v1.5.0

.github/workflows/main.yml

@@ -34,7 +34,7 @@ jobs:
 
     steps:
 
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
         with:
           token: ${{ secrets.GITHUB_TOKEN }}
 
@@ -48,7 +48,7 @@ jobs:
       - name: install dependencies
         uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          cache-version: 1
+          cache-version: 2
           extra-packages: |
              any::reticulate
              any::rcmdcheck
@@ -63,6 +63,14 @@ jobs:
             extSoftVersion()["BLAS"]
         shell: Rscript {0}
 
+      - if: matrix.os == 'ubuntu-latest'
+        run: |
+          sudo apt-get install python3-venv
+          pip install --upgrade pip
+          python3 -m venv env
+          source env/bin/activate
+          echo "VIRTUAL ENV:" $VIRTUAL_ENV
+
       - name: Install reticulate
         run: |
             library("reticulate")

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: netZooR
 Type: Package
 Title: Unified methods for the inference and analysis of gene regulatory networks
-Version: 1.4.0
-Date: 2023-06-03
+Version: 1.5.4
+Date: 2023-09-25
 Authors@R: c(person("Marouen", "Ben Guebila",
 			email = "benguebila@hsph.harvard.edu", role = c("aut","cre"), comment = c(ORCID = "0000-0001-5934-966X")),
 	     person("Tian", "Wang", 
@@ -24,11 +24,13 @@ Description: netZooR unifies the implementations of several Network Zoo methods
 Depends: R (>= 4.2.0),
    	 igraph,
 	 reticulate,
-         pandaR,
          yarn,
+	 pandaR,
          matrixcalc
 Remotes: 
-	 stan-dev/cmdstanr 
+	 stan-dev/cmdstanr,
+	 jnpaulson/pandaR, 
+	 cytoscape/RCy3
 biocViews:
 	 NetworkInference,
 	 Network,
@@ -37,8 +39,8 @@ biocViews:
          Transcription, 
          Microarray,   
          GraphAndNetwork
-Imports: 
-    RCy3,
+Imports:
+    RCy3, 
     viridisLite,
     STRINGdb,
     Biobase,
@@ -69,9 +71,11 @@ Imports:
     methods,
     dplyr,
     graphics,
-	cmdstanr,
+    cmdstanr,
     GeneNet,
-    loo
+    loo,
+    rARPACK,
+    corpcor
 License: GPL-3
 Encoding: UTF-8
 LazyData: false

NAMESPACE

@@ -1,11 +1,11 @@
 # Generated by roxygen2: do not edit by hand
 
-export(TIGER)
 export(adj2el)
 export(adj2regulon)
 export(alpaca)
 export(alpacaCrane)
 export(alpacaExtractTopGenes)
+export(cobra)
 export(condorCluster)
 export(condorCoreEnrich)
 export(condorMatrixModularity)
@@ -41,12 +41,13 @@ export(pandaDiffEdges)
 export(pandaPy)
 export(pandaToAlpaca)
 export(pandaToCondorObject)
-export(prior.pp)
+export(priorPp)
 export(puma)
 export(runEgret)
 export(sambar)
 export(sourcePPI)
 export(spider)
+export(tiger)
 export(visPandaInCytoscape)
 import(AnnotationDbi, except= select)
 

R/COBRA.R

@@ -0,0 +1,82 @@
+#' Run COBRA in R
+#' 
+#' Description:
+#'               COBRA decomposes a (partial) gene co-expression matrix as a 
+#'               linear combination of covariate-specific components. 
+#'               It can be applied for batch correction, differential co-expression 
+#'               analysis controlling for variables, and to understand the impact of
+#'               variables of interest to the observed co-expression. 
+#'
+#' Inputs:
+#' @param X               : design matrix of size (n, q), n = number of samples, q = number of covariates
+#' @param expressionData  : gene expression as a matrix of size (g, n), g = number of genes
+#' @param method     : if pearson, the decomposition of the co-expression matrix is compouted. If pcorsh, COBRA decomposes the regularized partial co-expression
+#'
+#' Outputs:
+#' @return psi : impact of each covariate on the eigenvalues as a matrix of size (q, n)
+#' @return Q   : eigenvectors corresponding to non-zero eigenvalues as a matrix of size (g, n)
+#' @return D   : non-zero eigenvalues as a list of length n
+#' @return G   : (standardized) gene expression as a matrix of size (g, n)
+#'
+#' @examples
+#'
+#' g <- 100 # number of genes
+#' n <- 10 # number of samples
+#' q <- 2 # number of covariates
+#' X <- X <- cbind(rep(1, n), rbinom(n, 1, 0.5))
+#' expressionData=matrix(rnorm(g*n, 1, 1), ncol = n, nrow = g)
+#'
+#' # Run COBRA algorithm
+#' cobra_output <- cobra(X, expressionData)
+#'
+#' @export  
+
+cobra <- function(X, expressionData, method = "pearson"){
+
+  if(!(method %in% c("pearson", "pcorsh", "dragon"))){
+    stop("Only Pearson and pcor methods are supported. Please make sure to provide a valid method argument.")
+  }
+
+  numSamples <- ncol(expressionData)
+  if(method != "dragon"){
+    N <- min(ncol(expressionData),nrow(expressionData)) 
+  }
+  C <- 0
+  if(method == "pearson"){
+    G_star <- expressionData-rowMeans(expressionData)
+    expressionData <- (G_star/sqrt(rowSums(G_star^2)))
+    expressionData <- as.matrix(expressionData)
+    C <- tcrossprod(expressionData)
+  }
+  if(method == "dragon"){
+    if(length(expressionData) != 2){
+      stop("Dragon needs two layers, please provide a list of two matrices in expressionData or consider using the pcorsh argument")
+    }
+    if(dim(expressionData[[1]])[2] != dim(expressionData[[2]])[2]){
+      stop("Dragon layers in expressionData list must have the same number of samples")
+    }
+    C <- dragon(t(expressionData[[1]]), layer2 = t(expressionData[[2]]), pval=FALSE)$cov
+    N <- dim(expressionData[[1]])[2]
+    numSamples <- N
+    expressionData <- rbind(expressionData[[1]], expressionData[[2]])
+    print(dim(expressionData))
+  }
+  if(method == "pcorsh"){
+   C <- matrix(as.numeric(pcor.shrink(t(expressionData))), dim(expressionData)[1], dim(expressionData)[1])
+  }
+
+  eigenG <- rARPACK::eigs_sym(C,N)
+
+  Q <- eigenG$vectors
+  D <- diag(eigenG$values)
+
+  hatmat <- ginv(crossprod(X))%*%t(X)
+  Qinv <- ginv(Q) 
+  QinvG <- Qinv%*%(expressionData)
+
+  est <- t(sapply(seq_len(nrow(hatmat)), function(hatmatRow){
+    diag(QinvG%*%(numSamples*diag(hatmat[hatmatRow,]))%*%t(QinvG))
+  }))
+
+  list(psi=est, Q=Q, D=eigenG$values, G=expressionData)
+}

R/PANDA.R

@@ -201,4 +201,3 @@ pandaPy <- function(expr_file, motif_file=NULL, ppi_file=NULL, computing="cpu",
   return(output)
 }  
 
-

R/PUMA.R

@@ -422,5 +422,3 @@ prepResult <- function(zScale, output, regulatoryNetwork, geneCoreg, tfCoopNetwo
   pandaObj(regNet=regulatoryNetwork, coregNet=geneCoreg, coopNet=tfCoopNetwork, numGenes=numGenes, numTFs=numTFs, numEdges=numEdges)
 }
 
-pandaObj <- setClass("panda", slots=c("regNet","coregNet","coopNet","numGenes","numTFs","numEdges"))
-setMethod("show","panda",function(object){print.panda(object)})

R/SPIDER.R

@@ -423,7 +423,4 @@ prepResult <- function(zScale, output, regulatoryNetwork, geneCoreg, tfCoopNetwo
     resList$coopNet <- tfCoopNetwork
   }
   pandaObj(regNet=regulatoryNetwork, coregNet=geneCoreg, coopNet=tfCoopNetwork, numGenes=numGenes, numTFs=numTFs, numEdges=numEdges)
-}
-
-pandaObj <- setClass("panda", slots=c("regNet","coregNet","coopNet","numGenes","numTFs","numEdges"))
-setMethod("show","panda",function(object){print.panda(object)})
+}