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Neurodiff_scATAC_ArchR_analysis_2021.Rmd
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---
title: "Neuro Differentiation Single Cell RNAseq"
Authors: Ankush Sharma & Martin Falck
output:
html_document:
df_print: paged
---
```{r setup, message=FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
<!-- #################################### -->
<!-- #loading data and Quality Control -->
<!-- #################################### -->
```{r 1_library and work, include=FALSE}
#Install ArchR
#if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools")
#if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
#devtools::install_github("GreenleafLab/ArchR", ref="master", repos = BiocManager::repositories())
#if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("BSgenome.Hsapiens.UCSC.hg38")}#
#BiocManager::install("TxDb.Hsapiens.UCSC.hg38.knownGene")
library(ArchR)
ArchR::installExtraPackages()
library(progress)
library(hdf5r)
library(HDF5Array)
library(TFBSTools)
library(hexbin)
library(Seurat)
set.seed(1)
#Add Reference genome, we used hg38 as reference genome
addArchRGenome("hg38")
library(BSgenome.Hsapiens.UCSC.hg38)
genomeAnnotation <- createGenomeAnnotation(genome = BSgenome.Hsapiens.UCSC.hg38)
genomeAnnotation
library(org.Hs.eg.db)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
currentjob <- "neuroDiffscATAC"
# Memory settings
options(future.globals.maxSize = 4000 * 1024^3) #Set a higher maximum memory size
addArchRThreads(threads = 1)
geneAnnotation <- createGeneAnnotation(TxDb = TxDb.Hsapiens.UCSC.hg38.knownGene, OrgDb = org.Hs.eg.db)
geneAnnotation <- createGeneAnnotation(
TSS = geneAnnotation$TSS,
exons = geneAnnotation$exons,
genes = geneAnnotation$genes)
```
```{r 2_Creating ARROW files,include=FALSE}
# Creating arrow files for ATAC seq for d0 and d20
ArrowFiles <- createArrowFiles(
inputFiles <-c("~/Dropbox (UiO)/Paracetamol_shared/SINGLE_CELL_ATAC/core_facility_cellranger_analysis/d0_ctrl/fragments.tsv.gz","~/Dropbox (UiO)/Paracetamol_shared/SINGLE_CELL_ATAC/core_facility_cellranger_analysis/d20_ctrl/fragments.tsv.gz"),
sampleNames = c("D0_Ctrl","D20_Ctrl"),
filterTSS = 3, #Dont set this too high because you can always increase later
filterFrags = 1000,
addTileMat = TRUE,
addGeneScoreMat = TRUE
)
ArrowFiles
```
```{r 3_doublet score,include=FALSE}
Sys.setenv('R_MAX_VSIZE'=1200000)
doubScores <- addDoubletScores(
input = ArrowFiles,
k = 10, #Refers to how many cells near a "pseudo-doublet" to count.
knnMethod = "UMAP", #Refers to the embedding to use for nearest neighbor search with doublet projection.
LSIMethod = 1
)
doubScores
plotPDF(plot, name = "0_doubscores_k10_UMAP_LSI1.pdf", addDOC = TRUE)
```
```{r 4_Setting up project}
Sys.setenv('R_MAX_VSIZE'=1200000)
set.seed(1)
Differentiation_P_1 <- ArchRProject(
ArrowFiles = ArrowFiles,
outputDirectory = "Differentiation_P_1",
copyArrows = FALSE
)
#This is recommended so that if you modify the Arrow files you have an original copy for later usage.
Differentiation_P_1
```
```{r 5_Setting up memory,include=FALSE}
paste0("Memory Size = ", round(object.size(Differentiation_P_1) / 10^6, 2), "MB")
getAvailableMatrices(Differentiation_P_1)
```
```{r 6_NAMES, include=FALSE}
##these steps are not neccesaary but dooesnot harm to run
#access the cell names and sample names associated with each cell:
head(Differentiation_P_1$cellNames)
head(Differentiation_P_1$Sample)
#access the TSS Enrichment Scores for each cell
quantile(Differentiation_P_1$TSSEnrichment)
#subset the project numerically, for example taking the first 100 cells
Differentiation_P_1[1:100, ]
#subset the project based on certain cell names
Differentiation_P_1[Differentiation_P_1$cellNames[1:100], ]
```
```{r plotting data1}
#retreiving column by name , such as number of unique nuclear (i.e. non-mitochondrial) fragments per cell:
df <- getCellColData(Differentiation_P_1, select = "nFrags")
df
#performing operation of columns
df <- getCellColData(Differentiation_P_1, select = c("log10(nFrags)", "nFrags - 1"))
df
#standard scATAC-seq metrics for quality control of individual cells
df <- getCellColData(Differentiation_P_1, select = c("log10(nFrags)", "TSSEnrichment"))
df
#Number of unique nuclear fragments (log10) by the TSS enrichment score. This plot is key for identifying high quality cells.
plot1 <- ggPoint(
x = df[,1],
y = df[,2],
colorDensity = TRUE,
continuousSet = "sambaNight",
xlabel = "Log10 Unique Fragments",
ylabel = "TSS Enrichment",
xlim = c(log10(500), quantile(df[,1], probs = 0.99)),
ylim = c(0, quantile(df[,2], probs = 0.99))
) + geom_hline(yintercept = 4, lty = "dashed") + geom_vline(xintercept = 3, lty = "dashed")
plot1
plotPDF(plot, name = "0_QC-Differentiation_TSS-vs-Frags.pdf", ArchRProj = Differentiation_P_1, addDOC = TRUE)
```
```{r Plotting Sample Statistics}
ArchRPalettes$custom <- c('D0_Ctrl' = '#FF7C70','D20_Ctrl' = '#AECDDF')
sample_id_palette <- ArchRPalettes$custom
plot1 <- plotGroups(
ArchRProj = Differentiation_P_1,
groupBy = "Sample",
colorBy = "cellColData",
name = "TSSEnrichment",
plotAs = "ridges",
pal = sample_id_palette
)
plot1
plot2 <- plotGroups(
ArchRProj = Differentiation_P_1,
groupBy = "Sample",
colorBy = "cellColData",
name = "TSSEnrichment",
plotAs = "violin",
alpha = 0.4,
addBoxPlot = TRUE,
pal = sample_id_palette
)
plot2
plot3 <- plotGroups(
ArchRProj = Differentiation_P_1,
groupBy = "Sample",
colorBy = "cellColData",
name = "log10(nFrags)",
plotAs = "ridges",
pal = sample_id_palette
)
plot3
plot4 <- plotGroups(
ArchRProj = Differentiation_P_1,
groupBy = "Sample",
colorBy = "cellColData",
name = "log10(nFrags)",
plotAs = "violin",
alpha = 0.4,
addBoxPlot = TRUE,
pal = sample_id_palette
)
plot4
plot1+plot2+plot3+plot4
plotPDF(plot1+plot2,plot3,plot4, name = "0_QC-Sample-Statistics.pdf", ArchRProj = Differentiation_P_1, addDOC = TRUE, width = 4, height = 4)
```
```{r Plotting Sample Fragment Size Distribution and TSS Enrichment Profiles}
plot5 <- plotFragmentSizes(ArchRProj = Differentiation_P_1,pal = sample_id_palette)
plot5
plot6 <- plotTSSEnrichment(ArchRProj = Differentiation_P_1, pal = sample_id_palette)
plot6
plotPDF(plot5,plot6, name = "0_QC-Sample-FragSizes-TSSProfile.pdf", ArchRProj = Differentiation_P_1, addDOC = TRUE, width = 5, height = 5)
saveArchRProject(ArchRProj = Differentiation_P_1, outputDirectory = "Save-Differentiation_P_1", load = FALSE)
```
<!-- #################################### -->
<!-- End of Save Project 1 -->
<!-- Filter Doublet -->
<!-- #################################### -->
```{r filter doublet function}
#start fromhere now
###RUN THIS FUNCTION before running ARCHR FILTER step in order to avoid posix.ct numeric
filterDoublets <- function(ArchRProj = NULL, cutEnrich = 1, cutScore = -Inf, filterRatio = 1){
fn <- unclass(lsf.str(envir = asNamespace("ArchR"), all = TRUE))
for (i in seq_along(fn)) {
tryCatch({
eval(parse(text = paste0(fn[i], "<-ArchR:::", fn[i])))
}, error = function(x) {
})
}
.validInput(input = ArchRProj, name = "ArchRProj", valid = c("ArchRProj"))
.validInput(input = cutEnrich, name = "cutEnrich", valid = c("numeric"))
.validInput(input = cutScore, name = "cutScore", valid = c("numeric"))
.validInput(input = filterRatio, name = "filterRatio", valid = c("numeric"))
if(any(grepl("filterDoublets", names(ArchRProj@projectSummary)))){
stop("Already ran filterDoublets on ArchRProject! Cannot be re-ran on an ArchRProject!")
}
df <- getCellColData(ArchRProj, c("Sample", "DoubletEnrichment", "DoubletScore"))
splitDF <- split(seq_len(nrow(df)), as.character(df$Sample))
cellsFilter <- lapply(splitDF, function(y){
x <- df[y, ,drop = FALSE]
n <- nrow(x)
x <- x[order(x$DoubletEnrichment, decreasing = TRUE), ]
if(!is.null(cutEnrich)){
x <- x[which(x$DoubletEnrichment >= cutEnrich), ]
}
if(!is.null(cutScore)){
x <- x[which(x$DoubletScore >= cutScore), ]
}
if(nrow(x) > 0){
head(rownames(x), filterRatio * n * (n / 100000))
}else{
NULL
}
}) %>% unlist(use.names=FALSE)
message("Filtering ", length(cellsFilter), " cells from ArchRProject!")
tabRemove <- table(df[cellsFilter,]$Sample)
tabAll <- table(df$Sample)
samples <- unique(df$Sample)
for(i in seq_along(samples)){
if(!is.na(tabRemove[samples[i]])){
message("\t", samples[i], " : ", tabRemove[samples[i]], " of ", tabAll[samples[i]], " (", round(100 * tabRemove[samples[i]] / tabAll[samples[i]], 1),"%)")
}else{
message("\t", samples[i], " : ", 0, " of ", tabAll[samples[i]], " (0%)")
}
}
if(length(cellsFilter) > 0){
ArchRProj@cellColData <- ArchRProj@cellColData[rownames(ArchRProj@cellColData) %ni% cellsFilter,,drop=FALSE]
}
ArchRProj
}
saveArchRProject(ArchRProj = Differentiation_P_1, outputDirectory = "Save-Differentiation_P_1", load = FALSE)
```
```{r filterDoublets}
Differentiation_P_2 <- filterDoublets(Differentiation_P_1, filterRatio = 1)
#Differentiation_P0 <- filterDoublets(Differentiation_P0, filterRatio = 1.0)
#filter more cells from the ArchR Project, you would use a higher filterRatio. To see additional arguments that can be tweaked, try ?filterDoublets.
#<!-- #################################### -->
#<!-- #Dimensionality reduction -->
#<!-- #################################### -->
Differentiation_P_2 <- addIterativeLSI(
ArchRProj = Differentiation_P_2,
useMatrix = "TileMatrix",
name = "IterativeLSI",
iterations = 10,
clusterParams = list( #See Seurat::FindClusters
# resolution = c(0.1, 0.2, 0.3, 0.35, 0.40, 0.45, 0.5, 0.55, 0.6, 0.7, 0.8, 0.9, 1.0),
resolution = c(0.5),
sampleCells = 10000,
n.start = 10),
LSIMethod = 2,
varFeatures = 25000,
dimsToUse = 1:30,
force = TRUE,
logFile = createLogFile("addIterativeLSI")
)
```
```{r batch effect correction}
library(harmony)
Differentiation_P_2 <- addHarmony(
ArchRProj = Differentiation_P_2,
reducedDims = "IterativeLSI",
name = "Harmony",
groupBy = "Sample"
)
```
```{r Clustering using seurat : resolution 0.7}
#library(uwot)
#?addClusters
Differentiation_P_2 <- addClusters(
input = Differentiation_P_2,
reducedDims = "IterativeLSI",
method = "Seurat",
metric = "pca",
name = "Clusters",
resolution = 0.7,
force = TRUE
)
```
```{r final seurat cluster treshold 0.6}
#HERE WE NEED TO CHOOSE THE CORRECT RESOLUTION BASED ON ABOVE
Differentiation_P_2<- addClusters(
input = Differentiation_P_2,
reducedDims = "IterativeLSI",
method = "Seurat",
name = "Clusters",
resolution = c(0.7),
force = TRUE
)
```
```{r Confusion matrix}
Differentiation_P_2
head(Differentiation_P_2$Clusters)
table(Differentiation_P_2$Clusters)
cM <- confusionMatrix(paste0(Differentiation_P_2$Clusters), paste0(Differentiation_P_2$Sample))
cM
```
```{r heatmap}
library(pheatmap)
cM <- cM / Matrix::rowSums(cM)
p <- pheatmap::pheatmap(
mat = as.matrix(cM),
color = paletteContinuous("whitePurple"),
border_color = "grey"
)
p
```
```{r UMAP of Interactive LSI}
###ADD UMAP
library(uwot)
Differentiation_P_2 <- addUMAP(
ArchRProj = Differentiation_P_2,
reducedDims = "IterativeLSI",
name = "UMAP",
nNeighbors = 4,
minDist = 0.8,
metric = "cosine",
force = TRUE
)
#####
ArchRPalettes$custom <- c('1' = '#FF7C70','2' = '#AECDDF')
sample_id_palette <- ArchRPalettes$custom
#####
p1 <- plotEmbedding(ArchRProj = Differentiation_P_2, colorBy = "cellColData", name = "Sample", embedding = "UMAP",pal = sample_id_palette,rastr = FALSE,size=0.3)
p1
######
ArchRPalettes$custom <- c('1' = '#AECDDF','2' = '#FF7C70')
sample_id_palette <- ArchRPalettes$custom
ArchRPalettes$custom <- c('1' = '#2A7185','2' = '#A64027', '3' = '#FBDF72', '4' = '#60824F', '5' = '#9CDFF0', '6' = '#CAB2D6', '7' = '#A5BE6A', '8' = '#A2A475', '9' = '#2F8AC4')
Atac_cluster_palette <- ArchRPalettes$custom
######
p2 <- plotEmbedding(ArchRProj = Differentiation_P_2, colorBy = "cellColData", name = "Clusters", embedding = "UMAP",pal = Atac_cluster_palette,rastr = FALSE,size=0.3)
p2
ggAlignPlots(p1, p2, type = "h")
plotPDF(p1,p2, name = "1_Plot-UMAP1-Sample-Clusters.pdf", ArchRProj = Differentiation_P_1, addDOC = TRUE, width = 11, height = 11,plotList = NULL)
```
```{r umap using COSINE harmony: DEFAULT }
library(umap)
#library(ArchR)
##cosine metric workes well
Differentiation_P_2 <- addUMAP(
ArchRProj = Differentiation_P_2,
reducedDims = "Harmony",
name = "UMAPHarmony",
nNeighbors = 8,
minDist = 0.2,
metric = "cosine",
force = TRUE
)
p3 <- plotEmbedding(ArchRProj = Differentiation_P_2, colorBy = "cellColData", name = "Sample", embedding = "UMAPHarmony",pal = sample_id_palette, size=0.3,rastr=FALSE)
p4 <- plotEmbedding(ArchRProj = Differentiation_P_2, colorBy = "cellColData", name = "Clusters", embedding = "UMAPHarmony",pal = Atac_cluster_palette,size=0.3,rastr=FALSE)
ggAlignPlots(p3, p4, type = "h")
plotPDF(p1,p2,p3,p4, name = "1_Plot-UMAP2Harmony-Sample-Clusters_n4_dist0.8_.pdf", ArchRProj = Differentiation_P_1, addDOC = TRUE, width = 11, height = 11)
```
```{r marker ID for ATAC,include=FALSE}
#geneScoreMatrix
markersGS <- getMarkerFeatures(
ArchRProj = Differentiation_P_2,
useMatrix = "GeneScoreMatrix",
groupBy = "Clusters",
bias = c("TSSEnrichment", "log10(nFrags)"),
testMethod = "wilcoxon"
)
markerListGS <- getMarkers(markersGS, cutOff = "FDR <= 0.01 & Log2FC >= 0.1")
Cluster_marker_list_GS <- write.csv(markerListGS, "1_ATAC_cluster_marker_list_GS.csv", sep = "\t")
markerListGS
```
```{r marker list heatmap,include=FALSE}
markerGenes <- c('SOX2','IRX2','RAX','LIX1','PAX6','HESX1','HES5','LRRC75A','FOXH1','CDH1','TDGF1','DNMT3B','EPCAM','PODXL','ID1','POU5F1','GAL','FOXG1','DPPA4','NR6A1','CD24','LRRC75A','ZIC2','FGF8','DLX6','DLX5','BMP4','GRIA1','SIX6','NKX2-1','NEUROG1','DLL1','ASCL1','NHLH1','NEUROD1','FEZF2','L1CAM','INA','GRIA2','GNRH1','INSM1','ELAVL4','ISL1','TH','DCX','TAGLN3','SCG2','ELAVL3','PCSK2','MAP6','HES6','DLL3','TLX3','NEUROD4','SYP','STMN2','POU2F2','TUBB3','SNAP25','NSG2','GAP43','NEFM','NEFL','RET','NTRK3','RELN','NTRK1','DLX2','DLX1','GAD1','ARX','SOX3','SYT4','SPARCL1','OTX2','LMO1','FABP7','DCT','VEPH1','PRRX1','CDK6','GNG11','HES1','TYMS','PCNA','CDK1','DLX6-AS1','TUBB4B','SELENOP','ID2','CRABP1','APOE','LDHA','FZD5','KRT18','FGF17','ASCL1','REST','GNRH1','GAD1','GAD2','ARX','CDKN1C','TAGLN','NANOG','KIF15','SIX3','PHC1','TUBB4A','NRXN1','CDH7','ROBO1','NCAM1','INA','NEFL','NEFM','TUBB3','TIBB4B','FN1','CDH2','NES','VIM','ACTG1','THY-1','EPHA1','CDH3','PAMBR1','RELN','SEMA4A','SEMA4D','TH')
ArchRPalettes$custom <- c('1' = '#B60A1C','2' = '#AECDDF')
customcolor_heatmaps <- ArchRPalettes$custom
heatmapGS <- markerHeatmap(
seMarker = markersGS,
cutOff = "FDR <= 0.01 & Log2FC >= 0.1",
labelMarkers = markerGenes,
transpose = TRUE,
)
ComplexHeatmap::draw(heatmapGS, heatmap_legend_side = "bot", annotation_legend_side = "bot")
plotPDF(heatmapGS, name = "1_RNAseq-markers_GeneScores-Marker_list-Heatmap-0.1-C", width = 8, height = 6, ArchRProj = Differentiation_P_2, addDOC = TRUE)
p2 <- lapply(p, function(x){
theme_ArchR(baseSize = 6.5) +
theme(plot.margin = unit(c(0, 0, 0, 0), "cm")) +
theme(
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank()
)
})
do.call(cowplot::plot_grid, c(list(ncol = 3),p2))
markerGenes_atac <- c('LOC105369438','LOC101927668','SCGB3A2','L1TD1','LNCPRESS2','RYR2','ELL2','ZFP42','TRG-AS1','LINC00678','RFTN2','ASTN2','PVT1','GPC5','UST-AS1','LNCPRESS2','TPRG1','RAB17','HDAC2-AS2','TRG-AS1','C3','SH2D3A','CLDN9','KLK9','PPP1R14A','SMIM24','KLK8','ARHGAP45','SPINT2','DPRX','VENTX','KLK8','IRX4','ARHGAP45','LINC01962','FAM83H','KLK9','MMP23A','TMEM102','CDX2','MIR2355','PTPRN2','GSE1','LINC01315','CCDC33','MAP6','MSI2','INSM1','MIR7158','EPHA4','FZD8','RBM5-AS1','NSF','MIR770','LOC101927557','MIR2392','PRDM16','PRTG','MAP1B','NSFP1','MSI2','FAM204A','MIR99AHG','CDH2','PCDH9','PLEKHG1','ZFHX4','SLIT2','GLI3','MAP1B','GLIS3','FIGN','FIGN','AATF','LOC101927557','AKAP6','MIR770','MBIP','MSI2','PRTG','NPAS3','PRDM16','SOX6')
heatmapGS <- plotMarkerHeatmap(
seMarker = markersGS,
cutOff = "FDR <= 0.01 & Log2FC >= 0.1",
labelMarkers = markerGenes_atac,
transpose = TRUE,
)
ComplexHeatmap::draw(heatmapGS, heatmap_legend_side = "bot", annotation_legend_side = "bot")
plotPDF(heatmapGS, name = "1_ATACmarkers_GeneScores-Marker_list-Heatmap-0.1-C", width = 8, height = 6, ArchRProj = Differentiation_P_2, addDOC = TRUE)
p2 <- lapply(p, function(x){
theme_ArchR(baseSize = 6.5) +
theme(plot.margin = unit(c(0, 0, 0, 0), "cm")) +
theme(
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank()
)
})
do.call(cowplot::plot_grid, c(list(ncol = 3),p2))
Transcription_Factors <- c('ARX','ASCL1','DLX1','DLX2','DLX5','DLX6','EMX2','FOXD3','FOXG1','FOXH1','HES1','HES5','HES6','HESX1','ID1','ID2','INSM1','IRX2','ISL1','MYC','NANOG','NEUROD1','NEUROD4','NEUROG1','NHLH1','NKX2-1','NR6A1','OTX2','PAX6','POU2F2','POU3F1','POU5F1','PRRX1','RAX','REST','SIX3','SIX6','SOX11','SOX21','SOX2','SOX3','TLX3','ZIC2','LHX2','NFIB','POU3F3','SOX5','ZIC4','ZEB1','NR2F2','ARX','ASCL1','DLX1','DLX2','DLX5','DLX6','EMX2','FOXD3','FOXG1','FOXH1','HES1','HES5','HES6','HESX1','ID1','ID2','INSM1','IRX2','ISL1','MYC','NANOG','NEUROD1','NEUROD4','NEUROG1','NHLH1','NKX2-1','NR6A1','OTX2','PAX6','POU2F2','POU3F1','POU5F1','PRRX1','RAX','REST','SIX3','SIX6','SOX11','SOX21','SOX2','SOX3','TLX3','ZIC2','LHX2','NFIB','POU3F3','SOX5','ZIC4','ZEB1','NR2F2')
heatmapGS <- plotMarkerHeatmap(
seMarker = markersGS,
cutOff = "FDR <= 0.01 & Log2FC >= 0.1",
labelMarkers = Transcription_Factors,
transpose = TRUE,
)
ComplexHeatmap::draw(heatmapGS, heatmap_legend_side = "bot", annotation_legend_side = "bot")
plotPDF(heatmapGS, name = "1_Transcription factors_markers_GeneScores-Marker_list-Heatmap-0.1-C", width = 8, height = 6, ArchRProj = Differentiation_P_2, addDOC = TRUE)
p2 <- lapply(p, function(x){
theme_ArchR(baseSize = 6.5) +
theme(plot.margin = unit(c(0, 0, 0, 0), "cm")) +
theme(
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank()
)
})
do.call(cowplot::plot_grid, c(list(ncol = 3),p2))
markerList_marker_genes <- getMarkers(markersGS, cutOff = "FDR <= 0.01 & Log2FC >= 0.1")
Cluster_marker_list_PM <- write.csv(markerListGS, "1_ATAC_genescores_plot_marker_genes_TopClusterMarkers_.csv", sep = "\t")
```
```{r vizualise marker genes on an embedding,include=FALSE}
#make UMAP plots of marker genes
markerGenes_atac <- c('LOC105369438','LOC101927668','SCGB3A2','L1TD1','LNCPRESS2','RYR2','ELL2','ZFP42','TRG-AS1','LINC00678','RFTN2','ASTN2','PVT1','GPC5','UST-AS1','LNCPRESS2','TPRG1','RAB17','HDAC2-AS2','TRG-AS1','C3','SH2D3A','CLDN9','KLK9','PPP1R14A','SMIM24','KLK8','ARHGAP45','SPINT2','DPRX','VENTX','KLK8','IRX4','ARHGAP45','LINC01962','FAM83H','KLK9','MMP23A','TMEM102','CDX2','MIR2355','PTPRN2','GSE1','LINC01315','CCDC33','MAP6','MSI2','INSM1','MIR7158','EPHA4','FZD8','RBM5-AS1','NSF','MIR770','LOC101927557','MIR2392','PRDM16','PRTG','MAP1B','NSFP1','MSI2','FAM204A','MIR99AHG','CDH2','PCDH9','PLEKHG1','ZFHX4','SLIT2','GLI3','MAP1B','GLIS3','FIGN','FIGN','AATF','LOC101927557','AKAP6','MIR770','MBIP','MSI2','PRTG','NPAS3','PRDM16','SOX6')
#markerGenes_atac_test <- c('LOC105369438','LOC101927668','SCGB3A2','L1TD1','LNCPRESS2')
p <- plotEmbedding(
ArchRProj = Differentiation_P_2,
reducedDims = "IterativeLSI",
colorBy = "GeneScoreMatrix",
name = markerGenes_atac,
embedding = "UMAP",
quantCut = c(0.01, 0.95),
size=0.3,
imputeWeights = NULL
)
p2 <- lapply(p, function(x){
x + guides(color = FALSE, fill = FALSE) +
theme_ArchR(baseSize = 6.5) +
theme(plot.margin = unit(c(0, 0, 0, 0), "cm")) +
theme(
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank()
)
})
do.call(cowplot::plot_grid, c(list(ncol = 3),p2))
plotPDF(plotList = p,
name = "1_Plot-UMAP-Marker-Genes-LSIumap-TopMarkersAND_atac_Markers-Imputation.pdf",
ArchRProj = Differentiation_P_1,
addDOC = TRUE, width = 5, height = 5)
markerGenes <- c('SOX2','IRX2','RAX','LIX1','PAX6','HESX1','HES5','LRRC75A','FOXH1','CDH1','TDGF1','DNMT3B','EPCAM','PODXL','ID1','POU5F1','GAL','FOXG1','DPPA4','NR6A1','CD24','LRRC75A','ZIC2','FGF8','DLX6','DLX5','BMP4','GRIA1','SIX6','NKX2-1','NEUROG1','DLL1','ASCL1','NHLH1','NEUROD1','FEZF2','L1CAM','INA','GRIA2','GNRH1','INSM1','ELAVL4','ISL1','TH','DCX','TAGLN3','SCG2','ELAVL3','PCSK2','MAP6','HES6','DLL3','TLX3','NEUROD4','SYP','STMN2','POU2F2','TUBB3','SNAP25','NSG2','GAP43','NEFM','NEFL','RET','NTRK3','RELN','NTRK1','DLX2','DLX1','GAD1','ARX','SOX3','SYT4','SPARCL1','OTX2','LMO1','FABP7','DCT','VEPH1','PRRX1','CDK6','GNG11','HES1','TYMS','PCNA','CDK1','DLX6-AS1','TUBB4B','SELENOP','ID2','CRABP1','APOE','LDHA','FZD5','KRT18','FGF17','ASCL1','REST','GNRH1','GAD1','GAD2','ARX','CDKN1C','TAGLN','NANOG','KIF15','SIX3','PHC1','TUBB4A','NRXN1','CDH7','ROBO1','NCAM1','INA','NEFL','NEFM','TUBB3','FN1','CDH2','NES','VIM','ACTG1','EPHA1','CDH3','RELN','SEMA4A','SEMA4D','TH')
p <- plotEmbedding(
ArchRProj = Differentiation_P_2,
reducedDims = "IterativeLSI",
colorBy = "GeneScoreMatrix",
name = markerGenes,
embedding = "UMAP",
quantCut = c(0.01, 0.95),
size=0.3,
imputeWeights = NULL
)
p$POU5F1
p2 <- lapply(p, function(x){
x + guides(color = FALSE, fill = FALSE) +
theme_ArchR(baseSize = 6.5) +
theme(plot.margin = unit(c(0, 0, 0, 0), "cm")) +
theme(
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank()
)
})
do.call(cowplot::plot_grid, c(list(ncol = 3),p2))
plotPDF(plotList = p,
name = "1_Plot-UMAP-Marker-Genes-LSIumap-TopMarkersAND_rna_Markers-Imputation.pdf",
ArchRProj = Differentiation_P_2,
addDOC = TRUE, width = 5, height = 5)
Transcription_Factors <- c('ARX','ASCL1','DLX1','DLX2','DLX5','DLX6','EMX2','FOXD3','FOXG1','FOXH1','HES1','HES5','HES6','HESX1','ID1','ID2','INSM1','IRX2','ISL1','MYC','NANOG','NEUROD1','NEUROD4','NEUROG1','NHLH1','NKX2-1','NR6A1','OTX2','PAX6','POU2F2','POU3F1','POU5F1','PRRX1','RAX','REST','SIX3','SIX6','SOX11','SOX21','SOX2','SOX3','TLX3','ZIC2','LHX2','NFIB','POU3F3','SOX5','ZIC4','ZEB1','NR2F2','ARX','ASCL1','DLX1','DLX2','DLX5','DLX6','EMX2','FOXD3','FOXG1','FOXH1','HES1','HES5','HES6','HESX1','ID1','ID2','INSM1','IRX2','ISL1','MYC','NANOG','NEUROD1','NEUROD4','NEUROG1','NHLH1','NKX2-1','NR6A1','OTX2','PAX6','POU2F2','POU3F1','POU5F1','PRRX1','RAX','REST','SIX3','SIX6','SOX11','SOX21','SOX2','SOX3','TLX3','ZIC2','LHX2','NFIB','POU3F3','SOX5','ZIC4','ZEB1','NR2F2')
p <- plotEmbedding(
ArchRProj = Differentiation_P_2,
reducedDims = "IterativeLSI",
colorBy = "GeneScoreMatrix",
name = Transcription_Factors,
embedding = "UMAP",
quantCut = c(0.01, 0.95),
size=0.3,
imputeWeights = NULL
)
p$POU5F1
p2 <- lapply(p, function(x){
x + guides(color = FALSE, fill = FALSE) +
theme_ArchR(baseSize = 6.5) +
theme(plot.margin = unit(c(0, 0, 0, 0), "cm")) +
theme(
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank()
)
})
do.call(cowplot::plot_grid, c(list(ncol = 3),p2))
plotPDF(plotList = p,
name = "1_Plot-UMAP-Marker-Genes-LSIumap-TopMarkersAND_Transcriptionfactors-Imputation.pdf",
ArchRProj = Differentiation_P_2,
addDOC = TRUE, width = 5, height = 5)
```
```{r marker gene computation with ,include=FALSE}
#make UMAP plots of marker genes
Differentiation_P_2 <- addImputeWeights(Differentiation_P_2)
markerGenes_atac <- c('LOC105369438','LOC101927668','SCGB3A2','L1TD1','LNCPRESS2','RYR2','ELL2','ZFP42','TRG-AS1','LINC00678','RFTN2','ASTN2','PVT1','GPC5','UST-AS1','LNCPRESS2','TPRG1','RAB17','HDAC2-AS2','TRG-AS1','C3','SH2D3A','CLDN9','KLK9','PPP1R14A','SMIM24','KLK8','ARHGAP45','SPINT2','DPRX','VENTX','KLK8','IRX4','ARHGAP45','LINC01962','FAM83H','KLK9','MMP23A','TMEM102','CDX2','MIR2355','PTPRN2','GSE1','LINC01315','CCDC33','MAP6','MSI2','INSM1','MIR7158','EPHA4','FZD8','RBM5-AS1','NSF','MIR770','LOC101927557','MIR2392','PRDM16','PRTG','MAP1B','NSFP1','MSI2','FAM204A','MIR99AHG','CDH2','PCDH9','PLEKHG1','ZFHX4','SLIT2','GLI3','MAP1B','GLIS3','FIGN','FIGN','AATF','LOC101927557','AKAP6','MIR770','MBIP','MSI2','PRTG','NPAS3','PRDM16','SOX6')
p <- plotEmbedding(
ArchRProj = Differentiation_P_2,
reducedDims = "IterativeLSI",
colorBy = "GeneScoreMatrix",
name = markerGenes_atac,
embedding = "UMAP",
size=0.3,
imputeWeights = getImputeWeights(Differentiation_P_2)
)
p$SERPINB2
p2 <- lapply(p, function(x){
x + guides(color = FALSE, fill = FALSE) +
theme_ArchR(baseSize = 6.5) +
theme(plot.margin = unit(c(0, 0, 0, 0), "cm")) +
theme(
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank()
)
})
do.call(cowplot::plot_grid, c(list(ncol = 3),p2))
plotPDF(plotList = p,
name = "1_Plot-UMAP-ATAC_Marker-Genes-MAGIC-TopMarkersANDatacMarkers-Imputation.pdf",
ArchRProj = Differentiation_P_2,
addDOC = TRUE, width = 5, height = 5)
markerGenes <- c("POU5F1", "NANOG", "TDGF1", "GAL", "PODXL", "ID1", "MYC", "PAX6", "DNMT3B", "LIN28A", "FOXD3-AS1", "CD24", "EPCAM", "DPPA4", "FZD2", "NR6A1", "SOX21", "ZIC2", "SOX11", "HESX1", "MYLK", "IGFBP5", "FZD5", "FGF8", "SOX2", "RAX", "LIX1", "FEZF2", "POU3F1", "POU3F2", "NKX2-1", "ASCL1", "NEUROG1", "NEUROD1", "ISL1", "BSX", "HMX2", "OTP", "GSX1", "SF1", "GNRH1", "GRIA2", "L1CAM", "NHLH1", "ELAVL4", "SIX3", "SIX6", "OTX2", "TH", "INSM1", "ARX", "DBX1", "DCX", "DLL1", "DLX1", "DLX2", "DLX5", "DLX6", "GAD1", "GRIA1", "INA", "KIF19", "NEFL", "NEFM", "NEUROD4", "NSG2", "POU2F2", "RELN", "SCG2", "SIM1", "SIM2", "SNAP25", "SP9", "SST", "STMN2", "SYP", "SYT4", "TAC1", "TAGLN3", "TLX3", "NTRK1", "NTRK3", "PCSK2", "RET", "DLL3", "GAD2", "GAP43", "REST", "SOX3", "TUBB3", "HES1", "BMP4", "KLF15", "CDK1", "TYMS", "ANLN", "HES6", "LMO1", "ELAVL3", "PRRX1", "OTX2", "PCNA", "SPARCL1", "MAP6", "KRT18", "DDIT4", "PAMR1", "FOXH1", "FABP7", "RBFOX3", "SOX6", "CDH7", "CDH12", "PHC1", "PHC2", "YAF2", "CDH1", "CDH2", "GNG2", "EMX2", "CDK6", "TAGLN", "GNG8", "KLF11", "LHX9", "DCT", "VEPH1", "GNG3", "GNG11", "TRIM71", "SOX3", "KRT19", "NSG1", "JHY")
p <- plotEmbedding(
ArchRProj = Differentiation_P_2,
reducedDims = "IterativeLSI",
colorBy = "GeneScoreMatrix",
name = markerGenes,
embedding = "UMAP",
size=0.3,
imputeWeights = getImputeWeights(Differentiation_P_2)
)
p$SERPINB2
p2 <- lapply(p, function(x){
x + guides(color = FALSE, fill = FALSE) +
theme_ArchR(baseSize = 6.5) +
theme(plot.margin = unit(c(0, 0, 0, 0), "cm")) +
theme(
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank()
)
})
do.call(cowplot::plot_grid, c(list(ncol = 3),p2))
plotPDF(plotList = p,
name = "1_Plot-UMAP-RNA_Marker-Genes-MAGIC-TopMarkersANDSCRNAMarkers-Imputation.pdf",
ArchRProj = Differentiation_P_2,
addDOC = TRUE, width = 5, height = 5)
Transcription_Factors <- c('ARX','ASCL1','DLX1','DLX2','DLX5','DLX6','EMX2','FOXD3','FOXG1','FOXH1','HES1','HES5','HES6','HESX1','ID1','ID2','INSM1','IRX2','ISL1','MYC','NANOG','NEUROD1','NEUROD4','NEUROG1','NHLH1','NKX2-1','NR6A1','OTX2','PAX6','POU2F2','POU3F1','POU5F1','PRRX1','RAX','REST','SIX3','SIX6','SOX11','SOX21','SOX2','SOX3','TLX3','ZIC2','LHX2','NFIB','POU3F3','SOX5','ZIC4','ZEB1','NR2F2','ARX','ASCL1','DLX1','DLX2','DLX5','DLX6','EMX2','FOXD3','FOXG1','FOXH1','HES1','HES5','HES6','HESX1','ID1','ID2','INSM1','IRX2','ISL1','MYC','NANOG','NEUROD1','NEUROD4','NEUROG1','NHLH1','NKX2-1','NR6A1','OTX2','PAX6','POU2F2','POU3F1','POU5F1','PRRX1','RAX','REST','SIX3','SIX6','SOX11','SOX21','SOX2','SOX3','TLX3','ZIC2','LHX2','NFIB','POU3F3','SOX5','ZIC4','ZEB1','NR2F2')
p <- plotEmbedding(
ArchRProj = Differentiation_P_2,
reducedDims = "IterativeLSI",
colorBy = "GeneScoreMatrix",
name = Transcription_Factors,
embedding = "UMAP",
size=0.3,
imputeWeights = getImputeWeights(Differentiation_P_2)
)
p$SERPINB2
p2 <- lapply(p, function(x){
x + guides(color = FALSE, fill = FALSE) +
theme_ArchR(baseSize = 6.5) +
theme(plot.margin = unit(c(0, 0, 0, 0), "cm")) +
theme(
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank()
)
})
do.call(cowplot::plot_grid, c(list(ncol = 3),p2))
plotPDF(plotList = p,
name = "1_Plot-UMAP-RNA_Marker-Genes-MAGIC-TopMarkersANDTRANSCRIOTIONFACTORS-Imputation.pdf",
ArchRProj = Differentiation_P_2,
addDOC = TRUE, width = 5, height = 5)
```
```{r Track blotting with ArchR Browser,include=FALSE}
ArchRPalettes$custom <- c('C1' = '#2A7185','C2' = '#A64027', 'C3' = '#FBDF72', 'C4' = '#60824F', 'C5' = '#9CDFF0', 'C6' = '#CAB2D6', 'C7' = '#A5BE6A', 'C8' = '#A2A475', 'C9' = '#2F8AC4')
Atac_cluster_palette <- ArchRPalettes$custom
markerGenes <- c('LOC105369438','LOC101927668','SCGB3A2','L1TD1','LNCPRESS2','RYR2','ELL2','ZFP42','TRG-AS1','LINC00678','RFTN2','ASTN2','PVT1','GPC5','UST-AS1','LNCPRESS2','TPRG1','RAB17','HDAC2-AS2','TRG-AS1','C3','SH2D3A','CLDN9','KLK9','PPP1R14A','SMIM24','KLK8','ARHGAP45','SPINT2','DPRX','VENTX','KLK8','IRX4','ARHGAP45','LINC01962','FAM83H','KLK9','MMP23A','TMEM102','CDX2','MIR2355','PTPRN2','GSE1','LINC01315','CCDC33','MAP6','MSI2','INSM1','MIR7158','EPHA4','FZD8','RBM5-AS1','NSF','MIR770','LOC101927557','MIR2392','PRDM16','PRTG','MAP1B','NSFP1','MSI2','FAM204A','MIR99AHG','CDH2','PCDH9','PLEKHG1','ZFHX4','SLIT2','GLI3','MAP1B','GLIS3','FIGN','FIGN','AATF','LOC101927557','AKAP6','MIR770','MBIP','MSI2','PRTG','NPAS3','PRDM16','SOX6')
p <- plotBrowserTrack(
ArchRProj = Differentiation_P_2,
groupBy = "Clusters",
geneSymbol = markerGenes,
upstream = 50000,
downstream = 50000,
pal=Atac_cluster_palette
)
p
grid::grid.newpage()
plotPDF(plotList = p,
name = "1_Plot-Tracks-Marker-Genes-Cluster-specific-markers-from-ATAC.pdf",
ArchRProj = Differentiation_P_2,
addDOC = TRUE, width = 5, height = 5)
markerGenes <- c('SOX2','IRX2','RAX','LIX1','PAX6','HESX1','HES5','LRRC75A','FOXH1','CDH1','TDGF1','DNMT3B','EPCAM','PODXL','ID1','POU5F1','GAL','FOXG1','DPPA4','NR6A1','CD24','LRRC75A','ZIC2','FGF8','DLX6','DLX5','BMP4','GRIA1','SIX6','NKX2-1','NEUROG1','DLL1','ASCL1','NHLH1','NEUROD1','FEZF2','L1CAM','INA','GRIA2','GNRH1','INSM1','ELAVL4','ISL1','TH','DCX','TAGLN3','SCG2','ELAVL3','PCSK2','MAP6','HES6','DLL3','TLX3','NEUROD4','SYP','STMN2','POU2F2','TUBB3','SNAP25','NSG2','GAP43','NEFM','NEFL','RET','NTRK3','RELN','NTRK1','DLX2','DLX1','GAD1','ARX','SOX3','SYT4','SPARCL1','OTX2','LMO1','FABP7','DCT','VEPH1','PRRX1','CDK6','GNG11','HES1','TYMS','PCNA','CDK1','DLX6-AS1','TUBB4B','SELENOP','ID2','CRABP1','APOE','LDHA','FZD5','KRT18','FGF17','ASCL1','REST','GNRH1','GAD1','GAD2','ARX','CDKN1C','TAGLN','NANOG','KIF15','SIX3','PHC1','TUBB4A','NRXN1','CDH7','ROBO1','NCAM1','INA','NEFL','NEFM','TUBB3','FN1','CDH2','NES','VIM','ACTG1','EPHA1','CDH3','RELN','SEMA4A','SEMA4D','TH')
p <- plotBrowserTrack(
ArchRProj = Differentiation_P_2,
groupBy = "Clusters",
geneSymbol = markerGenes,
upstream = 50000,
downstream = 50000,
pal=Atac_cluster_palette
)
grid::grid.newpage()
plotPDF(plotList = p,
name = "1_Plot-Tracks-Marker-Genes-Cluster-specific-markers-from-scRNA.pdf",
ArchRProj = Differentiation_P_1,
addDOC = TRUE, width = 5, height = 5)
Transcription_Factors <- c('ARX','ASCL1','DLX1','DLX2','DLX5','DLX6','EMX2','FOXD3','FOXG1','FOXH1','HES1','HES5','HES6','HESX1','ID1','ID2','INSM1','IRX2','ISL1','MYC','NANOG','NEUROD1','NEUROD4','NEUROG1','NHLH1','NKX2-1','NR6A1','OTX2','PAX6','POU2F2','POU3F1','POU5F1','PRRX1','RAX','REST','SIX3','SIX6','SOX11','SOX21','SOX2','SOX3','TLX3','ZIC2','LHX2','NFIB','POU3F3','SOX5','ZIC4','ZEB1','NR2F2','ARX','ASCL1','DLX1','DLX2','DLX5','DLX6','EMX2','FOXD3','FOXG1','FOXH1','HES1','HES5','HES6','HESX1','ID1','ID2','INSM1','IRX2','ISL1','MYC','NANOG','NEUROD1','NEUROD4','NEUROG1','NHLH1','NKX2-1','NR6A1','OTX2','PAX6','POU2F2','POU3F1','POU5F1','PRRX1','RAX','REST','SIX3','SIX6','SOX11','SOX21','SOX2','SOX3','TLX3','ZIC2','LHX2','NFIB','POU3F3','SOX5','ZIC4','ZEB1','NR2F2')
p <- plotBrowserTrack(
ArchRProj = Differentiation_P_2,
groupBy = "Clusters",
geneSymbol = Transcription_Factors,
upstream = 50000,
downstream = 50000,
pal=Atac_cluster_palette
)
grid::grid.newpage()
plotPDF(plotList = p,
name = "1_Plot-Tracks-Marker-Genes-Cluster-specific-markers_Transcription_factors.pdf",
ArchRProj = Differentiation_P_2,
addDOC = TRUE, width = 5, height = 5)
```
```{r Browser and shiny}
#peak matrix file
#ArchRBrowser(Differentiation_P_3)
saveRDS(Differentiation_P_2, file = "ARCHR_PART1_FINAL_before_integration_RNA.rds")
```
```{r RNASEQ integration unconstrained}
##ARIAL NOT IN FONT DATABASE- CHECK BEFORE RUNNING THIS STEP
#Differentiation_P_3 <- "ARCHR_PART1_FINAL_before_integration_RNA.rds"
#library(ArchR)
# load RDS FILE OF THE scRNA SEQ
set.seed(1)
Sys.setenv('R_MAX_VSIZE'=1200000)
addArchRThreads(threads=16)
library(SingleCellExperiment)
seRNA <- readRDS("~/Dropbox (UiO)/Paracetamol_shared/SINGLE_CELL_ANALYSIS/AGGREGATED_ANALYSIS/DIFFERENTIATION/FINAL_MERGED_ANALYSIS_D0_D20_res0.5/COHERENT_ALL_EndofPipe_Final_rnaobjects.qcFilt.rds")
seRNA
# Assign new IDs based on scCatch annotation results - Using Seurat Markers
#renumbered clusters from 0to13
library(ggplot2)
library(tidyverse)
library(ggrepel)
library(ggthemes)
library(dplyr)
DimPlot(seRNA, reduction = "umap", label = TRUE,group.by = "ident", pt.size = 0.3,cols = c('5' = '#8175AA', '1' = '#C3CE3D', '7' = '#FFC966', '2' = '#023858','10' = '#8CC2CA','3' = '#DAB6AF', '4' = '#CCCCCC', '9' = '#59A14F', '6' = '#B15928', '11' = '#C0D6E4', '8' = '#FFD700','12' = '#F28E2B','13' = '#A3ACB9'))+theme(text=element_text(size=12))
dev.copy(pdf,paste0("up_13.FINAL_original_LABELS_FILT.UMAP_final.pdf"), width=8, height=6, paper='special')
dev.off()
###NEW FROM SLACK ## RE SUGGESTED feb62021
fil.cluster.ids <- c("R7","R1","R9","R0","R12","R2","R3","R11","R8","R13","R10","R14","R15")
seRNA$original_seurat_clusters
Idents(seRNA) <- "seurat_clusters"
names(fil.cluster.ids) <- levels(seRNA)
seRNA <- RenameIdents(seRNA, fil.cluster.ids)
DimPlot(seRNA, reduction = "umap", label = TRUE,group.by = "ident", pt.size = 0.5,cols = c('R7' = '#8175AA', 'R1' = '#C3CE3D', 'R9' = '#FFC966', 'R0' = '#CCCCCC','R12' = '#8CC2CA','R2' = '#DAB6AF', 'R3' = '#023858', 'R11' = '#59A14F', 'R8' = '#B15928', 'R13' = '#C0D6E4', 'R10' = '#FFD700','R14' = '#F28E2B','R15' = '#A3ACB9'))
dev.copy(pdf,paste0(currentjob,"coherent_13.NEW_RENAME_NUMBERS_FILT.UMAP_final.pdf"), width=8, height=6, paper='special')
dev.off()
seRNA_1 = Seurat::as.SingleCellExperiment(seRNA)
#table(colData(seRNA)$BioClassification)
seRNA_1$ident
#The metadata contains a column called Seurat_clusters or SNN_res_0.5 which contains the cell type
colnames(colData(seRNA_1))
table(colData(seRNA_1)$ident)
#integrate scATAC-seq with scRNA-seq
Differentiation_P_2<- addGeneIntegrationMatrix(
ArchRProj = Differentiation_P_2,
useMatrix = "GeneScoreMatrix",
matrixName = "GeneIntegrationMatrix",
reducedDims = "IterativeLSI",
seRNA = seRNA_1,
addToArrow = FALSE,
groupRNA = "ident",
nameCell = "predictedCell_Un",
nameGroup = "predictedGroup_Un",
nameScore = "predictedScore_Un"
)
rm(seRNA)
```
```{r Confusion matrix for assignment of clusters }
###Confusion matrix for assigning clusters_CONSTRAINED INTEGRATION
Sys.setenv('R_MAX_VSIZE'=1200000)
set.seed(1)
cM <- as.matrix(confusionMatrix(Differentiation_P_2$Clusters, Differentiation_P_2$predictedGroup_Un))
preClust1 <- colnames(cM)[apply(cM, 1 , which.max)]
preClust1
cbind(preClust1, rownames(cM))
#Assignments
unique(unique(Differentiation_P_2$predictedGroup_Un))
preClust1
```
```{r string-based representation of clusters for downstream integration}
#we will create a confusionMatrix that looks at the intersection of Clusters and predictedGroup_Un which contains the cell types as identified by scRNA-seq.
c1 <- paste0(c(paste0("R0"),"R2","R3"), collapse="|")
c1
cNon1<- paste0(c(paste0("R9"),"R7","R12"), collapse="|")
cNon1
```
```{r Assign scATAC to these categories for downstream integration}
#
clust1 <- rownames(cM)[grep(c1, preClust1)]
clust1
clustNon1<- rownames(cM)[grep(cNon1, preClust1)]
clustNon1
#RNA get cells in these categories
rna1 <- colnames(seRNA_1)[grep(c1, colData(seRNA_1)$ident)]
head(rna1)
rnaNon1 <- colnames(seRNA_1)[grep(cNon1, colData(seRNA_1)$ident)]
head(rnaNon1)
```
```{r marker ID for ATAC}
Sys.setenv('R_MAX_VSIZE'=1200000)
set.seed(1)
groupList <- SimpleList(
cluster1 = SimpleList(
ATAC = Differentiation_P_2$cellNames[Differentiation_P_2$Clusters %in% clust1],
RNA = rna1
),
NonFN1 = SimpleList(
ATAC = Differentiation_P_2$cellNames[Differentiation_P_2$Clusters %in% clustNon1],
RNA = rnaNon1
)
)
Differentiation_P_2 <- addGeneIntegrationMatrix(
ArchRProj = Differentiation_P_2,
useMatrix = "GeneScoreMatrix",
matrixName = "GeneIntegrationMatrix",
reducedDims = "IterativeLSI",
seRNA = seRNA_1,
addToArrow = FALSE,
groupList = groupList,
groupRNA = "ident",
nameCell = "predictedCell_Co",
nameGroup = "predictedGroup_Co",
nameScore = "predictedScore_Co"
)
```
```{r Plot UMAP of Unconstrained p1 and constrained p2 integration ident}
ArchRPalettes$custom <-c( 'R0' = '#CCCCCC','R1'= '#C3CE3D', 'R2'='#DAB6AF','R3' = '#023858','R7' = '#8175AA','R8' ='#B15928','R9' = '#FFC966','R10' = '#FFD700','R11' = '#59A14F','R12' = '#8CC2CA','R13' = '#C0D6E4','R14' = '#F28E2B','R15' = '#A3ACB9')
unconstrained_palette <- ArchRPalettes$custom
p1 <- plotEmbedding(
Differentiation_P_2,
embedding = "UMAP",
reducedDims = "IterativeLSI",
colorBy = "cellColData",
name = "predictedGroup_Un",
pal = unconstrained_palette,
rastr = FALSE,
size=0.3,
)
p1
ArchRPalettes$custom <-c('R0' = '#CCCCCC','R2'= '#DAB6AF', 'R3'='#023858','R7' = '#8175AA','R9' = '#FFC966','R12' ='#8CC2CA')
constrained_palette <- ArchRPalettes$custom
p2 <- plotEmbedding(
Differentiation_P_2,
reducedDims = "IterativeLSI",
colorBy = "cellColData",
name = "predictedGroup_Co",
pal = constrained_palette,
rastr = FALSE,
size=0.3
)
p2
plotPDF(p1,p2, name = "2_Plot-UMAP-unconstrained-constrained-RNA-Integration_pal_MyChoice_Co3_rmNonSignSignatures.pdf", ArchRProj = Differentiation_P_2, addDOC = TRUE, width = 5, height = 5)