jumphone / vector

Unsupervised Inference of Developmental Directions for Single Cells

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

Contact:

fzhang_at_shsmu.edu.cn

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@[email protected]
rownames(VEC) = colnames(pbmc)
PCA = pbmc@[email protected]

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.selectCenter(OUT)

OUT=vector.drawArrow(OUT,P=0.9,SHOW=TRUE, COL=OUT$COL)

Additional function 3: Manually select region of interest:

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')

# or
VEC = cds@int_colData$reducedDims$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@[email protected]

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More tools & studies: https://fzhang.bioinfo-lab.com/

vector's Issues

Manually selected starting points are not well recognized

First of all, thank you very much for creating a script that not only recognizes accurately but also runs very fast!
During using, I found that when manually selecting the start point, the point marked by the mouse does not match the displayed point when running the next step. In fact, the script thinks that the marked point is far to the upper left than I select.

Looking forward to receiving your reply