# Vector **Repository Path**: jumphone/Vector ## Basic Information - **Project Name**: Vector - **Description**: Unsupervised Inference of Developmental Directions for Single Cells Using VECTOR, Cell Reports, 2020 - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-05-06 - **Last Updated**: 2022-06-01 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

# Unsupervised Inference of Developmental Directions for Single Cells ## Citation: Unsupervised Inference of Developmental Directions for Single Cells Using VECTOR, [https://doi.org/10.1016/j.celrep.2020.108069](https://doi.org/10.1016/j.celrep.2020.108069) ## Contact: 15110700005_at_fudan.edu.cn fzhang15_at_fudan.edu.cn ## Environment: R (3.6.1) #### Please install following R packages before using VECTOR: install.packages('circlize') # 0.4.11 install.packages('gatepoints') # 0.1.3 install.packages('stringr') # 1.4.0 install.packages('igraph') # 1.2.6 install.packages('gmodels') # 2.18.1 ## Usage: ### Step 1. Please prepare a Seurat object with UMAP and 150 PCs. Users can follow https://satijalab.org/seurat/ to generate Seurat object (V3.0.0). library(Seurat) # DATA: Expression matrix. Rownames are gene names. Colnames are cell names. pbmc <- CreateSeuratObject(counts = DATA, project = "pbmc3k", min.cells = 0, min.features = 0) pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000) pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 5000) all.genes <- rownames(pbmc) pbmc <- ScaleData(pbmc, features = all.genes) pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc),npcs = 150) pbmc <- RunUMAP(pbmc, dims = 1:50) DimPlot(pbmc, reduction = "umap") saveRDS(pbmc,file='pbmc.RDS') ### Step 2. Get UMAP and PCs from Seurat3 object. (pbmc: a Seurat object): VEC = pbmc@reductions$umap@cell.embeddings rownames(VEC) = colnames(pbmc) PCA = pbmc@reductions$pca@cell.embeddings source('https://raw.githubusercontent.com/jumphone/Vector/master/Vector.R') # Remove quantile-based colinearity among PCs (new feature in VECTOR 0.0.3): PCA=vector.rankPCA(PCA) ### Step 3. Use VECTOR:

source('https://raw.githubusercontent.com/jumphone/Vector/master/Vector.R') # Define pixel OUT=vector.buildGrid(VEC, N=30,SHOW=TRUE) # Build network OUT=vector.buildNet(OUT, CUT=1, SHOW=TRUE) # Calculate Quantile Polarization (QP) score OUT=vector.getValue(OUT, PCA, SHOW=TRUE) # Get pixel's QP score OUT=vector.gridValue(OUT,SHOW=TRUE) # Find starting point OUT=vector.autoCenter(OUT,UP=0.9,SHOW=TRUE) # Infer vector OUT=vector.drawArrow(OUT,P=0.9,SHOW=TRUE, COL=OUT$COL, SHOW.SUMMIT=TRUE) # OUT$P.PS : Peseudotime Score (PS) of each cell ## Additional function 1: Change QP score to a given gene's expression value (e.g. Nes):

NES.EXP = pbmc@assays$RNA@data[which(rownames(pbmc) =='Nes'),] OUT=vector.buildGrid(VEC, N=30,SHOW=TRUE) OUT=vector.buildNet(OUT, CUT=1, SHOW=TRUE) OUT=vector.getValue(OUT, PCA, SHOW=TRUE) OUT$VALUE=NES.EXP OUT=vector.showValue(OUT) OUT=vector.gridValue(OUT, SHOW=TRUE) OUT=vector.autoCenter(OUT,UP=0.9,SHOW=TRUE) OUT=vector.drawArrow(OUT,P=0.9,SHOW=TRUE, COL=OUT$COL) ## Additional function 2: Manually select starting point:

OUT=vector.buildGrid(VEC, N=30,SHOW=TRUE) OUT=vector.buildNet(OUT, CUT=1, SHOW=TRUE) OUT=vector.getValue(OUT, PCA, SHOW=TRUE) OUT=vector.gridValue(OUT,SHOW=TRUE) OUT=vector.autoCenter(OUT,UP=0.9,SHOW=TRUE) OUT=vector.drawArrow(OUT,P=0.9,SHOW=TRUE, COL=OUT$COL) ####################### OUT=vector.reDrawArrow(OUT, COL=OUT$COL) OUT=vector.selectRegion(OUT) ####################### SELECT_PS=OUT$SELECT_PS #Peseudotime Score (PS) of selected cells SELECT_INDEX=OUT$SELECT_INDEX #Index of selected cells in the expression matrix SELECT_COL=OUT$COL[OUT$SELECT_INDEX] #Colors ####################### # Identify development related genes EXP=as.matrix(pbmc@assays$RNA@data)[which(rownames(pbmc) %in% VariableFeatures(pbmc)),SELECT_INDEX] COR=c() i=1 while(i<=nrow(EXP)){ this_cor=cor(SELECT_PS, EXP[i,],method='spearman') COR=c(COR,this_cor) if(i %%100==1){print(i)} i=i+1} names(COR)=rownames(EXP) head(sort(COR),n=10) #Decreasing (top 10) tail(sort(COR),n=10) #Increasing (top 10) # Select one gene to draw figure show_gene=names(head(sort(COR),n=10))[1] show_gene.exp=EXP[which(rownames(EXP)==show_gene),] # Smooth expression value along pesudotime order (optional) show_gene.exp[order(SELECT_PS)]=smooth.spline(show_gene.exp[order(SELECT_PS)], df=5)$y # Draw figure plot(jitter(SELECT_PS), show_gene.exp, pch=16,col=SELECT_COL, ylab=show_gene,xlab='PS') show_gene.fit=lm(show_gene.exp~SELECT_PS) abline(show_gene.fit,col='black',lwd=1) ## Other: Get UMAP and PCs from Monocle3. (cds: a Monocle object): # Get UMAP: VEC = cds@reducedDims$UMAP colnames(VEC) = c('UMAP_1','UMAP_2') # Get 150 PCs library(Seurat) DATA=as.matrix(cds@assays$data[[1]]) pbmc <- CreateSeuratObject(counts = DATA, project = "pbmc3k", min.cells = 0, min.features = 0) pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000) pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 5000) all.genes <- rownames(pbmc) pbmc <- ScaleData(pbmc, features = all.genes) pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc),npcs = 150) PCA = pbmc@reductions$pca@cell.embeddings