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Vector.R
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###################
# Author: Feng Zhang
# Date: 11/25, 2019
##################
library('circlize')
library('gatepoints')
library('stringr')
library('igraph')
library('gmodels')
##################
vector.SeuratSelect <- function(pbmc){
pbmc=pbmc
##################
ppp=DimPlot(pbmc, reduction.use='umap', pt.size=0.5)
used.cells <- CellSelector(plot = ppp)
#################
return(used.cells)
}
vector.SeuratAddMetaByCell <- function(pbmc, used.cells){
pbmc=pbmc
used.cells=used.cells
SELECT=rep('NO',ncol(pbmc))
SELECT[which(colnames(pbmc) %in% used.cells)]='YES'
pbmc@meta.data$select=SELECT
return(pbmc)
}
.normX <- function(x){
y=(x-min(x))/(max(x)-min(x))
return(y)
}
vector.lcol <- function(TAG){
TAG=as.factor(TAG)
require(scales)
my_color_palette <- hue_pal()(length(unique(TAG)))
COL=my_color_palette[TAG]
return(COL)
}
vector.vcol<-function(VALUE, CV, CN){
VALUE=VALUE
library('circlize')
CRF=colorRamp2(CV, CN)
COL=CRF(VALUE)
return(COL)
}
vector.showValue<-function(OUT){
VEC=OUT$VEC
VALUE=OUT$VALUE
#################################
N.VALUE=.normX(VALUE)
COL=vector.vcol(N.VALUE, c(0,0.5,1),c('#009FFF','#FFF200','#ec2F4B'))
plot(VEC,col=COL,pch=16,cex=0.5)
return(OUT)
}
vector.calValue <- function(PCA){
OUT=list()
PCA=PCA
PCA.RC=apply(apply(PCA,2,rank), 2, .normX)
PCA.RC=abs(PCA.RC-0.5)
VALUE=apply(PCA.RC,1,mean)
#VALUE=rank(VALUE)
###########################
OUT$VALUE=VALUE
OUT$PCA.RC=PCA.RC
return(OUT)
}
vector.getValue <-function(OUT, PCA, SHOW=TRUE){
OUT=OUT
PCA=PCA
SHOW=SHOW
############
VALUE.OUT=vector.calValue(PCA)
###############
OUT$VALUE=VALUE.OUT$VALUE
OUT$PCA=PCA
OUT$PCA.RC=VALUE.OUT$PCA.RC
###############
if(SHOW==TRUE){
vector.showValue(OUT)
}
#####
return(OUT)
}
vector.buildGrid <- function(VEC, N=30, SHOW=TRUE, COL='grey70'){
#############
VEC.E=VEC
COL=COL
SHOW=SHOW
delta=0.000001
N=N
##############
if(SHOW==TRUE){
plot(VEC.E,col=COL,pch=16,cex=0.2)
}
###############
X=VEC[,1]
Y=VEC[,2]
this_step_x=( max(X) - min(X) - delta)/N
this_step_y=( max(Y) - min(Y) - delta)/N
NUM_CUT=1
############
INDEX_LIST=list()
CENTER_LIST=list()
this_x=min(X)
while(this_x<max(X)){
this_y=min(Y)
while(this_y<max(Y)){
################
this_in_index = which(VEC.E[,1]>=this_x & VEC.E[,1] <this_x+this_step_x &
VEC.E[,2]>=this_y & VEC.E[,2] <this_y+this_step_y)
this_center=c(this_x+this_step_x/2,this_y+this_step_y/2)
if(length(this_in_index)>=NUM_CUT){
#####################
INDEX_LIST=c(INDEX_LIST, list(this_in_index))
CENTER_LIST=c(CENTER_LIST, list(this_center))
######################
if(SHOW==TRUE){
points(this_center[1],this_center[2],col= 'black',pch=16,cex=0.5)
}
######################
}
this_y=this_y+this_step_y
}
#print(this_x)
this_x=this_x+this_step_x
}
############################################
OUT=list()
OUT$VEC=VEC.E
OUT$INDEX_LIST=INDEX_LIST
OUT$CENTER_LIST=CENTER_LIST
OUT$this_step_x=this_step_x
OUT$this_step_y=this_step_y
########################################
return(OUT)
}
vector.buildNet<-function(OUT,CUT=1,SHOW=TRUE,COL='grey70'){
library(igraph)
library(stringr)
OUT=OUT
SHOW=SHOW
CUT=CUT
COL=COL
VEC=OUT$VEC
INDEX_LIST=OUT$INDEX_LIST
CENTER_LIST=OUT$CENTER_LIST
this_step_x=OUT$this_step_x
this_step_y=OUT$this_step_y
delta=0.00001
###############################
if(SHOW==TRUE){
plot(VEC,col=COL,pch=16,cex=0.2)
}
#############################
CENTER_VEC=c()
i=1
while(i<=length(CENTER_LIST)){
CENTER_VEC=cbind(CENTER_VEC, CENTER_LIST[[i]])
i=i+1}
CENTER_VEC=t(CENTER_VEC)
OUT$CENTER_VEC=CENTER_VEC
##############################
p1=c()
p2=c()
#############################
CNUM=length(CENTER_LIST)
i=1
while(i<=CNUM){
this_p1_loc=CENTER_LIST[[i]]
this_p1=paste0('P',as.character(i))
used_j=which( ( abs(CENTER_VEC[,1]-this_p1_loc[1]) <= this_step_x+delta)
& ( abs(CENTER_VEC[,2]-this_p1_loc[2]) <= this_step_y+delta) )
for(j in used_j){
this_p2=paste0('P',as.character(j))
this_p2_loc=CENTER_LIST[[j]]
######################
if(length(INDEX_LIST[[i]])>=CUT &
length(INDEX_LIST[[j]])>=CUT &
this_p1 != this_p2){
p1=c(p1,this_p1)
p2=c(p2,this_p2)
if(SHOW==TRUE){
segments(x0=this_p1_loc[1],x1=this_p2_loc[1],
y0=this_p1_loc[2],y1=this_p2_loc[2],
col='black')
}
}
############
}
#if(i%%10==1){print(paste0(i,'/',CNUM))}
i=i+1
}
##########################
TAG=c()
i=1
while(i<=length(p1)){
this_p1=p1[i]
this_p2=p2[i]
sorted_pair=sort(c(this_p1,this_p2))
this_tag=paste0(sorted_pair[1],'|',sorted_pair[2])
TAG=c(TAG, this_tag)
i=i+1}
TAG=unique(TAG)
#NET=tapply(NET,1,sort)
p1=c()
p2=c()
i=1
while(i<=length(TAG)){
this_p1=strsplit(TAG[i],'\\|')[[1]][1]
this_p2=strsplit(TAG[i],'\\|')[[1]][2]
p1=c(p1,this_p1)
p2=c(p2,this_p2)
i=i+1}
##########################
library(igraph)
OUT$p1=p1
OUT$p2=p2
NET = cbind(p1,p2)
g <- make_graph(t(NET),directed = FALSE)
ALLNAME=paste0('P',1:CNUM)
ADD=ALLNAME[which(! ALLNAME %in% as_ids(V(g)))]
g <- g + ADD
##########################
DIST=distances(g, v = V(g), to = V(g), mode = c("all"))
library(stringr)
DIST.NUM=as.numeric(str_replace(colnames(DIST),'P',''))
DIST=DIST[order(DIST.NUM),order(DIST.NUM)]
###########################
library(igraph)
CPT=components(g)
MAXC=which(CPT$csize==max(CPT$csize))[1]
############
library(stringr)
###############
USED_NAME=names(which(CPT$membership==MAXC))
USED=as.numeric(str_replace(USED_NAME,'P',''))
USED_NAME=USED_NAME[order(USED)]
USED=USED[order(USED)]
if(SHOW==TRUE){
points(OUT$CENTER_VEC[USED,],col='red',pch=16, cex=0.5)
}
USED_INDEX=c()
i=1
while(i<=length(USED)){
USED_INDEX=c(USED_INDEX,INDEX_LIST[[USED[i]]])
i=i+1}
###################
OUT$GRAPH=g
OUT$DIST=DIST
OUT$USED=USED
OUT$USED_NAME=USED_NAME
OUT$USED_INDEX=USED_INDEX
return(OUT)
###########################
}
vector.gridValue <- function(OUT, SHOW=TRUE){
OUT=OUT
INDEX_LIST=OUT$INDEX_LIST
VALUE=OUT$VALUE
SHOW=SHOW
USED=OUT$USED
######################
################
CENTER_VALUE=c()
i=1
while(i<=length(INDEX_LIST)){
this_value = mean(VALUE[INDEX_LIST[[i]]])
CENTER_VALUE=c(CENTER_VALUE,this_value)
i=i+1}
############
CENTER_VEC=OUT$CENTER_VEC
##################
VALUE=VALUE
N.VALUE=(VALUE-min(VALUE))/(max(VALUE)-min(VALUE))
VALUE.COL=vector.vcol(N.VALUE, c(0,0.5,1),c('#009FFF','#FFF200','#ec2F4B'))
####################
#################################
VALUE=CENTER_VALUE
N.VALUE=(VALUE-min(VALUE))/(max(VALUE)-min(VALUE))
COL=vector.vcol(N.VALUE, c(0,0.5,1),c('#009FFF','#FFF200','#ec2F4B'))
################
if(SHOW==TRUE){
plot(OUT$VEC,col='grey80',pch=16,cex=0.5)
#points(OUT$CENTER_VEC,col=COL,pch=16)
points(OUT$CENTER_VEC[USED,],col=COL[USED],pch=15, cex=1.5)
}
################
OUT$CENTER_VALUE=CENTER_VALUE
OUT$ORIG.CENTER.COL=COL
#OUT$VALUE=VALUE
OUT$VALUE.COL=VALUE.COL
return(OUT)
}
vector.nonCenter<-function(OUT){
OUT=OUT
OUT$SCORE=OUT$CENTER_VALUE[OUT$USED]
OUT$COL=OUT$VALUE.COL
return(OUT)
}
vector.autoCenter <- function(OUT, UP=0.9, SHOW=TRUE){
#####################
OUT=OUT
SHOW=SHOW
UP=UP
#############################
DIST=OUT$DIST
USED=OUT$USED
USED_NAME=OUT$USED_NAME
CENTER_VALUE=OUT$CENTER_VALUE
CENTER_VEC=OUT$CENTER_VEC
CENTER_INDEX=OUT$CENTER_INDEX
INDEX_LIST=OUT$INDEX_LIST
############
USED_CENTER_VALUE=CENTER_VALUE[USED]
USED_CENTER_VEC=CENTER_VEC[USED,]
HIGH=USED[which(USED_CENTER_VALUE>=quantile(USED_CENTER_VALUE,UP))]
HIGH_NAME=USED_NAME[which(USED_CENTER_VALUE>=quantile(USED_CENTER_VALUE,UP))]
LOW_NAME=USED_NAME[which(USED_CENTER_VALUE<quantile(USED_CENTER_VALUE,UP))]
#plot(OUT$CENTER_VEC[USED,])
#points(CENTER_VEC[HIGH,], col='red',pch=16)
#plot(CENTER_VEC[HIGH,], col='red')
SUB=induced_subgraph(OUT$GRAPH, HIGH_NAME)
SUB_CPT=components(SUB)
CLUSTER=list()
LENGTH=c()
PCH=rep(1,length(HIGH))
DIST_COR=c()
DIST_MEAN=c()
i=1
while(i<=SUB_CPT$no){
this_name=names(which(SUB_CPT$membership==i))
this_index=as.numeric(str_replace(this_name,'P',''))
PCH[which(HIGH %in% this_index)]=as.character(i)
LENGTH=c(LENGTH, length(this_index))
if(length(this_index)==1){
this_dist=DIST[USED, this_index]
}else{
this_dist=apply(DIST[USED, this_index],1,mean)
}
this_cor=cor(this_dist, CENTER_VALUE[USED],method='spearman')
DIST_COR=c(DIST_COR,this_cor)
DIST_MEAN=c(DIST_MEAN, mean(this_dist))
CLUSTER=c(CLUSTER,list(this_index))
i=i+1}
####################
#SELECT=which(DIST_COR==min(DIST_COR))[1]
#SELECT=which(rank(DIST_MEAN)*rank(DIST_COR) == min(rank(DIST_MEAN)*rank(DIST_COR)) )
TMP=10^LENGTH - DIST_COR
#TMP=rank(-DIST_COR) * rank(LENGTH )
SELECT=which(TMP==max(TMP))[1]
#SELECT=which(LENGTH==max(LENGTH))[1]
#SELECT=which( rank(-DIST_COR) * rank(LENGTH) == max( rank(-DIST_COR) * rank(LENGTH) ) )[1]
SUMMIT=CLUSTER[[SELECT]]
#print(SELECT)
#plot(OUT$CENTER_VEC[USED,])
#points(OUT$CENTER_VEC[CLUSTER[[9]],],pch=16,col='red')
SCORE=c()
PS=c()
i=1
while(i<=length(USED_NAME)){
this_name=USED_NAME[i]
this_dist=DIST[which(colnames(DIST)==this_name),
which(rownames(DIST) %in% paste0('P',SUMMIT))]
this_dist=this_dist
#this_value=CENTER_VALUE[USED]
this_score=min(this_dist)#sum(rank(-this_dist) * rank(this_value))
#this_cor=cor(-this_value, this_dist)#,method='spearman')
SCORE=c(SCORE, this_score)
PS=c(PS, this_score)
i=i+1}
SCORE=max(SCORE)-SCORE
##########################################
VALUE=SCORE
#plot(OUT$VEC, col='grey70',pch=16)
N.VALUE=(VALUE-min(VALUE))/(max(VALUE)-min(VALUE))
#COL=vector.vcol(N.VALUE, c(0,0.5,1),c('#009FFF','#FFF200','#ec2F4B')) # too strong
#COL=vector.vcol(N.VALUE,c(0,0.5,1),c('#009FFF','#FFF200','#ffdde1')) # too weak
COL=vector.vcol(N.VALUE,c(0,0.5,1),c('#009FFF','#FFF200','#ee9ca7'))
###################################################
OUT$COL=rep('grey70',nrow(OUT$VEC))
OUT$ORIG.COL=rep('grey70',nrow(OUT$VEC))
OUT$P.SCORE=rep(0,nrow(OUT$VEC))
OUT$P.PS=rep(NA,nrow(OUT$VEC))
i=1
while(i<=length(USED)){
this_index=INDEX_LIST[[USED[i]]]
OUT$COL[this_index]=COL[i]
OUT$ORIG.COL[this_index]=OUT$ORIG.CENTER.COL[USED][i]
OUT$P.SCORE[this_index]=SCORE[i]
OUT$P.PS[this_index]=PS[i]
i=i+1
}
###########
if(SHOW==TRUE){
#plot(OUT$VEC, col=OUT$COL, pch=16, cex=0.5 )
plot(OUT$VEC, col=OUT$ORIG.COL, pch=16, cex=0.5)
text(CENTER_VEC[HIGH,1], CENTER_VEC[HIGH,2],labels=PCH,cex=1,pos=2)
points(CENTER_VEC[HIGH,1], CENTER_VEC[HIGH,2], col='black',pch=16,cex=1)
points(CENTER_VEC[SUMMIT,1],CENTER_VEC[SUMMIT,2], col='black',pch=16,cex=1.5)
points(CENTER_VEC[SUMMIT,1],CENTER_VEC[SUMMIT,2], col='red',pch=16,cex=1)
}
######################
############
OUT$SCORE=SCORE
OUT$SUMMIT=SUMMIT
OUT$CLUSTER=CLUSTER
OUT$LENGTH=LENGTH
OUT$PCH=PCH
OUT$DIST_COR=DIST_COR
OUT$PS=PS
#OUT$DIST_MEAN=DIST_MEAN
################################
#######################
return(OUT)
}
vector.drawArrow <- function(OUT, P=0.9, SHOW=TRUE, COL='grey70',OL=1.5,AL=60,CEX=0.5,AW=2, BD=TRUE, AC='grey20', SHOW.SUMMIT=TRUE){
################
AW=AW
BD=BD
AC=AC
OUT=OUT
SHOW=SHOW
P=P
COL=COL
USED=OUT$USED
DIST=OUT$DIST
ALL_VEC=OUT$VEC
USED_NAME=OUT$USED_NAME
USED_CENTER_VEC=OUT$CENTER_VEC[USED,]
USED_DIST=OUT$DIST[which(rownames(DIST) %in% USED_NAME),which(rownames(DIST) %in% USED_NAME)]
SCORE=OUT$SCORE
AL=AL
OL=OL
CEX=CEX
SHOW.SUMMIT=SHOW.SUMMIT
#####################
one=min(dist(USED_CENTER_VEC)) * OL
#####################
###################
.norm_one <-function(x,one=1){
one=one
if(var(x)!=0){
x=x/sqrt(sum(x^2)) * one }
return(x)
}
DIV=1/P
####################
if(SHOW==TRUE){
#plot(ALL_VEC,col=COL,pch=16,cex=CEX)
if(BD==TRUE){
plot(ALL_VEC,col=COL,pch=16,cex=CEX,
xlim=c(min(ALL_VEC[,1])-one, max(ALL_VEC[,1])+one ),
ylim=c(min(ALL_VEC[,2])-one, max(ALL_VEC[,2])+one )
)
}else{
plot(ALL_VEC,col=COL,pch=16,cex=CEX,
xlim=c(min(ALL_VEC[,1])-one, max(ALL_VEC[,1])+one ),
ylim=c(min(ALL_VEC[,2])-one, max(ALL_VEC[,2])+one ) , yaxt="n", axes=F
)
}
}
##########################
N.SCORE=.normX(SCORE)
SCORE.COL=vector.vcol(N.SCORE, c(0,0.5,1),c('#009FFF','#FFF200','#ec2F4B'))
A1_VEC=c()
A2_VEC=c()
A_LENGTH=c()
#####################
#one=min(dist(USED_CENTER_VEC)) * OL
#####################
i=1
while(i<=length(USED)){
this_p1_loc=USED_CENTER_VEC[i,]
vector_list=cbind(USED_CENTER_VEC[,1]-this_p1_loc[1],USED_CENTER_VEC[,2]-this_p1_loc[2])
vector_list_norm=t(apply(vector_list,1,.norm_one, one))
vector_weight_1= DIV^-(rank(USED_DIST[i,])-1) # equals to P ^ (rank(USED_DIST[i,])-1)
vector_weight_2= SCORE[i]-SCORE # PS - PS[i]; "PS_i - PS_j" in the paper; In paper, PS[i] is "PS_j".
vector_weight = vector_weight_1 * vector_weight_2
vector_weight = vector_weight/sum(abs(vector_weight))
final_vec=t(vector_list_norm) %*% vector_weight
this_p2_loc=c(this_p1_loc[1]+final_vec[1],this_p1_loc[2]+final_vec[2])
#plot(ALL_VEC,col=COL,pch=16,cex=0.2)
#this_arrow_length=0.1*sqrt(sum(final_vec^2))
#this_arrow_length=sqrt(sum(final_vec^2))#0.1 #* (1+sqrt(sum(final_vec^2)))
this_arrow_length=dev.size()[1]/AL * sqrt(sum(final_vec^2))/one # * sqrt(sum(final_vec^2)) #0.25
if(SHOW==TRUE){
this_arrow_col=AC#'grey20'
#if(AC==TRUE){this_arrow_col=SCORE.COL[i]}
arrows(x0=this_p1_loc[1],y0=this_p1_loc[2],
x1=this_p2_loc[1],y1=this_p2_loc[2],
lwd=AW, length=this_arrow_length,
col=this_arrow_col
#col=this_arrow_col#SCORE.COL[i]
)
}
A1_VEC=cbind(A1_VEC,this_p1_loc)
A2_VEC=cbind(A2_VEC,this_p2_loc)
A_LENGTH=c(A_LENGTH,this_arrow_length)
i=i+1}
###############################
if(SHOW==TRUE & SHOW.SUMMIT==TRUE){
#points(OUT$CENTER_VEC[OUT$SUMMIT,],col='black',pch=16,cex=1)
#points(OUT$CENTER_VEC[OUT$SUMMIT,],col='red',pch=16,cex=0.5)
X1=min(OUT$CENTER_VEC[OUT$SUMMIT,1])-one/10
X2=max(OUT$CENTER_VEC[OUT$SUMMIT,1])+one/10
Y1=min(OUT$CENTER_VEC[OUT$SUMMIT,2])-one/10
Y2=max(OUT$CENTER_VEC[OUT$SUMMIT,2])+one/10
rect(xleft=X1, ybottom=Y1, xright=X2, ytop=Y2, angle = 45,
col = NA, border = '#009FFF', lty = 1, lwd = 2)
#rect(xleft=X1, ybottom=Y1, xright=X2, ytop=Y2, angle = 45,
#col = NA, border = 'black', lty = 2, lwd = 2)
}
#################################
A1_VEC=t(A1_VEC)
A2_VEC=t(A2_VEC)
######
#OUT=list()
OUT$A1_VEC=A1_VEC
OUT$A2_VEC=A2_VEC
OUT$A_LENGTH=A_LENGTH
OUT$A_COL=SCORE.COL
###########
return(OUT)
}
#################################################################
# Manually do something
vector.selectPoint<-function(VEC,CEX=0.5){
VEC=VEC
CEX=CEX
############
library(gatepoints)
selectedPoints <- fhs(VEC, pch=16,col='red3',cex=CEX,mark = TRUE)
return(selectedPoints)
}
vector.selectCenter <- function(OUT){
#####################
OUT=OUT
SHOW=TRUE
#############################
DIST=OUT$DIST
USED=OUT$USED
USED_NAME=OUT$USED_NAME
CENTER_VALUE=OUT$CENTER_VALUE
CENTER_VEC=OUT$CENTER_VEC
CENTER_INDEX=OUT$CENTER_INDEX
INDEX_LIST=OUT$INDEX_LIST
############
plot(OUT$VEC, col='grey80',pch=16,cex=0.5)
points(OUT$CENTER_VEC[USED,], col=OUT$ORIG.CENTER.COL[USED], pch=16, cex=1)
SELECT_NAME=vector.selectPoint(OUT$CENTER_VEC[USED,],CEX=1)
SUMMIT=USED[as.numeric(SELECT_NAME)]
#print(SELECT)
#plot(OUT$CENTER_VEC[USED,])
#points(OUT$CENTER_VEC[CLUSTER[[9]],],pch=16,col='red')
SCORE=c()
i=1
while(i<=length(USED_NAME)){
this_name=USED_NAME[i]
this_dist=DIST[which(colnames(DIST)==this_name),which(rownames(DIST) %in% paste0('P',SUMMIT))]
this_dist=this_dist
#this_value=CENTER_VALUE[USED]
this_score=min(this_dist)#sum(rank(-this_dist) * rank(this_value))
#this_cor=cor(-this_value, this_dist)#,method='spearman')
SCORE=c(SCORE, this_score)
i=i+1}
PS=SCORE
SCORE=max(SCORE)-SCORE
##########################################
VALUE=SCORE
#plot(OUT$VEC, col='grey70',pch=16)
N.VALUE=(VALUE-min(VALUE))/(max(VALUE)-min(VALUE))
COL=vector.vcol(N.VALUE, c(0,0.5,1),c('#009FFF','#FFF200','#ec2F4B'))
###################################################
OUT$COL=rep('grey70',nrow(OUT$VEC))
OUT$ORIG.COL=rep('grey70',nrow(OUT$VEC))
OUT$P.SCORE=rep(0,nrow(OUT$VEC))
OUT$P.PS=rep(NA,nrow(OUT$VEC))
i=1
while(i<=length(USED)){
this_index=INDEX_LIST[[USED[i]]]
OUT$COL[this_index]=COL[i]
OUT$ORIG.COL[this_index]=OUT$ORIG.CENTER.COL[USED][i]
OUT$P.SCORE[this_index]=SCORE[i]
OUT$P.PS[this_index]=PS[i]
i=i+1
}
###########
if(SHOW==TRUE){
plot(OUT$VEC, col=OUT$COL, pch=16, cex=0.5 )
points(CENTER_VEC[SUMMIT,], col='black',pch=16,cex=1.5)
points(CENTER_VEC[SUMMIT,], col='red',pch=16,cex=1)
}
######################
############
OUT$SCORE=SCORE
OUT$PS=PS
OUT$SUMMIT=SUMMIT
################################
#######################
return(OUT)
}
vector.reDrawArrow <- function(OUT, COL='grey70'){
################
A1_VEC=OUT$A1_VEC
A2_VEC=OUT$A2_VEC
A_LENGTH=OUT$A_LENGTH
#A_COL=OUT$A_COL
VEC=OUT$VEC
COL=COL
#####################
plot(x=VEC[,1],y=VEC[,2], col=COL,cex=0.5, pch=16)
i=1
while(i<=length(A_LENGTH)){
arrows(x0=A1_VEC[i,1],y0=A1_VEC[i,2],
x1=A2_VEC[i,1],y1=A2_VEC[i,2],
lwd=2, length=A_LENGTH[i],
col='black'
#col=A_COL[i]
)
i=i+1
}
############################
return(OUT)
############################
}
vector.selectRegion <- function(OUT){
#######################
P.SCORE=OUT$P.SCORE
################
A1_VEC=OUT$A1_VEC
A2_VEC=OUT$A2_VEC
A_LENGTH=OUT$A_LENGTH
VEC=OUT$VEC
P.PS=OUT$P.PS
#####################
CENTER_LIST=OUT$CENTER_LIST
INDEX_LIST=OUT$INDEX_LIST
USED=OUT$USED
################################
SELECT_NAME=vector.selectPoint(VEC,CEX=0.1)
#########################
SELECT_INDEX=which(rownames(VEC) %in% SELECT_NAME)
SELECT_INDEX=SELECT_INDEX[which(SELECT_INDEX %in% OUT$USED_INDEX)]
#########################
#########################
A_USED=c()
i=1
while(i<=length(USED)){
if( length(which(INDEX_LIST[[USED[i]]] %in% SELECT_INDEX )) > 0 ){
A_USED=c(A_USED, i)
}
i=i+1}
##########################
#Draw new
COL=OUT$COL
COL[which(!rownames(VEC) %in% SELECT_NAME)]='grey70'
plot(x=VEC[,1],y=VEC[,2], col=COL,cex=0.5, pch=16)
#points(x=VEC[,1],y=VEC[,2], col=COL,cex=0.5, pch=16)
#########################
i=1
while(i<=length(A_LENGTH)){
arrows(x0=A1_VEC[i,1],y0=A1_VEC[i,2],
x1=A2_VEC[i,1],y1=A2_VEC[i,2],
lwd=2, length=A_LENGTH[i],
col='black'
)
i=i+1
}
##########################
#points(x=VEC[,1],y=VEC[,2], col='grey70',cex=0.5, pch=16)
for(i in A_USED){
arrows(x0=A1_VEC[i,1],y0=A1_VEC[i,2],
x1=A2_VEC[i,1],y1=A2_VEC[i,2],
lwd=2, length=A_LENGTH[i],
col='red'
)
}
#########################
OUT$A_USED=A_USED
OUT$SELECT_NAME=SELECT_NAME
OUT$SELECT_INDEX=SELECT_INDEX
OUT$SELECT_SCORE=P.SCORE[SELECT_INDEX]
OUT$SELECT_PS=P.PS[SELECT_INDEX]
########################
return(OUT)
}
vector.reDrawRegion <- function(OUT){
#######################
P.SCORE=OUT$P.SCORE
################
A1_VEC=OUT$A1_VEC
A2_VEC=OUT$A2_VEC
A_LENGTH=OUT$A_LENGTH
VEC=OUT$VEC
#####################
CENTER_LIST=OUT$CENTER_LIST
INDEX_LIST=OUT$INDEX_LIST
USED=OUT$USED
################################
SELECT_NAME=OUT$SELECT_NAME#vector.selectPoint(VEC,CEX=0.1)
#########################
SELECT_INDEX=which(rownames(VEC) %in% SELECT_NAME)
#########################
#########################
A_USED=c()
i=1
while(i<=length(USED)){
if( length(which(INDEX_LIST[[USED[i]]] %in% SELECT_INDEX )) > 0 ){
A_USED=c(A_USED, i)
}
i=i+1}
##########################
#Draw new
COL=OUT$COL
COL[which(!rownames(VEC) %in% SELECT_NAME)]='grey70'
plot(x=VEC[,1],y=VEC[,2], col=COL,cex=0.5, pch=16)
#points(x=VEC[,1],y=VEC[,2], col=COL,cex=0.5, pch=16)
#########################
i=1
while(i<=length(A_LENGTH)){
arrows(x0=A1_VEC[i,1],y0=A1_VEC[i,2],
x1=A2_VEC[i,1],y1=A2_VEC[i,2],
lwd=2, length=A_LENGTH[i],
col='black'
)
i=i+1
}
##########################
#points(x=VEC[,1],y=VEC[,2], col='grey70',cex=0.5, pch=16)
for(i in A_USED){
arrows(x0=A1_VEC[i,1],y0=A1_VEC[i,2],
x1=A2_VEC[i,1],y1=A2_VEC[i,2],
lwd=2, length=A_LENGTH[i],
col='red'
)
}
#########################
OUT$A_USED=A_USED
OUT$SELECT_NAME=SELECT_NAME
OUT$SELECT_INDEX=SELECT_INDEX
OUT$SELECT_SCORE=P.SCORE[SELECT_INDEX]
OUT$SELECT_PS=P.PS[SELECT_INDEX]
########################
return(OUT)
}
######################################
vector.RPPCA <- function(PCA){
PCA=PCA
##################
R.PCA=apply(PCA,2,rank)
library(gmodels)
D=t(R.PCA)
PCA.OUT=fast.prcomp(t(D), retx = TRUE, center = FALSE, scale. = FALSE, tol = NULL)
PPCA=PCA.OUT$x
return(PPCA)
}
vector.SeuratPCA <-function(pbmc, CUT=0.5){
pbmc=pbmc
CUT=CUT
#####################
library(gmodels)
D=as.matrix(pbmc@assays$RNA@scale.data)
PCA.OUT=fast.prcomp(t(D), retx = TRUE, center = FALSE, scale. = FALSE, tol = NULL)
EXP=(cumsum(PCA.OUT$sdev^2)/sum(PCA.OUT$sdev^2))
N=min(which( cumsum(PCA.OUT$sdev^2)/sum(PCA.OUT$sdev^2) > CUT))
#####################
PCA.OUT$EXP=EXP
PCA.OUT$CUT=CUT
PCA.OUT$N=N
#N=min(which( cumsum(PCA.OUT$sdev^2)/sum(PCA.OUT$sdev^2) > 0.7))
return(PCA.OUT)
}
vector.SeuratRandomPCA <-function(pbmc, RN=1000, CUT=0.5){
pbmc=pbmc
CUT=CUT
RN=RN
#####################
library(gmodels)
D=as.matrix(pbmc@assays$RNA@scale.data)
R_INDEX=sample(1:ncol(D), RN, replace =TRUE)
###########################
RD=D[,R_INDEX]
PCA.OUT=fast.prcomp(t(RD), retx = TRUE, center = FALSE, scale. = FALSE, tol = NULL)
EXP=(cumsum(PCA.OUT$sdev^2)/sum(PCA.OUT$sdev^2))
N=min(which( cumsum(PCA.OUT$sdev^2)/sum(PCA.OUT$sdev^2) > CUT))
PRED.PCA=t(D) %*% PCA.OUT$rotation
#####################
PCA.OUT$EXP=EXP
PCA.OUT$CUT=CUT
PCA.OUT$N=N
PCA.OUT$RN=RN
PCA.OUT$R_INDEX=R_INDEX
PCA.OUT$PRED.PCA=PRED.PCA
#N=min(which( cumsum(PCA.OUT$sdev^2)/sum(PCA.OUT$sdev^2) > 0.7))
return(PCA.OUT)
}
vector.medCurv <- function(PCA,MAX=1000){
PCA=PCA
MAX=MAX
MS=vector.calValue(PCA)$PCA.RC
COR=cor(MS)
MED=c()
ALL=c()
i=1
while(i<=ncol(COR) & i<=MAX){
this_col=COR[1:i,i]
ALL=c(ALL,this_col)
ALL[which(ALL==1)]=NA
this_med=median(ALL,na.rm = TRUE)
MED=c(MED,this_med)
if(i %%100==1){print(i)}
i=i+1}
MED[1]=MED[2]
return(MED)
}
# 2020.01.03
vector.smoothOut <- function(X, Z){
Y=X
Y[order(Z)]=X[order(Z)]-smooth(X[order(Z)])
return(Y)
}
vector.regressOut <- function(X, Z){
FIT=lm(X~Z)