseudotime
    ordering. Bioinformatics 33(5):764–766.
    https://doi.org/10.1093/bioinformatics/
    btw729

  15. Welch JD, Hartemink AJ, Prins JF (2016)
    SLICER: inferring branched, nonlinear cellu-
    lar trajectories from single cell RNA-seq data.
    Genome Biol 17.https://doi.org/10.1186/
    S13059-016-0975-3

  16. duVerle D, Yotsukura S, Nomura S, Aburatani
    H, Tsuda K (2016) CellTree: an R/biocon-
    ductor package to infer the hierarchical struc-
    ture of cell populations from single-cell RNA-
    seq data. BMC Bioinformatics 17.https://
    doi.org/10.1186/S12859-016-1175-6

  17. Rashid S, Kotton DN, Bar-Joseph Z (2017)
    TASIC: determining branching models from
    time series single cell data. Bioinformatics.
    https://doi.org/10.1093/bioinformatics/
    btx173

  18. Lo ̈nnberg T, Svensson V, James KR, Fernan-
    dez-Ruiz D, Sebina I, Montandon R, Soon
    MS, Fogg LG, Nair AS, Liligeto U (2017)
    Single-cell RNA-seq and computational anal-
    ysis using temporal mixture modelling
    resolves Th1/Tfh fate bifurcation in malaria.
    Sci Immunol 2(9):eaa12192

  19. Matsumoto H, Kiryu H (2016) SCOUP: a
    probabilistic model based on the Ornstein-
    Uhlenbeck process to analyze single-cell
    expression data during differentiation. BMC
    Bioinformatics 17. https://doi.org/10.
    1186/S12859-016-1109-3

  20. McCarthy DJ, Campbell KR, Lun ATL, Wills
    QF (2017) Scater: pre-processing, quality
    control, normalization and visualization of
    single-cell RNA-seq data in R. Bioinformatics
    33(8):1179–1186. https://doi.org/10.
    1093/bioinformatics/btw777


Applications of Single-Cell Sequencing for Multiomics 369
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