Pipeline

Unregistered user, login or register

  1. Srivastava, A. et al. (2019) Alevin efficiently estimates accurate gene abundances from dscRNA-seq data. Genome Biol, 20.
  2. Soneson C, Srivastava A (2020). alevinQC: Generate QC Reports For Alevin Output. R package version 1.4.0, https://github.com/csoneson/alevinQC
  3. Chen,S. et al. (2018) Fastp: An ultra-fast all-in-one FASTQ preprocessor. In, Bioinformatics., pp. i884–i890.
  4. Patro,R. et al. (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods, 14, 417–419.
  5. Stuart,T. et al. (2019) Comprehensive Integration of Single-Cell Data. Cell, 7, 1888-1902.
  6. Lun,A.T.L. et al. (2019) EmptyDrops: Distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol., 20.
  7. Aran,D. et al. (2019) Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol., 20, 163–172.
  8. Hartigan, J. A. and Wong, M. A. (1979) Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28, 100-108. 10.2307/2346830.
  9. Kaufman, L. and Rousseeuw, P. J. (1990) Finding Groups in Data: An Introduction to ClusterAnalysis. Wiley series in probability and mathematical statistics, Wiley, Chapter 2, 68-125.
  10. McQuitty, L.L. (1966) Similarity Analysis by Reciprocal Pairs for Discrete and Continuous Data. Educational and Psychological Measurement, 26, 825-831. 10.1177/001316446602600402.
  11. Calinski, R. B., and Harabasz, J. (1974) A Dendrite Method for Cluster Analysis, Communications in Statistics, 3, 1-27.
  12. Qiu,X. et al. (2017) Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods, 14, 979-982.
  13. Mardia, K. V., J. T. Kent, and J. M. Bibby (1979) Multivariate Analysis, London: Academic Press.
  14. L.J.P. van der Maaten and G.E. Hinton (2008). Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9, 2579-2605.
  15. McInnes, L. and Healy, J. (2018) UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv e-prints 1802.03426.
  16. Pierson, Emma and Yau, Christopher (2015) ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome biology, 16, 1
  17. Lee DD and Seung H (2001). Algorithms for non-negative matrix factorization. Advances in neural information processing systems.
  18. Borg, I. and Groenen, P. (1997) Modern Multidimensional Scaling - Theory and Applications. Springer Series in Statistics.
  19. Barrios,D. and Prieto,C. (2018) RJSplot: Interactive Graphs with R. Mol. Inform., 37.
  20. Love,M.I. et al. (2014) Differential analysis of count data - the DESeq2 package. Genome Biol., 15, 550.
  21. Robinson,M.D. et al. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139–140.
  22. Law,C.W. et al. (2014) Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol., 15.
  23. Young,M.D. et al. (2010) Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol., 11.
  24. Sergushichev,A. (2016) An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. bioRxiv, 60012.