debCAM-package: debCAM: A package for fully unsupervised deconvolution of...
In Lululuella/debCAM: Deconvolution by Convex Analysis of Mixtures
Description
The core function in this package is CAM which achieves fully
unsupervised deconvolution on mixture expression profiles.
Each step in CAM can also be performed separately by
CAMPrep, CAMMGCluster
and CAMASest in a more flexible workflow.
MGstatistic can help extract a complete marker list from CAM
results. MDL can help decide the underlying subpopulation
number. With other functions, e.g. AfromMarkers and
MGstatistic, this package can also perform supervised
deconvolution based on prior knowledge of molecular markers,
subpopulation-specific expression matrix (S) or proportion matrix (A).
Semi-supervised deconvolution can be achieved by combining molecular markers
from CAM and from prior knowledge to analyze mixture expressions.
References
Wang, N., Hoffman, E. P., Chen, L., Chen, L., Zhang, Z.,
Liu, C., … Wang, Y. (2016). Mathematical modelling of transcriptional
heterogeneity identifies novel markers and subpopulations in complex
tissues. Scientific Reports, 6, 18909. http://doi.org/10.1038/srep18909
Lululuella/debCAM documentation built on May 14, 2021, 2:45 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description
The core function in this package is CAM which achieves fully
unsupervised deconvolution on mixture expression profiles.
Each step in CAM can also be performed separately by
CAMPrep, CAMMGCluster
and CAMASest in a more flexible workflow.
MGstatistic can help extract a complete marker list from CAM
results. MDL can help decide the underlying subpopulation
number. With other functions, e.g. AfromMarkers and
MGstatistic, this package can also perform supervised
deconvolution based on prior knowledge of molecular markers,
subpopulation-specific expression matrix (S) or proportion matrix (A).
Semi-supervised deconvolution can be achieved by combining molecular markers
from CAM and from prior knowledge to analyze mixture expressions.
References
Wang, N., Hoffman, E. P., Chen, L., Chen, L., Zhang, Z., Liu, C., … Wang, Y. (2016). Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues. Scientific Reports, 6, 18909. http://doi.org/10.1038/srep18909
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.