An R package to integrate PANDA, LIONESS, CONDOR, and ALPACA.
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R update Jul 31, 2018
inst add priori mapping motif files Jan 29, 2019
man Add ALPACA Jul 30, 2018
NAMESPACE Add ALPACA Jul 30, 2018 Update Jul 31, 2018


An R package to integrate pypanda--Python implementation of PANDA and LIONESS, R implementation of CONDOR, and R implementation of ALPACA

PANDA(Passing Attributes between Networks for Data Assimilation) is a message-passing model to gene regulatory network reconstruction. It integrates multiple sources of biological data, including protein-protein interaction, gene expression, and sequence motif information, in order to reconstruct genome-wide, condition-specific regulatory networks.[Glass et al. 2013]

LIONESS(Linear Interpolation to Obtain Network Estimates for Single Samples) is a method to estimate sample-specific regulatory networks by applying linear interpolation to the predictions made by existing aggregate network inference approaches.[LIONESS arxiv paper]

CONDOR (COmplex Network Description Of Regulators) implements methods for clustering biapartite networks and estimatiing the contribution of each node to its community's modularity.[Platig et al. 2016]

ALPACA(ALtered Partitions Across Community Architectures) is a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. [Padi and Quackenbush 2018]

Table of Contents

Getting Started


Use this pacakage requires Python 2.7 and R (>= 3.3.3) and Internet access.

Python version and installation information is available here.

R version and installation information is available here.

There are also some Python packages required to apply Python implementation of PANDA and LIONESS.

How to install packages in different platforms could be find here.

The required Python packages are: pandas, numpy, networkx, matplotlib.pyplot.


This package could be downloaded via install_github() function from devtools package.


Running the sample datasets

Package CONDOR, igprah, viridisLite, and ALPACA are loaded with this package for further downstream analyses.

Use search() to check all loaded package currently.


Access help pages for usage of six core functions.


Use example datasets within package to test this package.

Refer to four input datasets files: one TB control expression dataset, one TB treated expression dataset, one motif sequence dataset, and one protein-protein interaction datasets in inst/extdat. All datasets are public data.

treated_expression_file_path <- system.file("extdata", "expr4.txt", package = "netZoo", mustWork = TRUE)
control_expression_file_path <- system.file("extdata", "expr10.txt", package = "netZoo", mustWork = TRUE)
motif_file_path <- system.file("extdata", "chip.txt", package = "netZoo", mustWork = TRUE)
ppi_file_path <- system.file("extdata", "ppi.txt", package = "netZoo", mustWork = TRUE)

PANDA and plot PANDA network

Assign the paths of treated expression dataset, motif dataset, and ppi dataset above to flag e(refers to "expression dataset"), m(refers to "=motif dataset"), and ppi(refers to "PPI" dataset) respectively. Then set option rm_missing to TRUE to run PANDA to generate an aggregate network for treated. Repeat but alter the paths of treated expression dataset to control expression datasets to generate an aggregate network for control. vector treated_all_panda_result and vector control_all_panda_result below are large lists with three elements: the entire PANDA network, indegree ("to" nodes) nodes and score, outdegree ("from" nodes) nodes and score. Use $panda,$indegree and '$outdegree' to access each item resepctively.

treated_all_panda_result <- runPanda(e = treated_expression_file_path, m = motif_file_path, ppi = ppi_file_path, rm_missing = TRUE )
control_all_panda_result <- runPanda(e = control_expression_file_path, m = motif_file_path, ppi = ppi_file_path, rm_missing = TRUE )

Use $pandato access the entire PANDA network.

treated_net <- treated_all_panda_result$panda

Plot the 100 edge with the largest weight of two PANDA network. Besides, one message will be returned to indicate the location of output .png plot.

plotPanda(top =100, file="treated_panda_100.png")

Repeat with networl of control.

control_net <- control_all_panda_result$panda
plotPanda(top =100, file="control_panda_100.png")

LIONESS and plot LIONESS network

The method how to run LIONESS is mostly idential with method how to run PANDA in this package, unless the return values of runLioness is a data frame where first two columns represent TFs (regulators) and Genes (targets) while the rest columns represent each sample. each cell filled with estimated score calculated by LIONESS.

treated_lioness <- runLioness(e = treated_expression_file_path, m = motif_file_path, ppi = ppi_file_path, rm_missing = TRUE )

Plot LIONESS network should clarify which sample by 0-based index.

plotLioness(col = 0, top = 100, file = "treat_lioness_sample1_100.png")

Repeat with control.

control_lioness <- runLioness(e = control_expression_file_path, m = motif_file_path, ppi = ppi_file_path, rm_missing = TRUE )
plotLioness(col = 0, top = 100, file = "control_lioness_sample1_100.png")


run CONDOR with a threshold to select edges. Defaults to threshold is the average of [median weight of non-prior edges] and [median weight of prior edges], all weights mentioned previous are transformationed with formula w'=ln(e^w+1) before calculating the median and average. But all the edges selected will remain the orginal weights calculated by PANDA before applying CONDOR.

treated_condor_object <- runCondor(treated_net, threshold = 0)
control_condor_object <- runCondor(control_net, threshold = 0)

plot communities. package igraph and package viridisLite (a color map package) are already loaded with this package.

treated network:

treated_color_num <- max(treated_condor_object$red.memb$com)
treated_color <- viridis(treated_color_num, alpha = 1, begin = 0, end = 1, direction = 1, option = "D")
condor.plot.communities(treated_condor_object, color_list=treated_color, point.size=0.04, xlab="Target", ylab="Regulator")

control network:

control_color_num <- max(control_condor_object$red.memb$com)
control_color <- viridis(control_color_num, alpha = 1, begin = 0, end = 1, direction = 1, option = "D")
condor.plot.communities(control_condor_object, color_list=control_color , point.size=0.04, xlab="Target", ylab="Regulator")


run LIONESS with two PANDA network above as first two arguments.

alpaca_result<- runAlpaca(treated_net, control_net, "~/Desktop/TB", verbose=T)

Further information

Future Work

Use vignette("condor") to access the vignette page of condor package and vignette("ALPACA") to access the vignette page of ALPACA package for any downstream analyses.


If there is an error like Error in fetch(key) : lazy-load database.rdb' is corrupt when accessing the help pages of functions in this package after being loaded. It's a limitation of base R and has not been solved yet. Restart R session and re-load this package will help.