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* add bioc information * update website * add crane and docs * update crane * update worflow * update crane * update workflows * update workflows * update workfolw * update workfolw * update workfolw * update workfolw * Update main.yml * Update main.yml * Update main.yml * fix lioness py * udpate lioness in R * include calculations python * update panda rm missing * update panda * update lioness joblib * revert commits * ALPACA fix to NAs in community assignement * version bump * revert lioness * update crane with alpaca changes * update lioness * update lioness python * fix test * update workflows * put back github actions * Fixed typo * Added LIONESS-pearson option + unit test * changed param labels in LIONESS example * updated docs for lioness() function * Update main.yml * Update main.yml * Update DESCRIPTION * fix crane issue #240 * expanded PANDA and LIONESS description * update crane fix * added ALPACA, EGRET, and OTTER descriptions * Update README.md * typo in na.rm * update workflow * update workflow2 * fix-actions-3 * add r lib v2 * add r lib v2 * update condor test * update condor test * update condor test * update condor test * show testthat output * add pandoc and use cache * use built in check * use built in check * update actions with built in check * bump version * use built in coverage * use built in coverage * remove sudo * update actions * update actions * removed sudo from actions * ugrade to R42 * remove virtualenv * remove virtualenv * remove virtualenv * modify condor test * update condor test * update condor test * adding retiucualte to covr * adding retiucualte to covr * add panda test to coverignore * update actions * remove lioness tests from coverage * remove lioness tests from coverage * added MONSTER and SAMBAR * remove lionessR test * Added CONDOR and YARN * Update README.md * put back otter in rbuild * remove condor test * remove condor test * remove lioness docs * add lioness to buildingore * donttest lionesss * remove coverage from ubuntu test * remove rbuilignore * update rbuildignore * update rbuildignore * update rbuildignore * update rbuildignore * update rbuildignore * add back buildignore * add back buildignore * add back buildignore * add back buildignore * add back buildignore * add back buildignore * add back buildignore * add back buildignore * add back buildignore * add back buildignore * put back tests * add padanedge diff test * add back egeret test * add monster test * fix monster test * tolerance in expect equal * tolerance in expect equal * tolerance in expect equal * tolerance in expect equal * add sourceppi * fixed PANDA tests * modify panda test * update lioness test * fix expect message * update tests * update tests * update tests * update tests * update tests * update tests * reduced lioness test data * reduced lioness test data * reduced lioness test data * reduced lioness test data * reduce test size * updating bioc (#250) * updating bioc * remove empty lines * remove empty lines * update citation * update citation * Update README.md * Update README.md * Update main.yml * Update main.yml * Update .Rbuildignore * Update .Rbuildignore * update lioness test * removed two last lioness tests * removed two last lioness tests * removed two last lioness tests * no saving of lioness networks * no saving of lioness networks * no saving of lioness networks * no saving of lioness networks * update lioness test * lower sizeof test * update test data * update test data * update test data * reduce panda tests * remove old test data * remove old test data * Update main.yml * 1.1.16 update pandapy with header argument (#260) * trying collapse readme (#261) * trying collapse readme * collapse animal description * Update README.md * update biconda (#265) * add spider (#269) * add spider * udpate spider * udpate spider * update spuder with degreeadjust * fix spider bug Co-authored-by: Kate Hoff Shutta <kshutta@umass.edu> Co-authored-by: katehoffshutta <43797774+katehoffshutta@users.noreply.github.com>
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| Original file line number | Diff line number | Diff line change |
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| #' Seeding PANDA Interactions to Derive Epigenetic Regulation | ||
| #' | ||
| #' This function runs the SPIDER algorithm | ||
| #' | ||
| #' @param motif A motif dataset, a data.frame, matrix or exprSet containing 3 columns. | ||
| #' Each row describes an motif associated with a transcription factor (column 1) a | ||
| #' gene (column 2) and a score (column 3) for the motif. | ||
| #' @param epifilter A binary matrix that is of the same size as motif that will be used as a mask to filter motif | ||
| #' for open chromatin region. Motif interactions that fall in open chromatin region will be kept and the others are removed. | ||
| #' @param expr An expression dataset, as a genes (rows) by samples (columns) data.frame | ||
| #' @param ppi A Protein-Protein interaction dataset, a data.frame containing 3 columns. | ||
| #' Each row describes a protein-protein interaction between transcription factor 1(column 1), | ||
| #' transcription factor 2 (column 2) and a score (column 3) for the interaction. | ||
| #' @param alpha value to be used for update variable, alpha (default=0.1) | ||
| #' @param hamming value at which to terminate the process based on hamming distance (default 10^-3) | ||
| #' @param iter sets the maximum number of iterations SPIDER can run before exiting. | ||
| #' @param progress Boolean to indicate printing of output for algorithm progress. | ||
| #' @param output a vector containing which networks to return. Options include "regulatory", | ||
| #' "coregulatory", "cooperative". | ||
| #' @param zScale Boolean to indicate use of z-scores in output. False will use [0,1] scale. | ||
| #' @param randomize method by which to randomize gene expression matrix. Default "None". Must | ||
| #' be one of "None", "within.gene", "by.genes". "within.gene" randomization scrambles each row | ||
| #' of the gene expression matrix, "by.gene" scrambles gene labels. | ||
| #' @param cor.method Correlation method, default is "pearson". | ||
| #' @param scale.by.present Boolean to indicate scaling of correlations by percentage of positive samples. | ||
| #' @param remove.missing.ppi Boolean to indicate whether TFs in the PPI but not in the motif data should be | ||
| #' removed. Only when mode=='legacy'. | ||
| #' @param remove.missing.motif Boolean to indicate whether genes targeted in the motif data but not the | ||
| #' expression data should be removed. Only when mode=='legacy'. | ||
| #' @param remove.missing.genes Boolean to indicate whether genes in the expression data but lacking | ||
| #' information from the motif prior should be removed. Only when mode=='legacy'. | ||
| #' @param edgelist Boolean to indicate if edge lists instead of matrices should be returned. | ||
| #' @param mode The data alignment mode. The mode 'union' takes the union of the genes in the expression matrix and the motif | ||
| #' and the union of TFs in the ppi and motif and fills the matrics with zeros for nonintersecting TFs and gens, 'intersection' | ||
| #' takes the intersection of genes and TFs and removes nonintersecting sets, 'legacy' is the old behavior with version 1.19.3. | ||
| #' #' Parameters remove.missing.ppi, remove.missingmotif, remove.missing.genes work only with mode=='legacy'. | ||
| #' @keywords keywords | ||
| #' @importFrom matrixStats rowSds | ||
| #' @importFrom matrixStats colSds | ||
| #' @importFrom Biobase assayData | ||
| #' @importFrom reshape melt.array | ||
| #' @export | ||
| #' @return An object of class "panda" containing matrices describing networks achieved by convergence | ||
| #' with SPIDER algorithm.\cr | ||
| #' "regNet" is the regulatory network\cr | ||
| #' "coregNet" is the coregulatory network\cr | ||
| #' "coopNet" is the cooperative network | ||
| #' @examples | ||
| #' data(pandaToyData) | ||
| #' spiderRes <- spider(pandaToyData$motif, pandaToyData$epifilter | ||
| #' pandaToyData$expression,pandaToyData$ppi,hamming=.1,progress=TRUE) | ||
| #' @references | ||
| #' Sonawane, Abhijeet Rajendra, et al. "Constructing gene regulatory networks using epigenetic data." npj Systems Biology and Applications 7.1 (2021): 1-13. | ||
| spider <- function(motif,expr=NULL,epifilter=NULL,ppi=NULL,alpha=0.1,hamming=0.001, | ||
| iter=NA,output=c('regulatory','coexpression','cooperative'), | ||
| zScale=TRUE,progress=FALSE,randomize=c("None", "within.gene", "by.gene"),cor.method="pearson", | ||
| scale.by.present=FALSE,edgelist=FALSE,remove.missing.ppi=FALSE, | ||
| remove.missing.motif=FALSE,remove.missing.genes=FALSE,mode="union"){ | ||
|
|
||
| randomize <- match.arg(randomize) | ||
| if(progress) | ||
| print('Initializing and validating') | ||
|
|
||
| if(epifilter[c(1,2),] != motif[c(1,2),]){ | ||
| stop('Chromatin accessibility data does not match motif data size and order.') | ||
| } | ||
|
|
||
| if(class(expr)=="ExpressionSet") | ||
| expr <- assayData(expr)[["exprs"]] | ||
|
|
||
| if (is.null(expr)){ | ||
| # Use only the motif data here for the gene list | ||
| num.conditions <- 0 | ||
| if (randomize!="None"){ | ||
| warning("Randomization ignored because gene expression is not used.") | ||
| randomize <- "None" | ||
| } | ||
| } else { | ||
| if(mode=='legacy'){ | ||
| if(remove.missing.genes){ | ||
| # remove genes from expression data that are not in the motif data | ||
| n <- nrow(expr) | ||
| expr <- expr[which(rownames(expr)%in%motif[,2]),] | ||
| message(sprintf("%s genes removed that were not present in motif", n-nrow(expr))) | ||
| } | ||
| if(remove.missing.motif){ | ||
| # remove genes from motif data that are not in the expression data | ||
| n <- nrow(motif) | ||
| motif <- motif[which(motif[,2]%in%rownames(expr)),] | ||
| epifilter <- epifilter[which(motif[,2]%in%rownames(expr)),] | ||
| message(sprintf("%s motif edges removed that targeted genes missing in expression data", n-nrow(motif))) | ||
| } | ||
| # Use the motif data AND the expr data (if provided) for the gene list | ||
| # Keep everything sorted alphabetically | ||
| expr <- expr[order(rownames(expr)),] | ||
| }else if(mode=='union'){ | ||
| gene.names=unique(union(rownames(expr),unique(motif[,2]))) | ||
| tf.names =unique(union(unique(ppi[,1]),unique(motif[,1]))) | ||
| num.TFs <- length(tf.names) | ||
| num.genes <- length(gene.names) | ||
| # gene expression matrix | ||
| expr1=as.data.frame(matrix(0,num.genes,ncol(expr))) | ||
| rownames(expr1)=gene.names | ||
| expr1[which(gene.names%in%rownames(expr)),]=expr[] | ||
| expr=expr1 | ||
| #PPI matrix | ||
| tfCoopNetwork <- matrix(0,num.TFs,num.TFs) | ||
| colnames(tfCoopNetwork)=tf.names | ||
| rownames(tfCoopNetwork)=tf.names | ||
| Idx1 <- match(ppi[,1], tf.names); | ||
| Idx2 <- match(ppi[,2], tf.names); | ||
| Idx <- (Idx2-1)*num.TFs+Idx1; | ||
| tfCoopNetwork[Idx] <- ppi[,3]; | ||
| Idx <- (Idx1-1)*num.TFs+Idx2; | ||
| tfCoopNetwork[Idx] <- ppi[,3]; | ||
| #Motif matrix | ||
| regulatoryNetwork=matrix(0,num.TFs,num.genes) | ||
| colnames(regulatoryNetwork)=gene.names | ||
| rownames(regulatoryNetwork)=tf.names | ||
| Idx1=match(motif[,1], tf.names); | ||
| Idx2=match(motif[,2], gene.names); | ||
| Idx=(Idx2-1)*num.TFs+Idx1; | ||
| regulatoryNetwork[Idx]=motif[,3]*epifilter[,3] | ||
| }else if(mode=='intersection'){ | ||
| gene.names=unique(intersect(rownames(expr),unique(motif[,2]))) | ||
| tf.names =unique(intersect(unique(ppi[,1]),unique(motif[,1]))) | ||
| num.TFs <- length(tf.names) | ||
| num.genes <- length(gene.names) | ||
| # gene expression matrix | ||
| expr1=as.data.frame(matrix(0,num.genes,ncol(expr))) | ||
| rownames(expr1)=gene.names | ||
| interGeneNames=gene.names[which(gene.names%in%rownames(expr))] | ||
| expr1[interGeneNames,]=expr[interGeneNames,] | ||
| expr=expr1 | ||
| #PPI matrix | ||
| tfCoopNetwork <- matrix(0,num.TFs,num.TFs) | ||
| colnames(tfCoopNetwork)=tf.names | ||
| rownames(tfCoopNetwork)=tf.names | ||
| Idx1 <- match(ppi[,1], tf.names); | ||
| Idx2 <- match(ppi[,2], tf.names); | ||
| Idx <- (Idx2-1)*num.TFs+Idx1; | ||
| indIdx=!is.na(Idx) | ||
| Idx=Idx[indIdx] #remove missing TFs | ||
| tfCoopNetwork[Idx] <- ppi[indIdx,3]; | ||
| Idx <- (Idx1-1)*num.TFs+Idx2; | ||
| indIdx=!is.na(Idx) | ||
| Idx=Idx[indIdx] #remove missing TFs | ||
| tfCoopNetwork[Idx] <- ppi[indIdx,3]; | ||
| #Motif matrix | ||
| regulatoryNetwork=matrix(0,num.TFs,num.genes) | ||
| colnames(regulatoryNetwork)=gene.names | ||
| rownames(regulatoryNetwork)=tf.names | ||
| Idx1=match(motif[,1], tf.names); | ||
| Idx2=match(motif[,2], gene.names); | ||
| Idx=(Idx2-1)*num.TFs+Idx1; | ||
| indIdx=!is.na(Idx) | ||
| Idx=Idx[indIdx] #remove missing genes | ||
| regulatoryNetwork[Idx]=motif[indIdx,3]*epifilter[indIdx,3]; | ||
| } | ||
| num.conditions <- ncol(expr) | ||
| if (randomize=='within.gene'){ | ||
| expr <- t(apply(expr, 1, sample)) | ||
| if(progress) | ||
| print("Randomizing by reordering each gene's expression") | ||
| } else if (randomize=='by.gene'){ | ||
| rownames(expr) <- sample(rownames(expr)) | ||
| expr <- expr[order(rownames(expr)),] | ||
| if(progress) | ||
| print("Randomizing by reordering each gene labels") | ||
| } | ||
| } | ||
|
|
||
| if (mode=='legacy'){ | ||
| # Create vectors for TF names and Gene names from motif dataset | ||
| tf.names <- sort(unique(motif[,1])) | ||
| gene.names <- sort(unique(rownames(expr))) | ||
| num.TFs <- length(tf.names) | ||
| num.genes <- length(gene.names) | ||
| } | ||
|
|
||
| # Bad data checking | ||
| if (num.genes==0){ | ||
| stop("Error validating data. No matched genes.\n Please ensure that gene names in expression data match gene names in motif data") | ||
| } | ||
|
|
||
| if(num.conditions==0) { | ||
| warning('No expression data given. SPIDER will run based on an identity co-regulation matrix') | ||
| geneCoreg <- diag(num.genes) | ||
| } else if(num.conditions<3) { | ||
| warning('Not enough expression conditions detected to calculate correlation. Co-regulation network will be initialized to an identity matrix.') | ||
| geneCoreg <- diag(num.genes) | ||
| } else { | ||
|
|
||
| if(scale.by.present){ | ||
| num.positive=(expr>0)%*%t((expr>0)) | ||
| geneCoreg <- cor(t(expr), method=cor.method, use="pairwise.complete.obs")*(num.positive/num.conditions) | ||
| } else { | ||
| geneCoreg <- cor(t(expr), method=cor.method, use="pairwise.complete.obs") | ||
| } | ||
| if(progress) | ||
| print('Verified sufficient samples') | ||
| } | ||
| if (any(is.na(geneCoreg))){ #check for NA and replace them by zero | ||
| diag(geneCoreg)=1 | ||
| geneCoreg[is.na(geneCoreg)]=0 | ||
| } | ||
|
|
||
| if (any(duplicated(motif))) { | ||
| warning("Duplicate edges have been found in the motif data. Weights will be summed.") | ||
| motif <- aggregate(motif[,3], by=list(motif[,1], motif[,2]), FUN=sum) | ||
| } | ||
|
|
||
| # Prior Regulatory Network | ||
| if(mode=='legacy'){ | ||
| Idx1=match(motif[,1], tf.names); | ||
| Idx2=match(motif[,2], gene.names); | ||
| Idx=(Idx2-1)*num.TFs+Idx1; | ||
| regulatoryNetwork=matrix(data=0, num.TFs, num.genes); | ||
| regulatoryNetwork[Idx]=motif[,3] | ||
| colnames(regulatoryNetwork) <- gene.names | ||
| rownames(regulatoryNetwork) <- tf.names | ||
| # PPI data | ||
| # If no ppi data is given, we use the identity matrix | ||
| tfCoopNetwork <- diag(num.TFs) | ||
| # Else we convert our two-column data.frame to a matrix | ||
| if (!is.null(ppi)){ | ||
| if(any(duplicated(ppi))){ | ||
| warning("Duplicate edges have been found in the PPI data. Weights will be summed.") | ||
| ppi <- aggregate(ppi[,3], by=list(ppi[,1], ppi[,2]), FUN=sum) | ||
| } | ||
| if(remove.missing.ppi){ | ||
| # remove edges in the PPI data that target TFs not in the motif | ||
| n <- nrow(ppi) | ||
| ppi <- ppi[which(ppi[,1]%in%tf.names & ppi[,2]%in%tf.names),] | ||
| message(sprintf("%s PPI edges removed that were not present in motif", n-nrow(ppi))) | ||
| } | ||
| Idx1 <- match(ppi[,1], tf.names); | ||
| Idx2 <- match(ppi[,2], tf.names); | ||
| Idx <- (Idx2-1)*num.TFs+Idx1; | ||
| tfCoopNetwork[Idx] <- ppi[,3]; | ||
| Idx <- (Idx1-1)*num.TFs+Idx2; | ||
| tfCoopNetwork[Idx] <- ppi[,3]; | ||
| } | ||
| colnames(tfCoopNetwork) <- tf.names | ||
| rownames(tfCoopNetwork) <- tf.names | ||
| } | ||
|
|
||
| ## Run SPIDER ## | ||
| tic=proc.time()[3] | ||
|
|
||
| # adjusting degree distribution | ||
| regulatoryNetwork = degreeAdjust(regulatoryNetwork) | ||
|
|
||
| if(progress) | ||
| print('Normalizing networks...') | ||
| regulatoryNetwork = normalizeNetwork(regulatoryNetwork) | ||
| tfCoopNetwork = normalizeNetwork(tfCoopNetwork) | ||
| geneCoreg = normalizeNetwork(geneCoreg) | ||
|
|
||
| if(progress) | ||
| print('Learning Network...') | ||
|
|
||
| minusAlpha = 1-alpha | ||
| step=0 | ||
| hamming_cur=1 | ||
| if(progress) | ||
| print("Using tanimoto similarity") | ||
| while(hamming_cur>hamming){ | ||
| if ((!is.na(iter))&&step>=iter){ | ||
| print(paste("Reached maximum iterations, iter =",iter),sep="") | ||
| break | ||
| } | ||
| Responsibility=tanimoto(tfCoopNetwork, regulatoryNetwork) | ||
| Availability=tanimoto(regulatoryNetwork, geneCoreg) | ||
| RA = 0.5*(Responsibility+Availability) | ||
|
|
||
| hamming_cur=sum(abs(regulatoryNetwork-RA))/(num.TFs*num.genes) | ||
| regulatoryNetwork=minusAlpha*regulatoryNetwork + alpha*RA | ||
|
|
||
| ppi=tanimoto(regulatoryNetwork, t(regulatoryNetwork)) | ||
| ppi=update.diagonal(ppi, num.TFs, alpha, step) | ||
| tfCoopNetwork=minusAlpha*tfCoopNetwork + alpha*ppi | ||
|
|
||
| CoReg2=tanimoto(t(regulatoryNetwork), regulatoryNetwork) | ||
| CoReg2=update.diagonal(CoReg2, num.genes, alpha, step) | ||
| geneCoreg=minusAlpha*geneCoreg + alpha*CoReg2 | ||
|
|
||
| if(progress) | ||
| message("Iteration", step,": hamming distance =", round(hamming_cur,5)) | ||
| step=step+1 | ||
| } | ||
|
|
||
| toc=proc.time()[3] - tic | ||
| if(progress) | ||
| message("Successfully ran SPIDER on ", num.genes, " Genes and ", num.TFs, " TFs.\nTime elapsed:", round(toc,2), "seconds.") | ||
| prepResult(zScale, output, regulatoryNetwork, geneCoreg, tfCoopNetwork, edgelist, motif) | ||
| } | ||
|
|
||
| #' Function to adjust the degree so that the hub nodes are not penalized in z-score transformation | ||
| #' | ||
| #' @param A Input adjacency matrix | ||
| degreeAdjust <- function(A){ | ||
| k1 <- colSums(A)/dim(A,1) | ||
| k2 <- rowSums(A)/dim(A,2) | ||
| B <- (matrix(replicate(dim(A,2),k1),nrow=dim(A,1)))^2 | ||
| B <- B + (matrix(t(replicate(dim(A,2),k2)),nrow=dim(A,1)))^2 | ||
| A <- A * sqrt(B); | ||
| } |
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