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.gitignore 100644 0 kb
Makevars 100644 0 kb
Makevars.win 100644 0 kb
RcppExports.cpp 100644 24 kb
attr_graph.cpp 100644 4 kb
attributeConnector.cpp 100644 3 kb
attribute_overlap.cpp 100644 6 kb
bicluster_methods.cpp 100644 7 kb
boost_graph.cpp 100644 1 kb
boost_graph.h 100644 0 kb
boost_types.h 100644 1 kb
boost_write.cpp 100644 2 kb
boost_write.h 100644 0 kb
distance_matrix.cpp 100644 10 kb
distance_matrix.h 100644 8 kb
extractBF.cpp 100644 3 kb
extractQUBIC2.cpp 100644 3 kb
full_graph.cpp 100644 4 kb
misc.cpp 100644 1 kb
network_edge_strength.cpp 100644 6 kb
sample_biclusters.cpp 100644 2 kb
sample_biclusters.h 100644 2 kb
write_matrix.cpp 100644 1 kb
README.md
# MoSBi - Molecular signatures using Biclustering R package of the biclustering ensemble algorithm MoSBi. For information about the algorithm please have a look at the publication: >MoSBi: Automated signature mining for molecular stratification and subtyping > >Tim Daniel Rose, Thibault Bechtler, Octavia-Andreea Ciora, Kim Anh Lilian Le, Florian Molnar, Nikolai Koehler, Jan Baumbach, Richard Röttger, Josch Konstantin Pauling > >Proceedings of the National Academy of Sciences, 2022; 119 (16): e2118210119; doi: [https://doi.org/10.1073/pnas.2118210119](https://doi.org/10.1073/pnas.2118210119) ### Installation The easiest way to install MoSBi is from Bioconductor. You can find the package [here](https://bioconductor.org/packages/mosbi/) and install is with this command: ``` r if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("mosbi") ``` For experienced users, the package can be installed directly from GitHub: ``` r # install.packages("devtools") devtools::install_github("tdrose/mosbi") ``` ### License This software is published under the AGPLv3 license. ![AGPLv3 logo](https://www.gnu.org/graphics/agplv3-with-text-162x68.png)