VISA

VISuAlize single cell data

Load ATAC Data

library(Seurat)
source('https://raw.githubusercontent.com/jumphone/VISA/master/VISA.R')

#load ATAC data from matrix file:
peaks=visa.read10Xatac(PATH='filtered_peak_bc_matrix')

activity.matrix <- CreateGeneActivityMatrix(peak.matrix = peaks, annotation.file = "Mus_musculus.GRCm38.97.chr.gtf",
    seq.levels = c(1:19, "X", "Y"), upstream = 2000, verbose = TRUE)

Visualize Peaks

#setwd('F:/BEER_REVISE/ATAC')

source('https://raw.githubusercontent.com/jumphone/BEER/master/BEER.R')
source('https://raw.githubusercontent.com/jumphone/VISA/master/VISA.R')

library(Seurat)
peaks <- Read10X_h5("../data/atac_v1_pbmc_10k_filtered_peak_bc_matrix.h5")

mybeer = readRDS('mybeer.final.RDS')

pbmc <- mybeer$seurat
PCUSE=mybeer$select
pbmc=BEER.combat(pbmc) 
umap=BEER.bbknn(pbmc, PCUSE, NB=3, NT=10)
pbmc@reductions$umap@cell.embeddings=umap
DimPlot(pbmc, reduction.use='umap', group.by='batch', pt.size=0.1,label=F) 

set.seed(123)
KC=kmeans(pbmc@reductions$umap@cell.embeddings,centers=30)
Idents(pbmc)=as.character(KC$cluster)
DimPlot(pbmc, reduction.use='umap', pt.size=0.1,label=T) 

KCREF=.generate_mean(as.matrix(pbmc@assays$RNA@data), as.character(KC$cluster))  
library('gplots')
C=cor(KCREF,method='spearman')
H=heatmap.2(C,scale=c("none"),dendrogram='both',Colv=TRUE,Rowv=TRUE,trace='none',
    col=colorRampPalette(c('blue','white','red')),margins=c(5,5))
HC=as.hclust(H$colDendrogram)
CLUST=cutree(HC,k=3)

RC=rep('black',ncol(C))
RC[which(CLUST==1)]='red'
RC[which(CLUST==2)]='blue'
RC[which(CLUST==3)]='green'

heatmap.2(C,scale=c("none"),dendrogram='both',Colv=TRUE,Rowv=TRUE,trace='none',
    col=colorRampPalette(c('blue','white','red')),RowSideColors=RC,margins=c(5,5))

CELL.CLUST=rep(NA,ncol(pbmc))
i=1
while(i<=max(CLUST)){
    CELL.CLUST[which(KC$cluster %in% names(CLUST[which(CLUST==i)]))]=i
    i=i+1
    }
    
Idents(pbmc)=as.character(CELL.CLUST)
DimPlot(pbmc, reduction.use='umap', pt.size=0.1,label=T) 

ATAC.C1.CN=colnames(pbmc)[which(pbmc@meta.data$batch=='ATAC' & Idents(pbmc)=='1')]
ATAC.C1.BC=visa.getBarcode(ATAC.C1.CN)    
peaks.BC=visa.getBarcode(colnames(peaks))
peaks.C1=as.matrix(peaks[,which(peaks.BC %in% ATAC.C1.BC)])


peaks.C1.percent=peaks.C1
peaks.C1.percent[which(peaks.C1>0)]=1

peaks.C1.signal=round(apply(peaks.C1,1,mean)*100)
   
BDG=visa.signal2bdg(peaks.C1.signal)



write.table(BDG,file='ATAC.C1.bedgraph',sep='\t',quote=FALSE,col.names=FALSE,row.names=FALSE)

#Then, load data to IGV

IGV

TMP

CHRs=unique(BDG[,1])
i=1
while(i<=length(CHRs)){
    this_chr=CHRs[i]
    this_index=which(BDG[,1] == this_chr)
    this_n=min(1000,length(this_index))
    this_start=as.numeric(BDG[this_index,2])
    this_end=as.numeric(BDG[this_index,3])
    this_signal=as.numeric(BDG[this_index,4])
    this_data=cbind(this_start,this_end,this_signal)
    this_data_scale=apply(this_data,2,scale)
    KM=kmeans(this_data_scale[,c(1:2)],centers = this_n )      
    
    i=i+1
    }