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This is the development version of MPAC; to use it, please install the devel version of Bioconductor.

Multi-omic Pathway Analysis of Cancer

Bioconductor version: Development (3.20)

Multi-omic Pathway Analysis of Cancer (MPAC), integrates multi-omic data for understanding cancer mechanisms. It predicts novel patient groups with distinct pathway profiles as well as identifying key pathway proteins with potential clinical associations. From CNA and RNA-seq data, it determines genes’ DNA and RNA states (i.e., repressed, normal, or activated), which serve as the input for PARADIGM to calculate Inferred Pathway Levels (IPLs). It also permutes DNA and RNA states to create a background distribution to filter IPLs as a way to remove events observed by chance. It provides multiple methods for downstream analysis and visualization.

Author: Peng Liu [aut, cre] , Paul Ahlquist [aut], Irene Ong [aut], Anthony Gitter [aut]

Maintainer: Peng Liu <pliu55.wisc+bioconductor at>

Citation (from within R, enter citation("MPAC")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

MPAC: Multi-omic Pathway Analysis of Cancer HTML R Script
Reference Manual PDF


biocViews Clustering, ImmunoOncology, RNASeq, Sequencing, Software, Survival, Technology
Version 0.99.14
In Bioconductor since BioC 3.20 (R-4.4)
License GPL-3
Depends R (>= 4.4.0)
Imports data.table (>= 1.14.2), SummarizedExperiment(>= 1.30.2), BiocParallel(>= 1.28.3), fitdistrplus (>= 1.1), igraph (>= 1.4.3), BiocSingular(>= 1.10.0), S4Vectors(>= 0.32.3), SingleCellExperiment(>= 1.16.0), bluster(>= 1.4.0), fgsea(>= 1.20.0), scran(>= 1.22.1), ComplexHeatmap(>= 2.16.0), grid, stats
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Suggests rmarkdown, knitr, svglite, bookdown (>= 0.34), testthat (>= 3.0.0)
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Follow Installation instructions to use this package in your R session.

Source Package MPAC_0.99.14.tar.gz
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macOS Binary (x86_64) MPAC_0.99.14.tgz
macOS Binary (arm64) MPAC_0.99.14.tgz
Source Repository git clone
Source Repository (Developer Access) git clone
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