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spQSP_SplnMarkers.R
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spQSP_SplnMarkers.R
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## author: Atul Deshpande
## email: adeshpande@jhu.edu
rm(list = ls())
getSpatialParameters_spQSP <- function(spatialPatterns){
good_gene_threshold <- 3;
sigmaRes <- max(floor(min(diff(range(spatialPatterns$x)),diff(range(spatialPatterns$y)))/250),1)
sigmaVec <- seq(2,40*sigmaRes,sigmaRes)
threshVec <- seq(1,2,0.1)
#goodGenes <- rownames(fullMat)[apply(fullMat,1,function(x) sum(x>0)>=good_gene_threshold)]
patternList <- colnames(spatialPatterns)[startsWith(colnames(spatialPatterns),"Pattern_")]
optParams<-sapply(patternList, function(i) unlist(getOptimalSigmaThresh(pattern = spatialPatterns[,i],
locs = data.frame(x = spatialPatterns$x, y = spatialPatterns$y),
sigmaVec, threshVec)))
return(optParams)
}
library(ggplot2)
library(dplyr)
library(tidyr)
library(matrixStats)
library(caret)
library(gridExtra)
library(patchwork)
library(hdf5r)
library(ggpubr)
library(stringr)
library(viridis)
source("./R/preprocessing.R")
source("./R/getSpatialParameters.R")
source("./R/getInteractingGenes.R")
source('./R/find_genes_of_interest_nonparametric_fast.R')
working_dir <- 'PATH_TO_WORKING_DIR'
#spQSP Simulation results
input_folder <- './sample_simulated_result'
#Desired output folder
output_folder <- './out_sample_simulated_result'
setwd(working_dir)
# SpInMarkersMode: defaut mode is "residual". You can also set "DE" mode for Differential Expression mode.
SpInMarkersMode = "DE"
# SpinMarkersRefPattern is the pattern whose "interaction" with every other pattern we want to study.
# Pattern_1: Cancer Cell
# Pattern_2 CD8+ T cell
SpinMarkersRefPattern = "Pattern_1"
patient_sample <- commandArgs(trailingOnly = TRUE)[1]
y_slice <- 5
cellPath <- sprintf('%s/snapShots/cell_280.csv', input_folder)
cellData_raw <- read.csv(cellPath)
#filter T_EFF and TEXH
cellData <- cellData_raw[!(cellData_raw$State %in% c(3,5)),]
#preprocessing and organize cytokine data
cytokinePath <- sprintf('%s/snapShots/grid_core_280.csv', input_folder)
cytokineData <- read.csv(cytokinePath)
#preprocessing and organize cytokine data
vasPath <- sprintf('%s/snapShots/vas_280.csv', input_folder)
vasData <- read.csv(vasPath)
##Organize Type Data
cellData$Pattern_ <- as.character(cellData$Type)
dmy <- dummyVars("~ Pattern_", cellData)
cellData <- cbind(cellData, data.frame(predict(dmy, newdata=cellData)))
cellData$State_ <- as.character(cellData$State)
dmyState <- dummyVars("~ State_", cellData)
cellData <- cbind(cellData, data.frame(predict(dmyState, newdata=cellData)))
##Addup all cells in each coordinate
cellData_2D <- cellData[cellData$y == y_slice,]
#typeData_2D <- subset (cellData_2D, select = c(x,z,Pattern_1, Pattern_2, Pattern_3, Pattern_4, Pattern_5))
typeData_2D <- subset (cellData_2D, select = c(x,z,Pattern_1, Pattern_2))
names(typeData_2D)[names(typeData_2D) == 'z'] <- 'y'
typeData_2D_agg <- aggregate(cbind(typeData_2D$Pattern_1, typeData_2D$Pattern_2),
list(typeData_2D$x, typeData_2D$y), sum)
#typeData_2D_agg <- aggregate(cbind(typeData_2D$Pattern_1, typeData_2D$Pattern_2, typeData_2D$Pattern_3, cellData_2D$Pattern_4, typeData_2D$Pattern_5 ),
#list(typeData_2D$x, typeData_2D$y), sum)
names(typeData_2D_agg) <- c('x', 'y', 'Pattern_1', 'Pattern_2')
#names(typeData_2D_agg) <- c('x', 'y', 'Pattern_1', 'Pattern_2', 'Pattern_3', 'Pattern_4', 'Pattern_5')
spPatterns_spQSP <- cbind(barcodes = paste(typeData_2D_agg$x, "_", typeData_2D_agg$y), typeData_2D_agg)
rownames(spPatterns_spQSP) <- spPatterns_spQSP$barcodes
expressionData = merge(cytokineData, vasData, by.x=c('x', 'y', 'z'), by.y=c('x', 'y', 'z'))
expressionData_2D <- expressionData[expressionData$y == 5,]
expressionData_2D <- subset (expressionData_2D, select = -c(y))
names(expressionData_2D)[names(expressionData_2D) == 'z'] <- 'y'
rownames(expressionData_2D) <- paste(expressionData_2D$x, "_", expressionData_2D$y)
filtered_expressionData_2D <- expressionData_2D[rownames(spPatterns_spQSP),]
colnames(filtered_expressionData_2D)[13] <- 'Vasculature'
cgMat_spQSP <- t(subset (filtered_expressionData_2D, select = -c(x,y)))
fullMat_spQSP <- as(cgMat_spQSP, "sparseMatrix")
#spPatterns_spQSP <- subset(spPatterns_spQSP, select = -c(Pattern_3, Pattern_4, Pattern_5) )
optParams_spQSP <- getSpatialParameters_spQSP(spPatterns_spQSP)
optParams <- optParams_spQSP
SpInMarkers_spQSP <- getInteractingGenes(data = fullMat_spQSP, reconstruction = NULL,
spatialPatterns = spPatterns_spQSP,
refPattern = SpinMarkersRefPattern, mode = SpInMarkersMode)
#replace Pattern_1 value to either NA(0) or "Pattern_1" (1)
hotspot_matrix <- subset(spPatterns_spQSP, select = -c(x, y, barcodes))
rownames(hotspot_matrix) <- rownames(SpInMarkers_spQSP[["hotspotRegions"]])
#
hotspot_matrix$Pattern_1[hotspot_matrix$Pattern_1 == 0] <- NA
hotspot_matrix$Pattern_1[hotspot_matrix$Pattern_1 == 1] <- "Pattern_1"
#hotspot_matrix$Pattern_1 <- SpInMarkers_spQSP[["hotspotRegions"]][,'Pattern_1']
#hotspot_matrix$Pattern_4[hotspot_matrix$Pattern_4 == 0] <- NA
#hotspot_matrix$Pattern_4[hotspot_matrix$Pattern_4 == 1] <- "Pattern_4"
#hotspot_matrix$Pattern_5[hotspot_matrix$Pattern_5 == 0] <- NA
#hotspot_matrix$Pattern_5[hotspot_matrix$Pattern_5 == 1] <- "Pattern_5"
# ###
hotspot_matrix$Pattern_2 <- SpInMarkers_spQSP[["hotspotRegions"]][,'Pattern_2']
#hotspot_matrix$Pattern_3 <- SpInMarkers_spQSP[["hotspotRegions"]][,'Pattern_3']
hotspot_matrix <- as.matrix(hotspot_matrix)
SpInMarkers_spQSP[["hotspotRegions"]] <- hotspot_matrix
SpInMarkers_spQSP <- getInteractingGenes(data = fullMat_spQSP, reconstruction = NULL,
spatialPatterns = spPatterns_spQSP, refPattern = SpinMarkersRefPattern,
mode = SpInMarkersMode, hotspotRegions = hotspot_matrix)
dir.create(file.path(output_folder), recursive = TRUE)
setwd(output_folder)
for (i in seq(1,length(SpInMarkers_spQSP$interacting_genes)))
{
filename <- paste0(gsub(x = 'spQSP_SPIN',pattern = '.rds',replacement = '_SpInMarkers_'),names(SpInMarkers_spQSP$interacting_genes[[i]])[1],".txt")
write.table(rownames(SpInMarkers_spQSP$interacting_genes[[i]]),file = filename,row.names = F,col.names = F, quote = F)
}
write.table((!is.na(SpInMarkers_spQSP$hotspotRegions))*1,file = 'hotspot.txt',row.names = T,col.names = T, quote = F)
#write.table((!is.na(hotspot_matrix))*1,file = 'hotspot.txt',row.names = T,col.names = T, quote = F)
write.table(subset(spPatterns_spQSP, select = c(x, y)),file = 'spatial_coord.txt',row.names = T,col.names = T, quote = F)
saveRDS(SpInMarkers_spQSP, file = 'spQSP_SPIN.rds')
################################################################################################################################################################
SpInMarkers_spQSP <- readRDS('spQSP_SPIN.rds')
geneList <- c("IL_2","Vasculature","VEGFA","TGFB" )
region <- SpInMarkers_spQSP$hotspotRegions[,1]
region <- ifelse(!is.na(region) & !is.na(SpInMarkers_spQSP$hotspotRegions[,2]),"Interacting",ifelse(!is.na(region),region,SpInMarkers_spQSP$hotspotRegions[,2]))
region <- factor(region, levels = c("Pattern_1", "Interacting", "Pattern_2"))
my_comparisons <- list( c("Interacting", "Pattern_1"),c("Interacting", "Pattern_2"), c("Pattern_1", "Pattern_2") )
png(filename = "Expression_boxplots_spQSP.png", units="in",width = 9,height = 8, res=600)
plist <- list()
mplot2 <- filtered_expressionData_2D[!is.na(region),]
region <- region[!is.na(region)]
mplot2 <- cbind(mplot2, region)
for (ii in 1:length(geneList)){
if(geneList[ii] == 'Vasculature'){
title <- ''
ylabel = 'Vascular Density'
}
else if(geneList[ii] == 'IL_2'){
title <- ''
ylabel = 'IL2 Concentration (ng/mL)'
}
else{
title = ''
unit <- ' Concentration (ng/mL)'
ylabel = paste0(geneList[ii], unit)
}
plist[[ii]]<- mplot2 %>% ggplot( aes_string(x="region", y=geneList[ii], fill="region")) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
#geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_bw() +
theme(
legend.position="none",
plot.title = element_text(size=14),
axis.text = element_text(size = 12),
axis.title.y = element_text(size = 12)
) +
ggtitle(title) +
xlab("") +
ylab(ylabel)+
scale_x_discrete(labels=c('Cancer', 'Interacting', 'CD8')) +
stat_compare_means(comparisons = my_comparisons, size = 4)
}
n <- length(plist)
nCol <- floor(sqrt(n))
do.call("grid.arrange", c(plist, ncol=2))
dev.off()