https://github.com/magStra/GPseudoClust

Code for the GPseudoClust method, including pre- and postprocessing steps. Magdalena E Strauß, Paul DW Kirk, John E Reid, Lorenz Wernisch (2019); GPseudoClust: deconvolution of shared pseudo-trajectories at single-cell resolution.
https://github.com/magStra/GPseudoClust

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Code for the GPseudoClust method, including pre- and postprocessing steps. Magdalena E Strauß, Paul DW Kirk, John E Reid, Lorenz Wernisch (2019); GPseudoClust: deconvolution of shared pseudo-trajectories at single-cell resolution.

• Host: GitHub
• URL: https://github.com/magStra/GPseudoClust
• Owner: magStra
• License: gpl-3.0
• Created: 2019-02-21T20:44:27.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-08-24T15:55:43.000Z (almost 5 years ago)
• Last Synced: 2024-02-24T13:31:07.181Z (4 months ago)
• Homepage:
• Size: 12.7 MB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome_single_cell - GPseudoClust - [Python] - Software that clusters genes for pseudotemporally ordered data and quantifies the uncertainty in cluster allocations arising from the uncertainty in the pseudotime ordering. (Software packages / RNA-seq)
• awesome-single-cell - GPseudoClust - [Python] - Software that clusters genes for pseudotemporally ordered data and quantifies the uncertainty in cluster allocations arising from the uncertainty in the pseudotime ordering. (Software packages / Marker and differential gene expression identification)
• awesome-single-cell - GPseudoClust - [Python] - Software that clusters genes for pseudotemporally ordered data and quantifies the uncertainty in cluster allocations arising from the uncertainty in the pseudotime ordering. (Software packages / RNA-seq)

README

        
# GPseudoClust

Code for the GPseudoClust method, including pre- and postprocessing steps. 

Magdalena E Strauß, Paul DW Kirk, John E Reid, Lorenz Wernisch (2019); 

GPseudoClust: deconvolution of shared pseudo-trajectories at single-cell resolution.

Author of the code: ME Strauss

A tutorial is provided in .mlx and .pdf format in the files GPseudoClustByExample.mlx and GPseudoClustByExample.pdf (

inside GPseudoClust folder).

The clustering method can be run without downloading additional software. 

However, for postprocessing our R package for combining PSMs using nonparametric Bayesian methods is required

library(devtools)

install_github("magStra/nonparametricSummaryPSM")

https://github.com/magStra/nonparametricSummaryPSM

For plotting we use the subaxis function by Aslak Grinsted:

https://www.mathworks.com/matlabcentral/fileexchange/3696-subaxis-subplot

The folder lmkk_summaryMatrixRepresentation contains additional methods for postprocessing, which use the following software, which requires 

separate download:

1) Code implementing the localised kernel k-means method available at https://github.com/mehmetgonen/lmkkmeans,

Gönen, M. and Margolin, A.A. (2014). Localized data fusion for kernel k-means clustering with application to cancer biology. 

In Advances in Neural Information Processing Systems 27, pages 1305-1313.

2) The Mosek optimisation software (https://www.mosek.com/).

3) The SIMLR software, where one of the functions is used for the estimation of the optimal number of clusters for the summary 

clustering in the post-processing. 

Wang, B. et al. (2017). Visualization and analysis of single-cell RNA-seq data by

kernel-based similarity learning. Nat Meth, 14, 414-416.

https://github.com/BatzoglouLabSU/SIMLR