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cell-type COmputational Differential Estimation CellCODE

Online

A multi-step statistical framework that uses latent variable analysis to analyze differential expression from mixture samples. This approach is based on latent variable analysis and is computationally transparent, requires no additional experimental data, yet outperforms existing methods that use independent proportion measurements. CellCODE has few parameters that are robust and easy to interpret. The method can be used to track changes in proportion, improve power to detect differential expression and assign the differentially expressed genes to the correct cell-type.

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CellCODE classification

CellCODE specifications

Software type:
Package/Module
Restrictions to use:
None
Programming languages:
R
Stability:
Stable
Interface:
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
Computer skills:
Advanced
Maintained:
Yes

CellCODE distribution

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No versioning.

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Publications

Institution(s)

Computational and Systems Biology, University of Pittsburgh

Link to literature

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