FUCHIKOMA-package: Revealing differentially expressed genes using nonlinear...

In rikenbit/FUCHIKOMA: Revealing differentially expressed genes using nonlinear dimensionality reduction for single cell RNA-Seq data

Description

fuchikoma detects differentially expressed genes (DEGs) in one of multiple clusters. To detect DEGs, fuchikoma has two calculation mode; "supervised-mode" and "unsupervised-mode". In supervised-mode, fuchikoma detects DEGs by using a label vector, in which the cluster of each sample or cell is written. In unsupervised-mode, fuchikoma detects DEGs without the label vector. In this mode, user run the diffusion map, and specify which diffusion components contribute to the difference of such cluster.

Details

The DESCRIPTION file: This package was not yet installed at build time.

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The main function is fuchikoma, which returns an object containing the calculation results.

Author(s)

Koki Tsuyuzaki, Haruka Ozaki, Mika Yoshimura, Itoshi Nikaido

Maintainer: Koki Tsuyuzaki <[e-mail]>

References

Koki Tsuyuzaki et al. (2015) fuchikoma: Detection of Differentially Expressed Genes in one of multiple clusters using BAHSIC and Diffusion Map. R package version 1.0.0

L. J. P. van der Maaten et al. (2008) Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579-2605

Laleh Haghverdi et al. (2015) Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics, 31(18), 2989-2998

Le Song et al. (2007) Gene selection via the BAHSIC family of algorithms, Bioinformatics, 23(13), i490-i498

Y-h Taguchi et al. (2015) Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease, BMC Bioinformatics, 16(139)

Diego Adhemar Jaitin et al. (2014) Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types. Science, 343 (6172): 776-779

Arthur Gretton et al. (2007) A Kernel Statistical Test of Independence, NIPS 21

Aaditya Ramdas et al. (2015) Nonparametric Independence Testing for Small Sample Sizes, IJCAI-15

Marioni, J.C. and Mason, C.E. and Mane, S.M. and Stephens, M. and Gilad, Y. (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Research, 18: 1509–1517.

See Also

DiffusionMap

rikenbit/FUCHIKOMA documentation built on May 27, 2019, 9:09 a.m.