diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..a8e38348 --- /dev/null +++ b/.gitignore @@ -0,0 +1,7 @@ +.Rproj.user +.Rhistory +.RData +.Ruserdata + +*.Rproj +.Rbuildignore diff --git a/DESCRIPTION b/DESCRIPTION index 019173c7..fc51df6e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,13 +1,34 @@ Package: CARNIVAL Title: A CAusal Reasoning tool for Network Identification (from gene expression data) using Integer VALue programming Version: 1.1.0 -Author: Enio Gjerga - Panuwat Trairatphisan - Anika Liu - Alberto Valdeolivas - Nikolas Peschke -Maintainer: Enio Gjerga -Description: An upgraded causal reasoning tool from Melas et al in R with updated assignments of TFs' weights from PROGENy scores. +Authors@R: c( + person(given = "Enio", + family = "Gjerga", + role = c("aut"), + email = "enio.gjerga@gmail.com", + comment = c(ORCID = "0000-0002-3060-5786")), + person(given = "Panuwat", + family = "Trairatphisan", + role = "aut"), + person(given = "Anika", + family = "Liu", + role = "ctb"), + person(given = "Alberto", + family = "Valdeolivas", + role = "ctb"), + person(given = "Nikolas", + family = "Peschke", + role = "ctb"), + person(given = "Aurelien", + family = "Dugourd", + role = "ctb"), + person(given = "Olga", + family = "Ivanova", + role = "cre", + email = "olga.ivanova@bioquant.uni-heidelberg.de", + comment = c(ORCID = "0000-0002-9111-4593")) + ) +Description: An upgraded causal reasoning tool from Melas et al in R with updated assignments of TFs' weights from PROGENy scores. Optimization parameters can be freely adjusted and multiple solutions can be obtained and aggregated. URL: https://github.com/saezlab/CARNIVAL BugReports: https://github.com/saezlab/CARNIVAL/issues @@ -24,7 +45,7 @@ Imports: lpSolve, igraph biocViews: Transcriptomics, GeneExpression, Network -License: Apache License (== 3.0) | file LICENSE +License: GPL-3 + file LICENSE LazyData: true Encoding: UTF-8 Suggests: diff --git a/NEWS.md b/NEWS.md new file mode 100644 index 00000000..bb5146a7 --- /dev/null +++ b/NEWS.md @@ -0,0 +1,8 @@ +# CARNIVAL 1.0.0 +* Official release with Bioconductor 3.11 + +# CARNIVAL 0.99.44 +* Deployment of GitHub page + +# CARNIVAL 0.99.0 +* Initial submission to Bioconductor diff --git a/R/checkMeasObj.R b/R/checkMeasObj.R index 5185aca9..24b0145c 100755 --- a/R/checkMeasObj.R +++ b/R/checkMeasObj.R @@ -15,6 +15,24 @@ checkMeasObj <- function(measObj = measObj, netObj = netObj){ stop("Measurement object should either be of matrix or data.frame class") } else { if(ncol(measObj)>0){ + + colnames(measObj) <- gsub(pattern = "-", replacement = "_", + x = colnames(measObj), fixed = TRUE) + colnames(measObj) <- gsub(pattern = "+", replacement = "_", + x = colnames(measObj), fixed = TRUE) + colnames(measObj) <- gsub(pattern = "*", replacement = "_", + x = colnames(measObj), fixed = TRUE) + colnames(measObj) <- gsub(pattern = "/", replacement = "_", + x = colnames(measObj), fixed = TRUE) + colnames(measObj) <- gsub(pattern = "<", replacement = "_", + x = colnames(measObj), fixed = TRUE) + colnames(measObj) <- gsub(pattern = ">", replacement = "_", + x = colnames(measObj), fixed = TRUE) + colnames(measObj) <- gsub(pattern = "=", replacement = "_", + x = colnames(measObj), fixed = TRUE) + colnames(measObj) <- gsub(pattern = " ", replacement = "_", + x = colnames(measObj), fixed = TRUE) + mSpecies = colnames(measObj) idx = which(mSpecies%in%nSpecies) @@ -39,23 +57,6 @@ checkMeasObj <- function(measObj = measObj, netObj = netObj){ } } - colnames(measObj) <- gsub(pattern = "-", replacement = "_", - x = colnames(measObj), fixed = TRUE) - colnames(measObj) <- gsub(pattern = "+", replacement = "_", - x = colnames(measObj), fixed = TRUE) - colnames(measObj) <- gsub(pattern = "*", replacement = "_", - x = colnames(measObj), fixed = TRUE) - colnames(measObj) <- gsub(pattern = "/", replacement = "_", - x = colnames(measObj), fixed = TRUE) - colnames(measObj) <- gsub(pattern = "<", replacement = "_", - x = colnames(measObj), fixed = TRUE) - colnames(measObj) <- gsub(pattern = ">", replacement = "_", - x = colnames(measObj), fixed = TRUE) - colnames(measObj) <- gsub(pattern = "=", replacement = "_", - x = colnames(measObj), fixed = TRUE) - colnames(measObj) <- gsub(pattern = " ", replacement = "_", - x = colnames(measObj), fixed = TRUE) - return(measObj) -} \ No newline at end of file +} diff --git a/R/write_constraints_6.R b/R/write_constraints_6.R index b7217455..25c233ac 100755 --- a/R/write_constraints_6.R +++ b/R/write_constraints_6.R @@ -72,7 +72,7 @@ write_constraints_6 <- function(variables=variables, idx1 <- which(rowSums(adj)==0) idx2 <- setdiff(seq_len(nrow(adj)), idx1) - + cc1 = NULL if(length(idx1)>0){ cc1 <- @@ -82,7 +82,7 @@ write_constraints_6 <- function(variables=variables, rownames(adj)[idx1], " in experiment ", ii))], " <= 0") - } + }else cc1 <- NULL cc2 <- rep("", length(idx2)) for(i in seq_len(length(idx2))){ diff --git a/R/write_constraints_7.R b/R/write_constraints_7.R index 05f8adaa..9236bd8a 100755 --- a/R/write_constraints_7.R +++ b/R/write_constraints_7.R @@ -69,7 +69,7 @@ write_constraints_7 <- function(variables=variables, idx1 <- which(rowSums(adj)==0) idx2 <- setdiff(seq_len(nrow(adj)), idx1) - + cc1 <- rep("", length(idx1)) if (length(idx1)>0) { cc1 <- paste0( diff --git a/R/write_constraints_8.R b/R/write_constraints_8.R index f83bd81c..3aeaba92 100755 --- a/R/write_constraints_8.R +++ b/R/write_constraints_8.R @@ -91,6 +91,7 @@ write_constraints_8 <- function(variables=variables, ## constraints8 <- c(constraints8, cc) ## + cc4 = c() if (length( setdiff( as.character(pknList[, 1]), diff --git a/R/write_objective_function_all.R b/R/write_objective_function_all.R index 49134c25..b57962cd 100755 --- a/R/write_objective_function_all.R +++ b/R/write_objective_function_all.R @@ -219,7 +219,7 @@ write_objective_function_all <- function(dataMatrix = dataMatrix, if(!is.null(measWeights)){ - mm <- as.matrix(measWeights[i, ]) + mm <- as.matrix(measWeights[i, ,drop=FALSE]) } else { @@ -250,4 +250,4 @@ write_objective_function_all <- function(dataMatrix = dataMatrix, return(OF) -} \ No newline at end of file +} diff --git a/README.md b/README.md index 56273ec1..5e263961 100755 --- a/README.md +++ b/README.md @@ -1,14 +1,26 @@ -# CARNIVAL +# CARNIVAL -CARNIVAL is an R-package providing a framework to perform causal reasoning to infer a subset of signalling network from transcriptomics data. This work was originally based on [Melas et al.](https://pubs.rsc.org/en/content/articlehtml/2015/ib/c4ib00294f) with a number improved functionalities comparing to the original version. -Transcription factors’ (TFs) activities and pathway scores from gene expressions can be inferred with our in-house tools [DoRothEA](https://github.com/saezlab/DoRothEA) & [PROGENy](https://github.com/saezlab/progeny), respectively. -TFs’ activities and signed directed protein-protein interaction networks +/- drug targets and pathway scores are then used to derive a series of linear constraints to generate integer linear programming (ILP) problems. -An ILP solver (CPLEX) is subsequently applied to identify the sub-network topology with minimised discrepancies on fitting error and model size. + + -More detailed descriptions of CARNIVAL, benchmarking and applicational studies can be found on it's dedicated [web-page](https://saezlab.github.io/CARNIVAL/) and in [Liu, Trairatphisan, Gjerga et al.](https://www.nature.com/articles/s41540-019-0118-z): +## Overview +CARNIVAL (**CA**usal **R**easoning for **N**etwork identification using **I**nteger **VAL**ue programming) is a method for the identification of upstream reguatory signalling pathways from downstream gene expression (GEX). -> Liu A*, Trairatphisan P*, Gjerga E*, Didangelos A, Barratt J, Saez-Rodriguez J. (2019). From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL. *npj Systems Biology and Applications*, https://doi.org/10.1038/s41540-019-0118-z (*equal contributions). +This is a tool currently being developed by the [saezlab](http://saezlab.org/) members and is an extension of the previously implemented Causal Reasoning ([*Melas et al.*](http://pubs.rsc.org/en/content/articlehtml/2015/ib/c4ib00294f)) method. More detailed information on the CARNIVAL pipeline as well as benchmarking and applicational studies are available on following paper. +> Liu A., Trairatphisan P., Gjerga E. et al. [From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL](https://www.nature.com/articles/s41540-019-0118-z) _npj Systems Biology and Applications_ volume 5, Article number: 40 (2019) (equal contributions). + +The aim of the CARNIVAL pipeline is to identify a subset of interactions from a prior knowledge network that represent potential regulated pathways linking known or potential targets of perturbation towards active transcription factors derived from GEX data. The pipeline includes a number improved functionalities comparing to the original version and consists of the following processes: + + * 1) Transcription factors’ (TFs) activities and pathway scores from gene expressions can be inferred with our in-house tools DoRothEA & PROGENy, respectively. + + * 2) TFs’ activities and signed directed protein-protein interaction networks with or without the provided target of perturbations and pathway scores are then used to derive a series of linear constraints to generate integer linear programming (ILP) problems. + + * 3) An ILP solver (IBM ILOG CPLEX) is subsequently applied to identify the sub-network topology with minimised fitting error and model size. + +Applications of CARNIVAL include the identification of drug’s modes of action and of deregulated processes in diseases (even if the molecular targets remain unknown) by deciphering the alterations of main signalling pathways as well as alternative pathways and off-target effects. + + ## Getting Started @@ -18,45 +30,61 @@ A tutorial for preparing CARNIVAL input files starting from differentially gene CARNIVAL requires the interactive version of IBM Cplex or CBC-COIN solver as the network optimiser. The IBM ILOG Cplex is freely available through Academic Initiative [here](https://www.ibm.com/products/ilog-cplex-optimization-studio?S_PKG=CoG&cm_mmc=Search_Google-_-Data+Science_Data+Science-_-WW_IDA-_-+IBM++CPLEX_Broad_CoG&cm_mmca1=000000RE&cm_mmca2=10000668&cm_mmca7=9041989&cm_mmca8=kwd-412296208719&cm_mmca9=_k_Cj0KCQiAr93gBRDSARIsADvHiOpDUEHgUuzu8fJvf3vmO5rI0axgtaleqdmwk6JRPIDeNcIjgIHMhZIaAiwWEALw_wcB_k_&cm_mmca10=267798126431&cm_mmca11=b&mkwid=_k_Cj0KCQiAr93gBRDSARIsADvHiOpDUEHgUuzu8fJvf3vmO5rI0axgtaleqdmwk6JRPIDeNcIjgIHMhZIaAiwWEALw_wcB_k_|470|135655&cvosrc=ppc.google.%2Bibm%20%2Bcplex&cvo_campaign=000000RE&cvo_crid=267798126431&Matchtype=b&gclid=Cj0KCQiAr93gBRDSARIsADvHiOpDUEHgUuzu8fJvf3vmO5rI0axgtaleqdmwk6JRPIDeNcIjgIHMhZIaAiwWEALw_wcB). The [CBC](https://projects.coin-or.org/Cbc) solver is open source and freely available for any user. Alternatively for smaller cases, users can rely on the freely available [lpSolve R-package](https://cran.r-project.org/web/packages/lpSolve/index.html). -### Installing +### Installation -CARNIVAL is currently available for the installation as an R-package from our GitHub page +```r +# install from bioconductor +if (!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager") -```R -# Install CARNIVAL from Github using devtools -# install.packages('devtools') # in case devtools hasn't been installed -library(devtools) -install_github('saezlab/CARNIVAL', build_vignettes = TRUE) -# or download the source file from GitHub and install from source -install.packages('path_to_extracted_CARNIVAL_directory', repos = NULL, type="source") +BiocManager::install("CARNIVAL") + +# install the development version from GitHub +# install.packages("devtools") +devtools::install_github("saezlab/CARNIVAL") ``` +### Inputs and Outputs of CARNIVAL + +The input for CARNIVAL consists of: + + * A prior knowledge network (PKN) comprises a list of signed and directed interactions between signalling proteins. (Required) + + * Inferred transcription factor activities which can be inferred from GEX data using [DoRothEA](https://github.com/saezlab/dorothea). (Required) + + * A list of target of perturbations (drugs, diseases, etc.) with or without their effects on signalling proteins. (Optional) + + * Inferred pathway scores representing signalling pathway activities from GEX data using [PROGENy](https://github.com/saezlab/progeny) (Optional) + +The outcome of CARNIVAL includes the list of identified networks that fitted to the provided experimental data as well as the predicted activities of signalling proteins in the networks whether they are up- or down-regulated. + ## Running CARNIVAL To obtain the list of tutorials/vignettes of the CARNIVAL package, user can start with typing the following commmand on R-console: -```R +```r vignette("CARNIVAL-vignette") ``` -## License - -Distributed under the GNU GPLv3 License. See accompanying file [LICENSE.txt](https://github.com/saezlab/CARNIVAL/blob/master/LICENSE.txt) or copy at [http://www.gnu.org/licenses/gpl-3.0.html](http://www.gnu.org/licenses/gpl-3.0.html). - ## References [Melas et al.](https://pubs.rsc.org/en/content/articlehtml/2015/ib/c4ib00294f): > Melas IN, Sakellaropoulos T, Iorio F, Alexopoulos L, Loh WY, Lauffenburger DA, Saez-Rodriguez J, Bai JPF. (2015). Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury. *Integrative Biology*, Issue 7, Pages 904-920, https://doi.org/10.1039/C4IB00294F. -[DoRothEA v2 - Garcia-Alonso et al.](https://www.biorxiv.org/content/early/2018/06/03/337915): +[DoRothEA - Garcia-Alonso et al.](https://github.com/saezlab/dorothea): -> Garcia-Alonso L, Ibrahim MM, Turei D, Saez-Rodriguez J. (2018). Benchmark and integration of resources for the estimation of human transcription factor activities. *bioRXiv*, https://doi.org/10.1101/337915. +> Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J. (2018). Benchmark and integration of resources for the estimation of human transcription factor activities. *Genome Research* 2019. 29: 1363-1375, https://doi.org/10.1101/gr.240663.118 -[PROGENy - Schubert et al.](https://www.nature.com/articles/s41467-017-02391-6): +[PROGENy - Schubert et al.](https://github.com/saezlab/progeny): > Schubert M, Klinger B, Klünemann M, Sieber A, Uhlitz F, Sauer S, Garnett MJ, Blüthgen N, Saez-Rodriguez J. (2018). Perturbation-response genes reveal signaling footprints in cancer gene expression. *Nature Communication*, Issue 9, Nr. 20. https://doi.org/10.1038/s41467-017-02391-6. +## Studies where CARNIVAL was used + +> Buhl E.M. et al. [Dysregulated mesenchymal PDGFR‐β drives kidney fibrosis](https://www.embopress.org/doi/10.15252/emmm.201911021) _EMBO Mol Med (2020)e11021 doi: 10.15252/emmm.201911021_ + +> Binenbaum I. et. al. [Bioinformatic framework for analysis of transcription factor changes as the molecular link between replicative cellular senescence signaling pathways and carcinogenesis](https://link.springer.com/content/pdf/10.1007/s10522-020-09866-y.pdf) _Biogerontology doi: 10.1007/s10522-020-09866-y_ ## Acknowledgement diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 00000000..8d33df74 --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,13 @@ +destination: docs +navbar: + structure: + left: [home, intro, reference, articles, tutorials, news] + right: [github, twitter, homepage] + components: + twitter: + icon: "fab fa-twitter fa-lg" + href: https://twitter.com/saezlab + homepage: + icon: "fas fa-university" + href: http://www.saezlab.org + diff --git a/docs/404.html b/docs/404.html new file mode 100644 index 00000000..31250e2a --- /dev/null +++ b/docs/404.html @@ -0,0 +1,171 @@ + + + + + + + + +Page not found (404) • CARNIVAL + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Site built with pkgdown 1.5.1.

+
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+
+ + + + +
+
+ + + + +
+

+Introduction

+

While gene expression profiling is commonly used to gain an overview of cellular processes, the identification of upstream processes that drive expression changes remains a challenge. To address this issue, we introduce CARNIVAL (Liu, Trairatphisan, Gjerga et al 2019), a causal network contextualization tool which derives network architectures from gene expression footprints. CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) integrates different sources of prior knowledge including signed and directed protein-protein interactions, transcription factor targets, and pathway signatures.

+
+

+Pipeline

+

CARNIVAL refines a quantitative objective function for ILP problem by incorporating TF and pathway activities on a continuous scale. In addition, the CARNIVAL framework allows us to contextualize the network with or without known targets of perturbations. The implementation is separated into two pipelines which will be referred henceforth as Standard CARNIVAL StdCARNIVAL (with known perturbation targets as an input) and Inverse CARNIVAL InvCARNIVAL (without information on targets of perturbation). The differential gene expression is used to infer transcription factor (TF) activities with DoRothEA, which are subsequently discretized in order to formulate ILPconstraints. As a result, CARNIVAL derives a family of highest scoring networks which best explain the inferred TF activities. Continuous pathway and TF activities can be additionally considered in the objective function.

+
+
+

+ILP solvers

+

CARNIVAL is an extension of the previously implemented Causal Reasoning method from Melas et al.. The network inference process is swiftly performed with an Integer Linear Programming (ILP) formulation of causal reasoning using three solvers: the R-CRAN lpSolve free software used for solving linear problems; the open-source mixed integer programming solver Cbc (Coin-or branch and cut); or the CPLEX optimizer from IBM which can be obtained for free through the Academic Initiative. To perform the analysis with cplex or cbc, the users will then need to store the binary cbc or cplex executables on any directory they wish. The binary files of cbc can be found by first downloading one of the optimization suites provided here: https://www.coin-or.org/download/binary/OptimizationSuite/, unzip the download and from there save the cbc executable (which can be found on the bin directory) file on any of the direcotries they wish of their machines. As for the cplex, the executable file can be obtained after registration on the ILOG CPLEX Optimization Studio Similar like before, users will have to find the cplex executable binary file and save on a directory of their own wish or keep them on their default installation paths. The path to interactive version of CPLEX is differed based on the operating system. The default installation path for each OS is as follows:

+

For Mac OS:

+
# ~/Applications/IBM/ILOG/CPLEX_Studio129/cplex/bin/x86-64_osx/cplex
+

For Linux:

+
# /opt/ibm/ILOG/CPLEX_Studio129/cplex/bin/x86-64_linux/cplex
+

For Windows:

+
# C:/Program Files/IBM/ILOG/CPLEX_Studio129/cplex/bin/x64_win64/cplex.exe
+

Note that the version of CPLEX has to be changed accordingly (the latest current version is CPLEX-Studio129).

+

The lpSolve solver can be used after downloading and installing the lpSolve R-package. This solver only works for smaller examples and it can give only one optimal solution. For larger real-case examples, the users can use cbc or cplex solvers.

+
+
+

+Prerequisites

+

Besides the above mentioned solvers, users need also to install the following R-package dependencies: readr; igraph; readxl; dplyr; lpSolve

+

In order to visualize the automatically generated CARNIVAL networks, users will also need to download and install the Graph Visualization software graphviz.

+
+
+
+

+Running CARNIVAL

+

In the CARNIVAL package, built-in examples are available as the test cases as follows:

+
    +
  1. A small toy example where the inputs are known (stdCARNIVAL)

  2. +
  3. A small toy example where the inputs are not known (invCARNVAL)

  4. +
+
+

+Toy Example - 1

+

Toy example for the stdCARNIVAL pipeline:

+
library(CARNIVAL)
+
+load(file = system.file("toy_inputs_ex1.RData",
+                        package="CARNIVAL"))
+load(file = system.file("toy_measurements_ex1.RData",
+                        package="CARNIVAL"))
+load(file = system.file("toy_network_ex1.RData",
+                        package="CARNIVAL"))
+
+# lpSolve
+result = runCARNIVAL(inputObj = toy_inputs_ex1, measObj = toy_measurements_ex1,
+                     netObj = toy_network_ex1)
+
+print(result)
+
## $weightedSIF
+##      Node1 Sign Node2 Weight
+## [1,] "I1"  "1"  "N1"  "100" 
+## [2,] "N1"  "1"  "M1"  "100" 
+## [3,] "N1"  "1"  "M2"  "100" 
+## 
+## $nodesAttributes
+##      Node ZeroAct UpAct DownAct AvgAct NodeType
+## [1,] "I1" "0"     "100" "0"     "100"  "S"     
+## [2,] "N1" "0"     "100" "0"     "100"  ""      
+## [3,] "I2" "0"     "100" "0"     "100"  "S"     
+## [4,] "N2" "100"   "0"   "0"     "0"    ""      
+## [5,] "M1" "0"     "100" "0"     "100"  "T"     
+## [6,] "M2" "0"     "100" "0"     "100"  "T"     
+## 
+## $sifAll
+## $sifAll[[1]]
+##      Node1 Sign Node2
+## [1,] "I1"  "1"  "N1" 
+## [2,] "N1"  "1"  "M1" 
+## [3,] "N1"  "1"  "M2" 
+## 
+## 
+## $attributesAll
+## $attributesAll[[1]]
+##      Nodes Activity
+## [1,] "I1"  "1"     
+## [2,] "N1"  "1"     
+## [3,] "I2"  "1"     
+## [4,] "M1"  "1"     
+## [5,] "M2"  "1"
+
+
+

+Toy Example - 2

+

Toy example for the invCARNIVAL pipeline:

+
library(CARNIVAL) # load CARNIVAL library
+
+load(file = system.file("toy_measurements_ex2.RData",
+                        package="CARNIVAL"))
+load(file = system.file("toy_network_ex2.RData",
+                        package="CARNIVAL"))
+
+# lpSolve
+result = runCARNIVAL(measObj = toy_measurements_ex2, netObj = toy_network_ex2)
+
+print(result)
+
## $weightedSIF
+##      Node1          Sign Node2 Weight
+## [1,] "I2"           "1"  "N1"  "100" 
+## [2,] "I2"           "1"  "N2"  "100" 
+## [3,] "N1"           "1"  "M1"  "100" 
+## [4,] "N1"           "1"  "M2"  "100" 
+## [5,] "N2"           "1"  "M2"  "100" 
+## [6,] "N2"           "1"  "M3"  "100" 
+## [7,] "Perturbation" "1"  "I2"  "100" 
+## 
+## $nodesAttributes
+##       Node           ZeroAct UpAct DownAct AvgAct NodeType
+##  [1,] "I1"           "100"   "0"   "0"     "0"    ""      
+##  [2,] "I2"           "0"     "100" "0"     "100"  ""      
+##  [3,] "I3"           "100"   "0"   "0"     "0"    ""      
+##  [4,] "N1"           "0"     "100" "0"     "100"  ""      
+##  [5,] "N2"           "0"     "100" "0"     "100"  ""      
+##  [6,] "Perturbation" "0"     "100" "0"     "100"  "S"     
+##  [7,] "M1"           "0"     "100" "0"     "100"  "T"     
+##  [8,] "M2"           "0"     "100" "0"     "100"  "T"     
+##  [9,] "M3"           "0"     "100" "0"     "100"  "T"     
+## 
+## $sifAll
+## $sifAll[[1]]
+##      Node1          Sign Node2
+## [1,] "I2"           "1"  "N1" 
+## [2,] "I2"           "1"  "N2" 
+## [3,] "N1"           "1"  "M1" 
+## [4,] "N1"           "1"  "M2" 
+## [5,] "N2"           "1"  "M2" 
+## [6,] "N2"           "1"  "M3" 
+## [7,] "Perturbation" "1"  "I2" 
+## 
+## 
+## $attributesAll
+## $attributesAll[[1]]
+##      Nodes          Activity
+## [1,] "I2"           "1"     
+## [2,] "N1"           "1"     
+## [3,] "N2"           "1"     
+## [4,] "Perturbation" "1"     
+## [5,] "M1"           "1"     
+## [6,] "M2"           "1"     
+## [7,] "M3"           "1"
+
+
+

+R version information

+
## R version 4.0.2 (2020-06-22)
+## Platform: x86_64-apple-darwin17.0 (64-bit)
+## Running under: macOS Mojave 10.14.5
+## 
+## Matrix products: default
+## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
+## 
+## locale:
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+## 
+## attached base packages:
+## [1] stats     graphics  grDevices utils     datasets  methods   base     
+## 
+## other attached packages:
+## [1] CARNIVAL_1.0.1 knitr_1.29    
+## 
+## loaded via a namespace (and not attached):
+##  [1] httr_1.4.1           Biobase_2.48.0       mixtools_1.2.0      
+##  [4] UniProt.ws_2.28.0    bit64_0.9-7.1        splines_4.0.2       
+##  [7] foreach_1.5.0        assertthat_0.2.1     stats4_4.0.2        
+## [10] BiocFileCache_1.12.0 RBGL_1.64.0          blob_1.2.1          
+## [13] yaml_2.2.1           Category_2.54.0      viper_1.22.0        
+## [16] pillar_1.4.6         RSQLite_2.2.0        backports_1.1.8     
+## [19] lattice_0.20-41      glue_1.4.1           digest_0.6.25       
+## [22] colorspace_1.4-1     htmltools_0.5.0      Matrix_1.2-18       
+## [25] GSEABase_1.50.0      lpSolve_5.6.15       XML_3.99-0.4        
+## [28] pkgconfig_2.0.3      genefilter_1.70.0    purrr_0.3.4         
+## [31] xtable_1.8-4         scales_1.1.1         tibble_3.0.3        
+## [34] annotate_1.66.0      generics_0.0.2       IRanges_2.22.1      
+## [37] ggplot2_3.3.2        ellipsis_0.3.1       BiocGenerics_0.34.0 
+## [40] survival_3.1-12      magrittr_1.5         crayon_1.3.4        
+## [43] memoise_1.1.0        evaluate_0.14        fs_1.4.2            
+## [46] doParallel_1.0.15    MASS_7.3-51.6        segmented_1.2-0     
+## [49] class_7.3-17         graph_1.66.0         tools_4.0.2         
+## [52] hms_0.5.3            lifecycle_0.2.0      stringr_1.4.0       
+## [55] S4Vectors_0.26.0     kernlab_0.9-29       munsell_0.5.0       
+## [58] AnnotationDbi_1.50.0 compiler_4.0.2       pkgdown_1.5.1       
+## [61] e1071_1.7-3          rlang_0.4.7          grid_4.0.2          
+## [64] RCurl_1.98-1.2       iterators_1.0.12     rstudioapi_0.11     
+## [67] rappdirs_0.3.1       igraph_1.2.5         bitops_1.0-6        
+## [70] rmarkdown_2.3        gtable_0.3.0         codetools_0.2-16    
+## [73] curl_4.3             DBI_1.1.0            R6_2.4.1            
+## [76] dplyr_1.0.0          bit_1.1-15.2         rprojroot_1.3-2     
+## [79] KernSmooth_2.23-17   readr_1.3.1          desc_1.2.0          
+## [82] stringi_1.4.6        parallel_4.0.2       Rcpp_1.0.5          
+## [85] vctrs_0.3.2          dbplyr_1.4.4         tidyselect_1.1.0    
+## [88] xfun_0.15
+
+
+
+ + + +
+ + + +
+ +
+

Site built with pkgdown 1.5.1.

+
+ +
+
+ + + + + + diff --git a/docs/articles/CARNIVAL_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/CARNIVAL_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/articles/CARNIVAL_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/articles/index.html b/docs/articles/index.html new file mode 100644 index 00000000..07a1497d --- /dev/null +++ b/docs/articles/index.html @@ -0,0 +1,170 @@ + + + + + + + + +Articles • CARNIVAL + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + +
+ + + + +
+ + +
+

Site built with pkgdown 1.5.1.

+
+ +
+
+ + + + + + + + diff --git a/docs/authors.html b/docs/authors.html new file mode 100644 index 00000000..e04e9fe8 --- /dev/null +++ b/docs/authors.html @@ -0,0 +1,204 @@ + + + + + + + + +Citation and Authors • CARNIVAL + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + + +
+ +
+
+ + +

Liu A., Trairatphisan P., Gjerga E. et al. 'From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL.' npj Systems Biology and Applications volume 5, Article number: 40 (2019) (equal contributions).

+
@Article{,
+  author = {Anika Liu and Panuwat Trairatphisan and Enio Gjerga and Athanasios Didangelos and Jonathan Barratt and Julio Saez-Rodriguez},
+  title = {From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL},
+  journal = {npj Systems Biology and Applications},
+  year = {2019},
+  doi = {10.1038/s41540-019-0118-z},
+}
+ + + +
    +
  • +

    Enio Gjerga. Author. +

    +
  • +
  • +

    Panuwat Trairatphisan. Author. +

    +
  • +
  • +

    Anika Liu. Contributor. +

    +
  • +
  • +

    Alberto Valdeolivas. Contributor. +

    +
  • +
  • +

    Nikolas Peschke. Contributor. +

    +
  • +
  • +

    Aurelien Dugourd. Maintainer. +

    +
  • +
+ +
+ +
+ + + +
+ + +
+

Site built with pkgdown 1.5.1.

+
+ +
+
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+ -webkit-box-flex: 1; + -ms-flex: 1; + flex: 1 +} + +.algolia-autocomplete .ds-dropdown-menu { + width: 100%; + min-width: none; + max-width: none; + padding: .75rem 0; + background-color: #fff; + background-clip: padding-box; + border: 1px solid rgba(0, 0, 0, .1); + box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175); +} + +@media (min-width:768px) { + .algolia-autocomplete .ds-dropdown-menu { + width: 175% + } +} + +.algolia-autocomplete .ds-dropdown-menu::before { + display: none +} + +.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] { + padding: 0; + background-color: rgb(255,255,255); + border: 0; + max-height: 80vh; +} + +.algolia-autocomplete .ds-dropdown-menu .ds-suggestions { + margin-top: 0 +} + +.algolia-autocomplete .algolia-docsearch-suggestion { + padding: 0; + overflow: visible +} + +.algolia-autocomplete .algolia-docsearch-suggestion--category-header { + padding: .125rem 1rem; + margin-top: 0; + font-size: 1.3em; + font-weight: 500; + color: #00008B; + border-bottom: 0 +} + +.algolia-autocomplete .algolia-docsearch-suggestion--wrapper { + float: none; + padding-top: 0 +} + +.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column { + float: none; + width: auto; + padding: 0; + text-align: left +} + +.algolia-autocomplete .algolia-docsearch-suggestion--content { + float: none; + width: auto; + padding: 0 +} + +.algolia-autocomplete .algolia-docsearch-suggestion--content::before { + display: none +} + +.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header { + padding-top: .75rem; + margin-top: .75rem; + border-top: 1px solid rgba(0, 0, 0, .1) +} + +.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column { + display: block; + padding: .1rem 1rem; + margin-bottom: 0.1; + font-size: 1.0em; + font-weight: 400 + /* display: none */ +} + +.algolia-autocomplete .algolia-docsearch-suggestion--title { + display: block; + padding: .25rem 1rem; + margin-bottom: 0; + font-size: 0.9em; + font-weight: 400 +} + +.algolia-autocomplete .algolia-docsearch-suggestion--text { + padding: 0 1rem .5rem; + margin-top: -.25rem; + font-size: 0.8em; + font-weight: 400; + line-height: 1.25 +} + +.algolia-autocomplete .algolia-docsearch-footer { + width: 110px; + height: 20px; + z-index: 3; + margin-top: 10.66667px; + float: right; + font-size: 0; + line-height: 0; +} + +.algolia-autocomplete .algolia-docsearch-footer--logo { + background-image: url("data:image/svg+xml;utf8,"); + background-repeat: no-repeat; + background-position: 50%; + background-size: 100%; + overflow: hidden; + text-indent: -9000px; + width: 100%; + height: 100%; + display: block; + transform: translate(-8px); +} + +.algolia-autocomplete .algolia-docsearch-suggestion--highlight { + color: #FF8C00; + background: rgba(232, 189, 54, 0.1) +} + + +.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight { + box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5) +} + +.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content { + background-color: rgba(192, 192, 192, .15) +} diff --git a/docs/docsearch.js b/docs/docsearch.js new file mode 100644 index 00000000..b35504cd --- /dev/null +++ b/docs/docsearch.js @@ -0,0 +1,85 @@ +$(function() { + + // register a handler to move the focus to the search bar + // upon pressing shift + "/" (i.e. "?") + $(document).on('keydown', function(e) { + if (e.shiftKey && e.keyCode == 191) { + e.preventDefault(); + $("#search-input").focus(); + } + }); + + $(document).ready(function() { + // do keyword highlighting + /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ + var mark = function() { + + var referrer = document.URL ; + var paramKey = "q" ; + + if (referrer.indexOf("?") !== -1) { + var qs = referrer.substr(referrer.indexOf('?') + 1); + var qs_noanchor = qs.split('#')[0]; + var qsa = qs_noanchor.split('&'); + var keyword = ""; + + for (var i = 0; i < qsa.length; i++) { + var currentParam = qsa[i].split('='); + + if (currentParam.length !== 2) { + continue; + } + + if (currentParam[0] == paramKey) { + keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); + } + } + + if (keyword !== "") { + $(".contents").unmark({ + done: function() { + $(".contents").mark(keyword); + } + }); + } + } + }; + + mark(); + }); +}); + +/* Search term highlighting ------------------------------*/ + +function matchedWords(hit) { + var words = []; + + var hierarchy = hit._highlightResult.hierarchy; + // loop to fetch from lvl0, lvl1, etc. + for (var idx in hierarchy) { + words = words.concat(hierarchy[idx].matchedWords); + } + + var content = hit._highlightResult.content; + if (content) { + words = words.concat(content.matchedWords); + } + + // return unique words + var words_uniq = [...new Set(words)]; + return words_uniq; +} + +function updateHitURL(hit) { + + var words = matchedWords(hit); + var url = ""; + + if (hit.anchor) { + url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; + } else { + url = hit.url + '?q=' + escape(words.join(" ")); + } + + return url; +} diff --git a/docs/favicon-16x16.png b/docs/favicon-16x16.png new file mode 100644 index 00000000..9f0d87e1 Binary files /dev/null and b/docs/favicon-16x16.png differ diff --git a/docs/favicon-32x32.png b/docs/favicon-32x32.png new file mode 100644 index 00000000..ccce8949 Binary files /dev/null and b/docs/favicon-32x32.png differ diff --git a/docs/favicon.ico b/docs/favicon.ico new file mode 100644 index 00000000..64980d90 Binary files /dev/null and b/docs/favicon.ico differ diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 00000000..7dee8ca5 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,249 @@ + + + + + + + +A CAusal Reasoning tool for Network Identification (from gene expression data) using Integer VALue programming • CARNIVAL + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    + + + +
    +

    +Overview

    +

    CARNIVAL (CAusal Reasoning for Network identification using Integer VALue programming) is a method for the identification of upstream reguatory signalling pathways from downstream gene expression (GEX).

    +

    This is a tool currently being developed by the saezlab members and is an extension of the previously implemented Causal Reasoning (Melas et al.) method. More detailed information on the CARNIVAL pipeline as well as benchmarking and applicational studies are available on following paper.

    +
    +

    Liu A., Trairatphisan P., Gjerga E. et al. From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL npj Systems Biology and Applications volume 5, Article number: 40 (2019) (equal contributions).

    +
    +

    The aim of the CARNIVAL pipeline is to identify a subset of interactions from a prior knowledge network that represent potential regulated pathways linking known or potential targets of perturbation towards active transcription factors derived from GEX data. The pipeline includes a number improved functionalities comparing to the original version and consists of the following processes:

    +
      +
    • 1) Transcription factors’ (TFs) activities and pathway scores from gene expressions can be inferred with our in-house tools DoRothEA & PROGENy, respectively.

    • +
    • 2) TFs’ activities and signed directed protein-protein interaction networks with or without the provided target of perturbations and pathway scores are then used to derive a series of linear constraints to generate integer linear programming (ILP) problems.

    • +
    • 3) An ILP solver (IBM ILOG CPLEX) is subsequently applied to identify the sub-network topology with minimised fitting error and model size.

    • +
    +

    Applications of CARNIVAL include the identification of drug’s modes of action and of deregulated processes in diseases (even if the molecular targets remain unknown) by deciphering the alterations of main signalling pathways as well as alternative pathways and off-target effects.

    +

    +
    +
    +

    +Getting Started

    +

    A tutorial for preparing CARNIVAL input files starting from differentially gene expression (DEG) and for running the CARNIVAL pipeline are provided as vignettes in R-Markdown, R-script and HTML formats. The wrapper script “runCARNIVAL” was introduced to take input arguments, pre-process input descriptions, run optimisation and export results as network files and figures. Three built-in CARNIVAL examples are also supplied as case studies for users.

    +
    +

    +Prerequisites

    +

    CARNIVAL requires the interactive version of IBM Cplex or CBC-COIN solver as the network optimiser. The IBM ILOG Cplex is freely available through Academic Initiative here. The CBC solver is open source and freely available for any user. Alternatively for smaller cases, users can rely on the freely available lpSolve R-package.

    +
    +
    +

    +Installation

    +
    # install from bioconductor
    +if (!requireNamespace("BiocManager", quietly = TRUE))
    +    install.packages("BiocManager")
    +
    +BiocManager::install("CARNIVAL")
    +
    +# install the development version from GitHub
    +# install.packages("devtools")
    +devtools::install_github("saezlab/CARNIVAL")
    +
    +
    +

    +Inputs and Outputs of CARNIVAL

    +

    The input for CARNIVAL consists of:

    +
      +
    • A prior knowledge network (PKN) comprises a list of signed and directed interactions between signalling proteins. (Required)

    • +
    • Inferred transcription factor activities which can be inferred from GEX data using DoRothEA. (Required)

    • +
    • A list of target of perturbations (drugs, diseases, etc.) with or without their effects on signalling proteins. (Optional)

    • +
    • Inferred pathway scores representing signalling pathway activities from GEX data using PROGENy (Optional)

    • +
    +

    The outcome of CARNIVAL includes the list of identified networks that fitted to the provided experimental data as well as the predicted activities of signalling proteins in the networks whether they are up- or down-regulated.

    +
    +
    +
    +

    +Running CARNIVAL

    +

    To obtain the list of tutorials/vignettes of the CARNIVAL package, user can start with typing the following commmand on R-console:

    +
    vignette("CARNIVAL-vignette")
    +
    +
    +

    +References

    +

    Melas et al.:

    +
    +

    Melas IN, Sakellaropoulos T, Iorio F, Alexopoulos L, Loh WY, Lauffenburger DA, Saez-Rodriguez J, Bai JPF. (2015). Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury. Integrative Biology, Issue 7, Pages 904-920, https://doi.org/10.1039/C4IB00294F.

    +
    +

    DoRothEA - Garcia-Alonso et al.:

    +
    +

    Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J. (2018). Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Research 2019. 29: 1363-1375, https://doi.org/10.1101/gr.240663.118

    +
    +

    PROGENy - Schubert et al.:

    +
    +

    Schubert M, Klinger B, Klünemann M, Sieber A, Uhlitz F, Sauer S, Garnett MJ, Blüthgen N, Saez-Rodriguez J. (2018). Perturbation-response genes reveal signaling footprints in cancer gene expression. Nature Communication, Issue 9, Nr. 20. https://doi.org/10.1038/s41467-017-02391-6.

    +
    +
    +
    +

    +Studies where CARNIVAL was used

    +
    +

    Buhl E.M. et al. Dysregulated mesenchymal PDGFR‐β drives kidney fibrosis EMBO Mol Med (2020)e11021 doi: 10.15252/emmm.201911021

    +
    +
    +

    Binenbaum I. et. al. Bioinformatic framework for analysis of transcription factor changes as the molecular link between replicative cellular senescence signaling pathways and carcinogenesis Biogerontology doi: 10.1007/s10522-020-09866-y

    +
    +
    +
    +

    +Acknowledgement

    +

    CARNIVAL has been developed as a computational tool to analyse -omics data within the TransQST Consortium and H2020 Symbiosys ITN Training Network.

    +

    “This project has received funding by the European Union’s H2020 program (675585 Marie-Curie ITN ‘‘SymBioSys’’) and the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 116030. The Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.”

    +
    +
    +
    + + +
    + + +
    + +
    +

    Site built with pkgdown 1.5.1.

    +
    + +
    +
    + + + + + + diff --git a/docs/link.svg b/docs/link.svg new file mode 100644 index 00000000..88ad8276 --- /dev/null +++ b/docs/link.svg @@ -0,0 +1,12 @@ + + + + + + diff --git a/docs/logo.png b/docs/logo.png new file mode 100644 index 00000000..7770d72e Binary files /dev/null and b/docs/logo.png differ diff --git a/docs/news/index.html b/docs/news/index.html new file mode 100644 index 00000000..05712521 --- /dev/null +++ b/docs/news/index.html @@ -0,0 +1,190 @@ + + + + + + + + +Changelog • CARNIVAL + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    +CARNIVAL 1.0.0

    +
      +
    • Official release with Bioconductor 3.11
    • +
    +
    +
    +

    +CARNIVAL 0.99.44

    +
      +
    • Deployment of GitHub page
    • +
    +
    +
    +

    +CARNIVAL 0.99.0

    +
      +
    • Initial submission to Bioconductor
    • +
    +
    +
    + + + +
    + + +
    + + +
    +

    Site built with pkgdown 1.5.1.

    +
    + +
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padding-right: 10px;} +.ref-arguments th, .ref-arguments td {vertical-align: top;} +.ref-arguments .name {width: 20%;} +.ref-arguments .desc {width: 80%;} + +/* Nice scrolling for wide elements --------------------------------------- */ + +table { + display: block; + overflow: auto; +} + +/* Syntax highlighting ---------------------------------------------------- */ + +pre { + word-wrap: normal; + word-break: normal; + border: 1px solid #eee; +} + +pre, code { + background-color: #f8f8f8; + color: #333; +} + +pre code { + overflow: auto; + word-wrap: normal; + white-space: pre; +} + +pre .img { + margin: 5px 0; +} + +pre .img img { + background-color: #fff; + display: block; + height: auto; +} + +code a, pre a { + color: #375f84; +} + +a.sourceLine:hover { + text-decoration: none; +} + +.fl {color: #1514b5;} +.fu {color: #000000;} /* function */ +.ch,.st {color: #036a07;} /* string */ +.kw {color: #264D66;} /* keyword */ +.co {color: #888888;} /* comment */ + +.message { color: black; 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  • , and enclosing
  • if in dropdown + if (pos >= 0) { + var menu_anchor = $(links[pos]); + menu_anchor.parent().addClass("active"); + menu_anchor.closest("li.dropdown").addClass("active"); + } + }); + + function paths(pathname) { + var pieces = pathname.split("/"); + pieces.shift(); // always starts with / + + var end = pieces[pieces.length - 1]; + if (end === "index.html" || end === "") + pieces.pop(); + return(pieces); + } + + // Returns -1 if not found + function prefix_length(needle, haystack) { + if (needle.length > haystack.length) + return(-1); + + // Special case for length-0 haystack, since for loop won't run + if (haystack.length === 0) { + return(needle.length === 0 ? 0 : -1); + } + + for (var i = 0; i < haystack.length; i++) { + if (needle[i] != haystack[i]) + return(i); + } + + return(haystack.length); + } + + /* Clipboard --------------------------*/ + + function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle=element.getAttribute('data-original-title'); + element.setAttribute('data-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-original-title', tooltipOriginalTitle); + } + + if(ClipboardJS.isSupported()) { + $(document).ready(function() { + var copyButton = ""; + + $(".examples, div.sourceCode").addClass("hasCopyButton"); + + // Insert copy buttons: + $(copyButton).prependTo(".hasCopyButton"); + + // Initialize tooltips: + $('.btn-copy-ex').tooltip({container: 'body'}); + + // Initialize clipboard: + var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { + text: function(trigger) { + return trigger.parentNode.textContent; + } + }); + + clipboardBtnCopies.on('success', function(e) { + changeTooltipMessage(e.trigger, 'Copied!'); + e.clearSelection(); + }); + + clipboardBtnCopies.on('error', function() { + changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); + }); + }); + } +})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml new file mode 100644 index 00000000..d5b1ed99 --- /dev/null +++ b/docs/pkgdown.yml @@ -0,0 +1,7 @@ +pandoc: 2.7.3 +pkgdown: 1.5.1 +pkgdown_sha: ~ +articles: + CARNIVAL: CARNIVAL.html +last_built: 2020-07-21T12:51Z + diff --git a/docs/reference/figures/graphical_abstract.png b/docs/reference/figures/graphical_abstract.png new file mode 100644 index 00000000..e8ae41a3 Binary files /dev/null and b/docs/reference/figures/graphical_abstract.png differ diff --git a/docs/reference/figures/logo.png b/docs/reference/figures/logo.png new file mode 100644 index 00000000..7770d72e Binary files /dev/null and b/docs/reference/figures/logo.png differ diff --git a/docs/reference/index.html b/docs/reference/index.html new file mode 100644 index 00000000..3db2003f --- /dev/null +++ b/docs/reference/index.html @@ -0,0 +1,196 @@ + + + + + + + + +Function reference • CARNIVAL + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
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    + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    All functions

    +

    +
    +

    runCARNIVAL()

    +

    runCARNIVAL

    +
    + + +
    + + +
    + + +
    +

    Site built with pkgdown 1.5.1.

    +
    + +
    +
    + + + + + + + + diff --git a/docs/reference/runCARNIVAL.html b/docs/reference/runCARNIVAL.html new file mode 100644 index 00000000..195ababe --- /dev/null +++ b/docs/reference/runCARNIVAL.html @@ -0,0 +1,310 @@ + + + + + + + + +<code>runCARNIVAL</code> — runCARNIVAL • CARNIVAL + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    + +
    +
    + + +
    +

    Run CARNIVAL pipeline using to the user-provided list of inputs or run CARNIVAL +built-in examples

    +
    + +
    runCARNIVAL(inputObj=NULL, measObj=measObj, netObj=netObj, weightObj=NULL,
    +            solverPath=NULL, solver=c("lpSolve", "cplex", "cbc"),
    +            timelimit=3600, mipGAP=0.05, poolrelGAP=0.0001, limitPop=500,
    +            poolCap=100, poolIntensity=4, poolReplace=2, alphaWeight=1,
    +            betaWeight=0.2, threads = 0, dir_name=NULL)
    + +

    Arguments

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    inputObj

    Data frame of the list for target of perturbation - optional + or default set to NULL to run invCARNIVAL when inputs are not + known

    measObj

    Data frame of the measurement file (i.e. DoRothEA normalised + enrichment scores) - always required

    netObj

    Data frame of the prior knowledge network - always required

    weightObj

    Data frame of the additional weight (i.e. PROGENy pathway + score or measured protein activities) - optional or default set + as NULL to run CARNIVAL without weights

    solverPath

    Path to executable cbc/cplex file - default set to NULL, in + which case the solver from lpSolve package is used

    solver

    Solver to use: lpSolve/cplex/cbc (Default set to lpSolve)

    timelimit

    CPLEX/Cbc parameter: Time limit of CPLEX optimisation (in + seconds)

    mipGAP

    CPLEX parameter: the absolute tolerance on the gap between the + best integer objective and the objective of the best node + remaining. When this difference falls below the value of this + parameter, the linear integer optimization is stopped. Default set + to 0.05

    poolrelGAP

    CPLEX/Cbc parameter: Allowed relative gap of accepted solution + comparing within the pool of accepted solution (fraction; default: + 0.0001)

    limitPop

    CPLEX parameter: Allowed number of solutions to be generated + (default: 500)

    poolCap

    CPLEX parameter: Allowed number of solution to be kept in the + pool of solution (default: 100)

    poolIntensity

    CPLEX parameter: Intensity of solution searching (0,1,2,3,4 + - default: 4)

    poolReplace

    CPLEX parameter: Replacement strategy of solutions in the + pool (0,1,2 - default: 2 = most diversified solutions)

    alphaWeight

    Objective function: weight for mismatch penalty (default: 1 - + will only be applied once measurement file only contains + discrete values)

    betaWeight

    Objective function: weight for node penalty (default: 0.2)

    threads

    Number of threads to be used for the analysis (default: 0 - +meaning all threads)

    dir_name

    For plotting: define if DOT figure will be exported in the +directory defined by this variable. Default set to NULL, meaning that no .DOT +figure will be exported. If a valid path is specified, the figure will be +exported as 'network_solution.dot' file in the specified directory. If +specified dir_name path oes not exist, nos figure will be generated

    + +

    Value

    + +

    The networks and predicted node activities from the CARNIVAL pipeline as a +variable which are also optionally saved in the destined result folder

    + +

    Examples

    +
    ## loading toy example 1 + +load(file = system.file("toy_inputs_ex1.RData", +package="CARNIVAL")) +load(file = system.file("toy_measurements_ex1.RData", +package="CARNIVAL")) +load(file = system.file("toy_network_ex1.RData", +package="CARNIVAL")) + +## lpSolve +res1 = runCARNIVAL(inputObj = toy_inputs_ex1, measObj = toy_measurements_ex1, + netObj = toy_network_ex1)
    #> Writing constraints...
    #> Solving LP problem...
    #> Parsed with column specification: +#> cols( +#> `enter Problem` = col_character() +#> )
    +# ## cbc +# res2 = runCARNIVAL(inputObj = toy_inputs_ex1, measObj = toy_measurements_ex1, +# netObj = toy_network_ex1, solverPath = solverPath, +# solver = "cbc") +# +# ## cplex +# res3 = runCARNIVAL(inputObj = toy_inputs_ex1, measObj = toy_measurements_ex1, +# netObj = toy_network_ex1, solverPath = solverPath, +# solver = "cplex") + +
    +
    + +
    + + + +
    + + + + + + + + diff --git a/inst/CITATION b/inst/CITATION new file mode 100644 index 00000000..0fc647aa --- /dev/null +++ b/inst/CITATION @@ -0,0 +1,15 @@ +citEntry( + entry="article", + author = c(person("Anika", "Liu"), + person("Panuwat", "Trairatphisan"), + person("Enio", "Gjerga"), + person("Athanasios", "Didangelos"), + person("Jonathan", "Barratt"), + person("Julio", "Saez-Rodriguez")), + title = "From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL", + journal = "npj Systems Biology and Applications", + year = 2019, + doi = "10.1038/s41540-019-0118-z", + + textVersion = "Liu A., Trairatphisan P., Gjerga E. et al. 'From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL.' npj Systems Biology and Applications volume 5, Article number: 40 (2019) (equal contributions)." +) diff --git a/man/figures/graphical_abstract.png b/man/figures/graphical_abstract.png new file mode 100644 index 00000000..e8ae41a3 Binary files /dev/null and b/man/figures/graphical_abstract.png differ diff --git a/man/figures/logo.png b/man/figures/logo.png new file mode 100644 index 00000000..7770d72e Binary files /dev/null and b/man/figures/logo.png differ diff --git a/pkgdown/favicon/apple-touch-icon-120x120.png b/pkgdown/favicon/apple-touch-icon-120x120.png new file mode 100644 index 00000000..26c64992 Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon-120x120.png differ diff --git a/pkgdown/favicon/apple-touch-icon-152x152.png b/pkgdown/favicon/apple-touch-icon-152x152.png new file mode 100644 index 00000000..7ef7de06 Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon-152x152.png differ diff --git a/pkgdown/favicon/apple-touch-icon-180x180.png b/pkgdown/favicon/apple-touch-icon-180x180.png new file mode 100644 index 00000000..a3bd320f Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon-180x180.png differ diff --git a/pkgdown/favicon/apple-touch-icon-60x60.png b/pkgdown/favicon/apple-touch-icon-60x60.png new file mode 100644 index 00000000..ad83dddd Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon-60x60.png differ diff --git a/pkgdown/favicon/apple-touch-icon-76x76.png b/pkgdown/favicon/apple-touch-icon-76x76.png new file mode 100644 index 00000000..b89cc808 Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon-76x76.png differ diff --git a/pkgdown/favicon/apple-touch-icon.png b/pkgdown/favicon/apple-touch-icon.png new file mode 100644 index 00000000..1f9d56f7 Binary files /dev/null and b/pkgdown/favicon/apple-touch-icon.png differ diff --git a/pkgdown/favicon/favicon-16x16.png b/pkgdown/favicon/favicon-16x16.png new file mode 100644 index 00000000..9f0d87e1 Binary files /dev/null and b/pkgdown/favicon/favicon-16x16.png differ diff --git a/pkgdown/favicon/favicon-32x32.png b/pkgdown/favicon/favicon-32x32.png new file mode 100644 index 00000000..ccce8949 Binary files /dev/null and b/pkgdown/favicon/favicon-32x32.png differ diff --git a/pkgdown/favicon/favicon.ico b/pkgdown/favicon/favicon.ico new file mode 100644 index 00000000..64980d90 Binary files /dev/null and b/pkgdown/favicon/favicon.ico differ