update R packages

DESCRIPTION

@@ -1,35 +1,20 @@
 Package: NetID
 Type: Package
-Title: A Meta-cell Base Method to Infer Fate Specific Gene Regulatory Network from Single Cell Gene Expression Data
+Title: A Meta-cell Base Method to Infer Fate Specific Gene Regulatory
+        Network from Single Cell Gene Expression Data
 Version: 0.1.0
 biocViews:
-Imports:
-    Matrix,
-    lmtest,
-    glmnet,
-    RobustRankAggreg,
-    irlba,
-    rsvd,
-    reticulate,
-    Seurat,
-    Hmisc,
-    mclust,
-    pracma,
-    doParallel,
-    doRNG,
-    RaceID (>= 0.2.9),
-    igraph,
-    rARPACK
-Suggests:
-	GENIE3,
-	SingleCellExperiment,
-	knitr,
-	rmarkdown
+Imports: Matrix, lmtest, glmnet, RobustRankAggreg, irlba, rsvd,
+        reticulate, Seurat, Hmisc, mclust, pracma, doParallel, doRNG,
+        RaceID (>= 0.2.9), igraph, rARPACK
+Suggests: GENIE3, SingleCellExperiment, knitr, rmarkdown
 VignetteBuilder: knitr
 Author: Weixu Wang and Dominic Grün
 Maintainer: Weixu Wang <[email protected]>
 Description: First sample the cells from single cell data, generate aggregate profile on pruned K-Nearest Neighbor graph, lead to a better gene expression profile to quantify gene regulations.
 License: MIT + file LICENSE
 Encoding: UTF-8
-LazyData: true
 RoxygenNote: 7.2.3
+NeedsCompilation: no
+Packaged: 2023-09-06 08:38:49 UTC; wangweixu
+Depends: R (>= 3.5.0)

R/FateCausal.R

@@ -62,7 +62,7 @@
 #'
 #' @export
 #'
-FateCausal <- function(dyn.out,L=30,alpha = 0,lambda=100,cutoff=0.25,weight=0.2,redirected=FALSE,fate_method = "palantir",aggre_method = "RobustRankAggreg",restart=FALSE,n.cores = 8,work_dir = NULL){
+FateCausal <- function(dyn.out,L=30,alpha = 0,lambda=100,cutoff=0.25,weight=0.2,redirected=FALSE,fate_method = "palantir",restart=FALSE,n.cores = 8,work_dir = NULL){
   if(!is.null(work_dir)){setwd(work_dir)}
 
   #velo_m <- velo_m[apply(velo_m,1,function(x){sum(is.nan(x))})==0,]
@@ -195,40 +195,7 @@ FateCausal <- function(dyn.out,L=30,alpha = 0,lambda=100,cutoff=0.25,weight=0.2,
     ### generate the rank score
     granger_net <- m1
 
-    ### aggregate through SUMMA
-    if(aggre_method == "SUMMA"){
-      scaffoldNet <- as(as.matrix(t(dyn.out$skeleton$skeleton)[rownames(grn_list[[1]]),colnames(grn_list[[1]])]), "dgCMatrix")
-      scaffoldNet <- Matrix::summary(scaffoldNet)
-      prediction_m <- cbind(apply(scaffoldNet,MARGIN=1,extract,M=granger_net),apply(scaffoldNet,MARGIN=1,extract,M=GENIE3_base))
-      weight_vec <- summa::summa(prediction_m,"rank")@estimated_performance
-      weight_vec <- weight_vec / sum(weight_vec)
-      rm(prediction_m);gc()
-      grn_list_final[[i]] <- granger_net*weight_vec[1]+GENIE3_base*weight_vec[2]
-    }
-    if(aggre_method == "RobustRankAggreg"){
-      scaffoldNet <- as(as.matrix(t(dyn.out$skeleton$skeleton)[rownames(grn_list[[1]]),colnames(grn_list[[1]])]), "dgCMatrix")
-      scaffoldNet <- Matrix::summary(scaffoldNet)
-      prediction_m <- cbind(apply(scaffoldNet,MARGIN=1,extract,M=granger_net),apply(scaffoldNet,MARGIN=1,extract,M=GENIE3_base))
-      linkList <- list(c(1:nrow(prediction_m))[order(prediction_m[,1],decreasing=TRUE)],
-                       c(1:nrow(prediction_m))[order(prediction_m[,2],decreasing=TRUE)])
-      linkList[[1]] <- as.character(linkList[[1]][which(prediction_m[linkList[[1]],1]!=0)])
-      linkList[[2]] <- as.character(linkList[[2]][which(prediction_m[linkList[[2]],2]!=0)])
-
-      r = RobustRankAggreg::rankMatrix(linkList)
-      score <- RobustRankAggreg::aggregateRanks(rmat = r)
-      scaffoldNet$x[as.integer(score$Name)] <- rankScore(1 / score$Score)
-      aggreGRN <- Matrix::sparseMatrix(
-        i = scaffoldNet$i,
-        j = scaffoldNet$j,
-        x = scaffoldNet$x,dims = dim(GENIE3_base)
-      )
-      rownames(aggreGRN) <- rownames(GENIE3_base)
-      colnames(aggreGRN) <- colnames(GENIE3_base)
-      grn_list_final[[i]] <- aggreGRN
-    }
-    if(aggre_method == "manual"){
-      grn_list_final[[i]] <- granger_net*(1-weight)+GENIE3_base*weight
-    }
+    grn_list_final[[i]] <- granger_net
   }
   names(grn_list_final) <- names(grn_list)
 

README.md

@@ -5,9 +5,7 @@ A scalable method to infer fate specific gene regulatory network from single cel
 
 ## Tutorial
 
-```
-vignette("NetID")
-```
+See Jupyter Notebooks in notebook folder
 
 ## Installation
 ### Basic installation
@@ -23,7 +21,7 @@ pip install geosketch
 ```
 #### install NetID
 ```
-install.packages('NetID_0.1.0.tar.gz', repos=NULL, type='source')
+devtools::install_github("WWXKenmo/NetID_package")
 ```
 ### Advance installation
 #### install cellrank and palantir to realize lineage-specific GRN prediction