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plot-breast-features.Rmd
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---
title: "Cube FFPE vs FF pilot"
output: html_notebook
---
```{r}
# pacman is a "package manager" that defines the p_load function.
# p_load automatically installs a library if it is not already installed.
# This often, but doesn't always, works.
suppressPackageStartupMessages(library(pacman))
suppressPackageStartupMessages(library(Seurat))
suppressPackageStartupMessages(p_load(hdf5r))
suppressPackageStartupMessages(p_load(ggplot2))
suppressPackageStartupMessages(p_load(patchwork))
suppressPackageStartupMessages(p_load(dplyr))
suppressPackageStartupMessages(p_load(plyr))
suppressPackageStartupMessages(p_load(cowplot))
# suppressPackageStartupMessages(p_load(scales))
suppressPackageStartupMessages(p_load(reshape2))
suppressPackageStartupMessages(p_load(ggbeeswarm))
# suppressPackageStartupMessages(library(devtools))
# install_github("jokergoo/ComplexHeatmap")
suppressPackageStartupMessages(p_load(ComplexHeatmap))
# devtools::install_github("sjmgarnier/viridisLite")
suppressPackageStartupMessages(library(viridisLite))
suppressPackageStartupMessages(library(dendextend))
# suppressPackageStartupMessages(p_load(CCA))
# suppressPackageStartupMessages(p_load("sRDA"))
suppressPackageStartupMessages(p_load(PMA))
suppressPackageStartupMessages(p_load(ggpubr))
suppressPackageStartupMessages(p_load(rstatix))
suppressPackageStartupMessages(p_load(gtools))
suppressPackageStartupMessages(p_load(DropletUtils)) # for downsampling count matrices
suppressPackageStartupMessages(p_load(foreach))
suppressPackageStartupMessages(p_load(parallel))
suppressPackageStartupMessages(p_load(scran)) # for deconvolution-based normalization
suppressPackageStartupMessages(p_load(scuttle)) # for deconvolution-based normalization
suppressPackageStartupMessages(p_load(rjson))
##if (!require(devtools)) install.packages("devtools")
##devtools::install_github("gaospecial/ggVennDiagram")
suppressPackageStartupMessages(p_load(ggVennDiagram))
suppressPackageStartupMessages(p_load(gridExtra))
suppressPackageStartupMessages(p_load(biomaRt))
suppressPackageStartupMessages(p_load(gplots))
suppressPackageStartupMessages(p_load(RColorBrewer))
suppressPackageStartupMessages(p_load(harmony))
#devtools::install_github("dmcable/spacexr", build_vignettes = FALSE)
suppressPackageStartupMessages(p_load(spacexr))
suppressPackageStartupMessages(p_load(Matrix))
suppressPackageStartupMessages(p_load(doParallel))
suppressPackageStartupMessages(p_load(sf))
suppressPackageStartupMessages(p_load(spdep))
#if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("RITAN")
suppressPackageStartupMessages(p_load(RITAN))
suppressPackageStartupMessages(p_load(RITANdata))
#if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("org.Hs.eg.db")
suppressPackageStartupMessages(p_load(org.Hs.eg.db))
#if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("GOSemSim")
suppressPackageStartupMessages(p_load(GOSemSim))
# install.packages("BiocManager")
#BiocManager::install("clusterProfiler")
suppressPackageStartupMessages(p_load(clusterProfiler))
suppressPackageStartupMessages(p_load(data.table))
suppressPackageStartupMessages(p_load(rhdf5))
suppressPackageStartupMessages(p_load(devtools))
suppressPackageStartupMessages(p_load(Rsamtools))
suppressPackageStartupMessages(p_load(edgeR))
```
Include util functions
```{r}
url <- "https://raw.githubusercontent.com/TheJacksonLaboratory/r_hne_image_utils/main/hovernet-utils.R"
source_url(url) # for extract.cell.info.from.hovernet.output, extract.hovernet.cell.info
source("utils.R")
source("plotting-utils.R")
```
Setup a parallel execution environment.
```{r}
num.cores <- setup.parallel.environment()
```
Configure variables that point to location of images and directories where analyses and plots will be stored.
```{r}
#dataset.labels <- list('SC2200108_DT-21-4142-BR6526' = 'FFPE', 'SC2200109_DT-21-4142-BR6526' = 'FFPE', 'SC2200110_DT-21-4141-BR7212' = 'FFPE', 'SC2200111_DT-21-4141-BR7212' = 'FFPE', 'SC2200104_BR6526' = 'Frozen')
# long.dataset.names <- c('SC2200108_DT-21-4142-BR6526', 'SC2200109_DT-21-4142-BR6526', 'SC2200110_DT-21-4141-BR7212', 'SC2200111_DT-21-4141-BR7212', 'SC2200104_BR6526')
# long.dataset.names <- c('SC2200104_BR6526')
# long.dataset.names <- names(dataset.labels)
dataset.secondary.labels <- NULL
data_types <- c('pdac','endometrium','gbm','breastcancer','lungcancer','peritoneum', 'endometrium_and_peritoneum')
#for (curr_datatypes in data_types){
#}
# curr_datatypes=data_types[1]
curr_datatypes='pdac'
if (curr_datatypes==data_types[1]){
base_dir<-'/projects/compsci/jgeorge/USERS/somara/pdac/'
output_base_dir <- '/projects/compsci/jgeorge/whitebr/cancer-cube/pdac/'
biological.replicates <- c("BR6526", "BR7212")
long.dataset.names <- list('BR6526-108' = 'SC2200108BR6526', 'BR6526-109' = 'SC2200109BR6526', 'BR7212-110' = 'SC2200110BR7212', 'BR7212-111' = 'SC2200111BR7212', 'BR6526-104' = 'SC2200104_BR6526')
dataset.labels <- list('BR6526-108' = 'FFPE', 'BR6526-109' = 'FFPE', 'BR7212-110' = 'FFPE', 'BR7212-111' = 'FFPE', 'BR6526-104' = 'Frozen')
species_sample <- replicate(length(dataset.labels), "Mouse")
} else if (curr_datatypes==data_types[2]){
base_dir <- '/projects/compsci/jgeorge/USERS/somara/endometrium/'
output_base_dir <- '/projects/compsci/jgeorge/whitebr/cancer-cube/endometrium/'
dir.create(output_base_dir, recursive=TRUE)
biological.replicates <- c("EuE-31")
long.dataset.names <- list('EuE-31-236' = 'SC2200236_EuE-31', 'EuE-31-235' = 'SC2200235_EuE-31', 'EuE-31-165' = 'SC2200165_EuE-31', 'EuE-31-164' = 'SC2200164_EuE-31')
dataset.labels <- list('EuE-31-164' = 'Frozen', 'EuE-31-165' = 'Frozen', 'EuE-31-235' = 'FFPE', 'EuE-31-236' = 'FFPE')
species_sample <- replicate(length(dataset.labels), "Human")
} else if (curr_datatypes==data_types[3]) {
base_dir <- '/projects/compsci/USERS/somara/gbm/'
biological.replicates <- c("UC-01","UC-02")
long.dataset.names <- list('UC-02-242' = 'SC2200242_UC-02', 'UC-02-241' = 'SC2200241_UC-02', 'UC-01-240' = 'SC2200240_UC-01', 'UC-02-171' = 'SC2200171_UC-02', 'UC-02-170' = 'SC2200170_UC-02', 'UC-01-169' = 'SC2200169_UC-01', 'UC-01-168' = 'SC2200168_UC-01')
dataset.labels <- list('UC-02-242' = 'FFPE', 'UC-02-241' = 'FFPE', 'UC-01-240' = 'FFPE', 'UC-02-171' = 'Frozen', 'UC-02-170' = 'Frozen','UC-01-169' = 'Frozen', 'UC-01-168' = 'Frozen')
species_sample <- replicate(length(dataset.labels), "Human")
} else if (curr_datatypes==data_types[4]) {
base_dir <- '/projects/compsci/jgeorge/USERS/somara/breast_cancer/'
output_base_dir <- '/projects/compsci/jgeorge/whitebr/cancer-cube/breast/'
biological.replicates <- c("T1O")
long.dataset.names <- list('T1O-244' = 'SC2200244_T1O', 'T1O-243' = 'SC2200243_T1O', 'T1O-199' = 'SC2200199_T1O', 'T1O-198' = 'SC2200198_T1O')
dataset.labels <- list('T1O-244' = 'FFPE', 'T1O-243' = 'FFPE', 'T1O-199' = 'Frozen', 'T1O-198' = 'Frozen')
species_sample <- replicate(length(dataset.labels), "Mouse")
} else if (curr_datatypes==data_types[5]) {
base_dir <- '/projects/compsci/USERS/somara/lung_cancer/'
biological.replicates <- c("LM2O")
long.dataset.names <- list('LM2O-246' = 'SC2200246_LM2O', 'LM2O-245' = 'SC2200245_LM2O', 'LM2O-201' = 'SC2200201_LM2O', 'LM2O-200' = 'SC2200200_LM2O')
dataset.labels <- list('LM2O-246' ='FFPE', 'LM2O-245' ='FFPE', 'LM2O-201' = 'Frozen', 'LM2O-200' = 'Frozen')
species_sample <- replicate(length(dataset.labels), "Mouse")
} else if (curr_datatypes==data_types[6]){
base_dir <- '/projects/compsci/USERS/somara/peritoneum/'
biological.replicates <- c('EcP-33')
long.dataset.names <- list('EcP-33-238' = 'SC2200238_EcP-33', 'EcP-33-167' = 'SC2200167_EcP-33', 'EcP-33-166' = 'SC2200166_EcP-33')
dataset.labels <- list('EcP-33-167' = 'Frozen', 'EcP-33-166' = 'Frozen', 'EcP-33-238' = 'FFPE')
species_sample <- replicate(length(dataset.labels), "Human")
} else if(curr_datatypes=='endometrium_and_peritoneum') {
base_dir <- '/projects/compsci/jgeorge/whitebr/cancer-cube/data/endometrium_and_peritoneum/'
output_base_dir <- '/projects/compsci/jgeorge/whitebr/cancer-cube/endometrium/'
dir.create(output_base_dir, recursive=TRUE)
biological.replicates <- c("EuE-31", "EcP-33")
long.dataset.names <- list('EuE-31-236' = 'SC2200236_EuE-31', 'EuE-31-235' = 'SC2200235_EuE-31', 'EuE-31-165' = 'SC2200165_EuE-31', 'EuE-31-164' = 'SC2200164_EuE-31', 'EcP-33-238' = 'SC2200238_EcP-33', 'EcP-33-167' = 'SC2200167_EcP-33', 'EcP-33-166' = 'SC2200166_EcP-33')
dataset.labels <- list('EuE-31-164' = 'Frozen', 'EuE-31-165' = 'Frozen', 'EuE-31-235' = 'FFPE', 'EuE-31-236' = 'FFPE', 'EcP-33-167' = 'Frozen', 'EcP-33-166' = 'Frozen', 'EcP-33-238' = 'FFPE')
dataset.secondary.labels <- list('EuE-31-164' = 'EuE', 'EuE-31-165' = 'EuE', 'EuE-31-235' = 'EuE', 'EuE-31-236' = 'EuE', 'EcP-33-167' = 'EcP', 'EcP-33-166' = 'EcP', 'EcP-33-238' = 'EcP')
species_sample <- replicate(length(dataset.labels), "Human")
}
# Write csv file for automatic ST pipeline
# write.automated.csv.file(base_dir, long.dataset.names, species_sample)
dataset.metadata.df <- data.frame(sample.name = names(dataset.labels), type = unlist(unname(dataset.labels)))
datasets <- names(long.dataset.names)
names(datasets) <- datasets
```
```{r}
datasets <- datasets[order(dataset.metadata.df$sample.name, dataset.metadata.df$type)]
dataset.labels <- dataset.labels[datasets]
long.dataset.names <- long.dataset.names[datasets]
ffpe.datasets <- datasets[dataset.labels == "FFPE"]
frozen.datasets <- datasets[dataset.labels == "Frozen"]
color_list<-list('FFPE'="#d8b365" , 'Frozen'='#5ab4ac')
# Directory holding the raw data
spaceranger_dirs <- paste0(base_dir, long.dataset.names, "/spaceranger/")
names(spaceranger_dirs) <- datasets
# Define and create output results and plots directories
# base_dir_antonis<-'/projects/compsci/USERS/somara/cancer-cude/qc-pilot/'
analysis_dir <- paste0(output_base_dir, '/analysis/')
dir.create(analysis_dir, showWarnings = FALSE, recursive = TRUE)
plots_dir <- paste0(output_base_dir, '/plots/')
dir.create(plots_dir, showWarnings = FALSE, recursive = TRUE)
# Prefix for all output files (results and plots)
analysis_file_prefix <- paste0(curr_datatypes,"-pilot-")
```
```{r}
# Read in the filtered and unfiltered matrices and append the tissue positions information to the metadata:
filtered.objs <- create.visium.seurat.objects(spaceranger_dirs, filter.spots = TRUE)
unfiltered.objs <- create.visium.seurat.objects(spaceranger_dirs, filter.spots = FALSE)
```
```{r}
nms <- names(unfiltered.objs)
for(nm in names(unfiltered.objs)) {
g <- plot.hne(unfiltered.objs[[nm]], keep.invisible.legend = TRUE)
png(paste0(plots_dir, nm, "-hne.png"))
print(g)
d <- dev.off()
g <- plot.spatial(unfiltered.objs[[nm]], "nFeature_Spatial", "# Genes", rescale.legend = FALSE)
png(paste0(plots_dir, nm, "-genes.png"))
print(g)
d <- dev.off()
}
```
```{r}
stop("stop")
```
```{r}
# Add cpm data
filtered.objs <-
llply(filtered.objs,
.fun = function(obj) {
cpm.mat <- cpm(GetAssayData(obj, slot="counts", assay="Spatial"))
obj[["cpm"]] <- CreateAssayObject(counts = cpm.mat)
obj
})
```
```{r}
g <- plot.biotypes.across.samples(filtered.objs, sample.labels = dataset.labels)
png(paste0(plots_dir,curr_datatypes, "-read-proportion-biotype-vs-sample.png"),height=1*480, width=2*480)
print(g)
d <- dev.off()
```
```{r}
nms <- names(unfiltered.objs)
names(nms) <- nms
align.metrics <-
llply(nms, .parallel = FALSE,
.fun = function(nm) {
print(nm)
alignment.metric.file <- paste0(analysis_dir, "/", nm, "-alignment-metrics.csv")
if(!file.exists(alignment.metric.file)) {
bam.file <- paste0(spaceranger_dirs[[nm]], "/possorted_genome_bam.bam")
df <- get.per.spot.alignment.metrics(bam.file)
write.table(file=alignment.metric.file, df, row.names=FALSE, col.names=TRUE, sep=",", quote=FALSE)
}
as.data.frame(fread(alignment.metric.file))
})
```
```{r}
# Get the per-spot saturation metrics
nms <- names(unfiltered.objs)
names(nms) <- nms
filtered.saturation.metrics <-
llply(nms,
.fun = function(nm) {
print(nm)
mol.info.file <- paste0(spaceranger_dirs[[nm]], "molecule_info.h5")
get.per.spot.saturation.stats(mol.info.file, filter = TRUE)
})
unfiltered.saturation.metrics <-
llply(nms,
.fun = function(nm) {
print(nm)
mol.info.file <- paste0(spaceranger_dirs[[nm]], "molecule_info.h5")
get.per.spot.saturation.stats(mol.info.file, filter = FALSE)
})
```
```{r}
# Add saturation and alignment metrics to unfiltered and filtered Seurat objects
nms <- names(unfiltered.objs)
names(nms) <- nms
for(nm in nms) {
align.meta <- align.metrics[[nm]]
# Spot barcode is in CB column. Make that the row name and remove CB.
align.meta <- align.meta[!is.na(align.meta$CB),]
rownames(align.meta) <- align.meta$CB
align.meta <- align.meta[, !(colnames(align.meta) == "CB")]
seq.meta <- filtered.saturation.metrics[[nm]]
# Add "-1" postfix to barcode
rownames(seq.meta) <- paste0(rownames(seq.meta), "-1")
m <- merge(align.meta, seq.meta, by = "row.names", all = TRUE)
m[is.na(m)] <- 0
rownames(m) <- m$Row.names
m <- m[, !(colnames(m) == "Row.names")]
m$map.frac <- m$tot.mapped.reads / m$tot.reads
filtered.objs[[nm]] <- add.metadata.to.seurat.obj(filtered.objs[[nm]], m)
seq.meta <- unfiltered.saturation.metrics[[nm]]
# Add "-1" postfix to barcode
rownames(seq.meta) <- paste0(rownames(seq.meta), "-1")
m <- merge(align.meta, seq.meta, by = "row.names", all = TRUE)
m[is.na(m)] <- 0
rownames(m) <- m$Row.names
m <- m[, !(colnames(m) == "Row.names")]
m$map.frac <- m$tot.mapped.reads / m$tot.reads
unfiltered.objs[[nm]] <- add.metadata.to.seurat.obj(unfiltered.objs[[nm]], m)
}
```
```{r}
all.meta <- ldply(filtered.objs, .fun = function(obj) obj[[]])
sample.df <- data.frame(sample = names(dataset.labels), label = as.character(dataset.labels))
all.meta <- merge(all.meta, sample.df, by.x = c("orig.ident"), by.y = c("sample"))
if(!is.null(dataset.secondary.labels)) {
sec.df <- data.frame(sample = names(dataset.secondary.labels), sec.label = as.character(dataset.secondary.labels))
all.meta <- merge(all.meta, sec.df, by.x = c("orig.ident"), by.y = c("sample"))
}
all.meta$orig.ident <- factor(all.meta$orig.ident)
# plot(all.meta$nFeature_Spatial, all.meta$num.nonzero.genes)
```
```{r}
all.unfiltered.meta <- ldply(unfiltered.objs, .fun = function(obj) obj[[]])
sample.df <- data.frame(sample = names(dataset.labels), label = as.character(dataset.labels))
all.unfiltered.meta <- merge(all.unfiltered.meta, sample.df, by.x = c("orig.ident"), by.y = c("sample"))
if(!is.null(dataset.secondary.labels)) {
sec.df <- data.frame(sample = names(dataset.secondary.labels), sec.label = as.character(dataset.secondary.labels))
all.unfiltered.meta <- merge(all.unfiltered.meta, sec.df, by.x = c("orig.ident"), by.y = c("sample"))
}
all.unfiltered.meta$orig.ident <- factor(all.unfiltered.meta$orig.ident)
all.unfiltered.meta$spot_type <- factor(all.unfiltered.meta$spot_type, levels = c("tissue", "background"))
```
```{r}
flag <- all.meta$label == "FFPE"
plot(all.meta$E[flag], all.meta$nCount_Spatial[flag])
diff <- abs(all.meta$E - all.meta$nCount_Spatial)
abline(b = 1, a = 0)
plot(all.meta$tot.reads, all.meta$total.reads)
abline(b = 1, a = 0)
```
```{r}
# FFPE
dataset <- "EuE-31-236"
# FFPE:
# on.off.reads = on + off; off > 0
# on.off.reads = tot.umis
# nCount_Spatial < tot.umis
# nCount_Spatail < on.off.reads; nCount_Spatial > on.reads
# E is very, very low as is tot.mapped.reads ~ E
# tot.unammped.reads > tot.mapped.reads
# Frozen
# dataset <- "EuE-31-165"
# Frozen: nCount_Spatial = tot.umis = on.reads = on.off.reads ~ tot.mapped.reads ~ E
mol.info.file <- paste0(spaceranger_dirs[[dataset]], "molecule_info.h5")
gt <- get.targeted.genes(mol.info.file)
obj <- unfiltered.objs[[dataset]]
mat <- GetAssayData(obj, slot="counts")
mat.off <- mat[!(rownames(mat) %in% gt$gene.targets),]
mat.on <- mat[(rownames(mat) %in% gt$gene.targets),]
df.off <- data.frame(spot = colnames(mat.off), off.reads = as.vector(colSums(mat.off)))
df.on <- data.frame(spot = colnames(mat.on), on.reads = as.vector(colSums(mat.on)))
df.on.off <- merge(df.on, df.off)
df.on.off$on.off.reads <- df.on.off$on.reads + df.on.off$off.reads
meta <- filtered.objs[[dataset]][[]]
meta <- merge(meta, df.on.off, by.x = c("row.names"), by.y = c("spot"))
plot(meta$E, meta$tot.umis)
abline(a=0,b=1)
```
```{r}
# g <- ggplot(data = all.meta, aes(x = spot_type, y = saturation)) + facet_wrap(orig.ident ~ label) + geom_violin()
g <- ggplot(data = all.meta, aes(x = orig.ident, y = saturation, fill = label)) + geom_violin()
g <- g + xlab("Sample") + ylab("Per-spot Saturation\n(PCR Duplication Rate)")
if(is.null(dataset.secondary.labels)) {
foo <- reshape2::melt(all.meta[, c("orig.ident", "label", "num.nonzero.genes", "num.umis")], id.vars=c("orig.ident","label"))
g <- ggplot(data = foo, aes(x=orig.ident,y=value,fill=label)) + facet_wrap( ~ variable, scales="free") + geom_violin() + scale_y_continuous(trans="log2") + xlab("") + ylab("Count") + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) + labs(fill="")
} else {
foo <- reshape2::melt(all.meta[, c("orig.ident", "label", "sec.label", "num.nonzero.genes", "num.umis")], id.vars=c("orig.ident","label", "sec.label"))
g <- ggplot(data = foo, aes(x=orig.ident,y=value,fill=label)) + facet_wrap(sec.label ~ variable, scales="free") + geom_violin() + scale_y_continuous(trans="log2") + xlab("") + ylab("Count") + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) + labs(fill="")
}
```
```{r}
# Plot Number of UMIs and Number of Genes as a function of tissue vs background and FFPE vs Frozen
g.umi <- ggplot(data = all.unfiltered.meta, aes(x = orig.ident, y = num.umis, fill = label, linetype = spot_type))
if(!is.null(dataset.secondary.labels)) {
g.umi <- g.umi + facet_wrap(~ sec.label, scales="free")
}
g.umi <- g.umi + geom_violin() + scale_y_continuous(trans="log2")
g.umi <- g.umi + labs(fill="Protocol") + labs(linetype="Spot") + ylab("Number of UMIs") + xlab("")
g.umi <- g.umi + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
# g.umi <- g.umi + theme(legend.position = "top", legend.box = "vertical", text = element_text(size = 15))
g.umi <- g.umi + theme(legend.position = "top", text = element_text(size = 15))
g.genes <- ggplot(data = all.unfiltered.meta, aes(x = orig.ident, y = num.nonzero.genes, fill = label, linetype = spot_type))
if(!is.null(dataset.secondary.labels)) {
g.genes <- g.genes + facet_wrap(~ sec.label, scales="free")
}
g.genes <- g.genes + geom_violin() + scale_y_continuous(trans="log2")
g.genes <- g.genes + labs(fill="Protocol") + labs(linetype="Spot") + ylab("Number of Genes\n(with counts > 0)") + xlab("")
g.genes <- g.genes + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
g.genes <- g.genes + theme(legend.position = "top", text = element_text(size = 15))
g.legend <- get_legend(g.umi)
g.umi <- g.umi + theme(legend.position = "none")
g.genes <- g.genes + theme(legend.position = "none")
g <- plot_grid(plotlist=list(g.umi, g.genes), ncol=2, labels = "AUTO")
g <- plot_grid(g, g.legend, nrow = 2, rel_heights = c(5, 1))
png(paste0(plots_dir, "/", analysis_file_prefix, "num-umis-and-genes.png"), width = 2 * 480)
print(g)
d <- dev.off()
```
```{r}
# Plot Frac of reads that are mapped and fraction of reads that are exons
g.E <- ggplot(data = all.meta, aes(x = orig.ident, y = E.frac.mapped, fill = label))
if(!is.null(dataset.secondary.labels)) {
g.E <- g.E + facet_wrap(label ~ sec.label, scales="free")
}
g.E <- g.E + geom_violin()
g.E <- g.E + labs(fill="Protocol") + ylab("Exonic Fraction of Mapped Reads") + xlab("")
g.E <- g.E + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
g.E <- g.E + theme(legend.position = "top", text = element_text(size = 15))
g.map <- ggplot(data = all.meta, aes(x = orig.ident, y = map.frac, fill = label))
if(!is.null(dataset.secondary.labels)) {
g.map <- g.map + facet_wrap( ~ sec.label, scales="free")
}
g.map <- g.map + geom_violin()
g.map <- g.map + labs(fill="Protocol") + ylab("Mapped Fraction of Total Reads") + xlab("")
g.map <- g.map + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
g.map <- g.map + theme(legend.position = "top", text = element_text(size = 15))
g.legend <- get_legend(g.E)
g.E <- g.E + theme(legend.position = "none")
g.map <- g.map + theme(legend.position = "none")
g <- plot_grid(plotlist=list(g.map, g.E), ncol=2, labels = "AUTO")
g <- plot_grid(g, g.legend, nrow = 2, rel_heights = c(5, 1))
png(paste0(plots_dir, "/", analysis_file_prefix, "frac-E-and-mapped.png"), width = 2 * 480)
print(g)
d <- dev.off()
```
```{r}
# Plot saturation
g.sat <- ggplot(data = all.meta, aes(x = orig.ident, y = saturation, fill = label))
if(!is.null(dataset.secondary.labels)) {
g.sat <- g.sat + facet_wrap(~ sec.label, scales="free")
} else {
g.sat <- g.sat + facet_wrap(~ label, scales="free")
}
g.sat <- g.sat + geom_violin()
g.sat <- g.sat + labs(fill="Protocol") + ylab("Saturation Rate\n(Non-unique Reads / Total Reads)") + xlab("")
g.sat <- g.sat + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
g.sat <- g.sat + theme(legend.position = "bottom", text = element_text(size = 15))
png(paste0(plots_dir, "/", analysis_file_prefix, "saturation.png"), width = 1 * 480)
print(g.sat)
d <- dev.off()
```
```{r}
# Create a reference for RCTD deconvolution
counts_sc_full <- fread(paste0("/projects/compsci/jgeorge/USERS/somara/endometrium/","endo-2022_global.csv"))
counts_sc <- counts_sc_full
foo <- as.data.frame(counts_sc)
rownames(foo) <- foo$index
counts_sc <- t(foo[,-1])
# Note: using "main" cell types. These are a coarse set of cell types.
cell_types_main <- read.csv(paste0("/projects/compsci/jgeorge/USERS/somara/endometrium/","endo-2022_global_celltype_main.csv"))
cell_types_main <- setNames(cell_types_main[[2]], cell_types_main[[1]])
cell_types_main <- as.factor(cell_types_main) # convert to factor data type
cell_types_full <- fread(paste0("/projects/compsci/jgeorge/USERS/somara/endometrium/","endo-2022_global_celltype.csv"))
cell_types_full <- setNames(cell_types_full[[2]], cell_types_full[[1]])
cell_types_full <- as.factor(cell_types_full) # convert to factor data type
```
```{r}
# Run RCTD spot deconvolution
main.rctds <-
llply(datasets, .parallel = FALSE,
.fun = function(dataset) {
print(dataset)
rds.file <- paste0(analysis_dir, "/", dataset, "-rctd-main.rds")
if(!file.exists(rds.file)) {
obj <- filtered.objs[[dataset]]
mol.info.file <- paste0(spaceranger_dirs[[dataset]], "molecule_info.h5")
gt <- get.targeted.genes(mol.info.file)
st.counts <- GetAssayData(obj, assay="Spatial", slot="counts")
st.counts <- st.counts[rownames(st.counts) %in% gt$gene.targets,]
st.coords <- obj[[]][, c("col", "row")]
colnames(st.coords) <- c("x","y")
nUMI <- colSums(st.counts) # In this case, total counts per pixel is nUMI
puck <- SpatialRNA(st.coords, st.counts, nUMI)
#barcodes <- colnames(puck@counts) # pixels to be used (a list of barcode names).
#plot_puck_continuous(puck, barcodes, puck@nUMI, ylimit = c(0,round(quantile(puck@nUMI,0.9))),
# title ='plot of nUMI')
# Using "main" cell types here
sc_mat <- counts_sc[rownames(counts_sc) %in% gt$gene.targets,]
cell_types <- cell_types_main[colnames(sc_mat)]
nUMI <- colSums(sc_mat)
reference <- Reference(sc_mat, cell_types, nUMI, n_max_cells = ncol(sc_mat) + 1)
max_cores <- min(10, num.cores - 1)
myRCTD <- create.RCTD(puck, reference, max_cores = max_cores, keep_reference = TRUE)
myRCTD <- suppressPackageStartupMessages(run.RCTD(myRCTD, doublet_mode = 'full'))
saveRDS(myRCTD, rds.file)
}
cat(paste0("Reading ", rds.file, "\n"))
myRCTD <- readRDS(rds.file)
gc()
myRCTD
})
full.rctds <-
llply(datasets, .parallel = FALSE,
.fun = function(dataset) {
print(dataset)
rds.file <- paste0(analysis_dir, "/", dataset, "-rctd-full.rds")
if(!file.exists(rds.file)) {
obj <- filtered.objs[[dataset]]
mol.info.file <- paste0(spaceranger_dirs[[dataset]], "molecule_info.h5")
gt <- get.targeted.genes(mol.info.file)
st.counts <- GetAssayData(obj, assay="Spatial", slot="counts")
st.counts <- st.counts[rownames(st.counts) %in% gt$gene.targets,]
st.coords <- obj[[]][, c("col", "row")]
colnames(st.coords) <- c("x","y")
nUMI <- colSums(st.counts) # In this case, total counts per pixel is nUMI
puck <- SpatialRNA(st.coords, st.counts, nUMI)
#barcodes <- colnames(puck@counts) # pixels to be used (a list of barcode names).
#plot_puck_continuous(puck, barcodes, puck@nUMI, ylimit = c(0,round(quantile(puck@nUMI,0.9))),
# title ='plot of nUMI')
# Using "main" cell types here
sc_mat <- counts_sc[rownames(counts_sc) %in% gt$gene.targets,]
cell_types <- cell_types_full[colnames(sc_mat)]
nUMI <- colSums(sc_mat)
reference <- Reference(sc_mat, cell_types, nUMI, n_max_cells = ncol(sc_mat) + 1)
max_cores <- min(10, num.cores - 1)
myRCTD <- create.RCTD(puck, reference, max_cores = max_cores, keep_reference = TRUE)
myRCTD <- suppressPackageStartupMessages(run.RCTD(myRCTD, doublet_mode = 'full'))
saveRDS(myRCTD, rds.file)
}
cat(paste0("Reading ", rds.file, "\n"))
myRCTD <- readRDS(rds.file)
gc()
myRCTD
})
```
```{r}
# Add RCTD metadata to the Seurat objects
filtered.objs <-
llply(datasets, .parallel = FALSE,
.fun = function(nm) {
print(nm)
md.df <- format.rctd.output_(main.rctds[[nm]], normalize = FALSE)
md.df <- md.df[, !(colnames(md.df) %in% c("x","y"))]
colnames(md.df) <- paste0("main.", colnames(md.df))
obj <- add.metadata.to.seurat.obj(filtered.objs[[nm]], md.df)
md.df <- format.rctd.output_(full.rctds[[nm]], normalize = FALSE)
md.df <- md.df[, !(colnames(md.df) %in% c("x","y"))]
colnames(md.df) <- paste0("full.", colnames(md.df))
obj <- add.metadata.to.seurat.obj(obj, md.df)
obj
})
```
```{r}
angio.genes <- c("ANGPT1", "ANGPTL1", "ANGPT2", "SNCG", "FGF7", "CCL21")
epithelial.markers <- c("EPCAM", "CDH1")
stromal.markers <- c("COL1A1", "PDGRFA", "CD10")
# CD31 = PECAM1
endothelial.markers <- c("VWF", "PECAM1")
myeloid.markers <- c("PTPRC", "CD68", "CD14")
lymphocyte.markers <- c("PTPRC", "CD2", "CD3G", "CD20", "CD56")
fibroblast.markers <- c("MME")
cycling.genes <- c("TOP2A", "MKI67")
all.cell.type.markers <- unique(c(epithelial.markers, stromal.markers, endothelial.markers, myeloid.markers, lymphocyte.markers, fibroblast.markers))
ligand.receptor.genes <- c("TIE1", "TEK", "CCND1", "BTG2", "DLL4", "NOTCH1", "NOTCH4", "PECAM1", "JAM2", "VCAM1", "ICAM1", "CD99", "CXCL12", "PLVAP", "AQP1", "ICAM2")
all.genes <- unique(c(all.cell.type.markers, cycling.genes, ligand.receptor.genes))
# g <- plot.gene.expression.distribution.relative.to.quantiles(filtered.objs[[1]], all.cell.type.markers)
tls.genes <- c("MS4A1", "CD19", "CD3E", "CD3G", "PDPN", "HLA-DRA")
tls.names <- c("CD20", "CD19", "CD3E", "CD3G", "PDPN", "HLA-DRA")
for(dataset in datasets) {
obj <- filtered.objs[[dataset]]
mat <- GetAssayData(obj, slot="counts")
flag.b.cell <- (mat["MS4A1",] > 0) | (mat["CD19",] > 0)
flag.t.cell <- (mat["CD3E",] > 0) | (mat["CD3G",] > 0)
flag.follicular.dc <- (mat["PDPN",] > 0)
flag.apc <- (mat["HLA-DRA",] > 0)
flag <- flag.b.cell & flag.t.cell & flag.follicular.dc & flag.apc
df <- data.frame(TLS = flag)
rownames(df) <- colnames(mat)
obj <- add.metadata.to.seurat.obj(obj, df)
g <- SpatialDimPlot(obj, group.by = c("TLS"), cols=cbbPalette[1:2]) + theme(legend.text = element_text(size=20), legend.title = element_text(size = 20)) + guides(fill = guide_legend(override.aes = list(size = 5)))
png(paste0(plots_dir, "/", analysis_file_prefix, "tls-map-", dataset, ".png"), width = 1 * 480, height = 1 * 480)
print(g)
d <- dev.off()
cat(paste0(dataset, ": ", length(which(flag)), "\n"))
}
for(dataset in datasets) {
obj <- filtered.objs[[dataset]]
mat <- GetAssayData(obj, slot="counts")
flag.mac <- (mat["CD86",] > 0)
flag.treg <- (mat["CTLA4",] > 0)
# flag.treg <- flag.treg | (mat["FOXP3",] > 0)
flag <- flag.mac & flag.treg
df <- data.frame(CTLA4 = flag)
rownames(df) <- colnames(mat)
obj <- add.metadata.to.seurat.obj(obj, df)
g <- SpatialDimPlot(obj, group.by = c("CTLA4"), cols=cbbPalette[1:2]) + theme(legend.text = element_text(size=20), legend.title = element_text(size = 20)) + guides(fill = guide_legend(override.aes = list(size = 5)))
png(paste0(plots_dir, "/", analysis_file_prefix, "mac-ctla4-map-", dataset, ".png"), width = 1 * 480, height = 1 * 480)
print(g)
d <- dev.off()
cat(paste0(dataset, " mac+treg: ", length(which(flag)), " mac: ", length(which(flag.mac)), " treg: ", length(which(flag.treg)), "\n"))
}
g1 <- plot.features(filtered.objs[["EuE-31-235"]], features = tls.genes, feature.names = tls.names)
g1 <- add.title.to.plot(g1, "EuE-31-235")
g2 <- plot.features(filtered.objs[["EuE-31-236"]], features = tls.genes, feature.names = tls.names)
g2 <- add.title.to.plot(g2, "EuE-31-236")
g <- plot_grid(g1, g2, labels="AUTO", ncol=2)
png(paste0(plots_dir, "/", analysis_file_prefix, "tls.png"), width = 4 * 480, height = 1 * 480)
print(g)
d <- dev.off()
obj <- filtered.objs[["EuE-31-235"]]
g1 <- plot.gene.expression.distribution.relative.to.quantiles(filtered.objs[["EuE-31-235"]], tls.genes, probs = c(0.6, 0.7,0.8, 0.9, 1.0))
g1 <- g1 + theme(text = element_text(size = 30), axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
g2 <- plot.gene.expression.distribution.relative.to.quantiles(filtered.objs[["EuE-31-236"]], tls.genes, probs = c(0.6, 0.7,0.8, 0.9, 1.0))
png(paste0(plots_dir, "/", analysis_file_prefix, "EuE-31-235-tls-marker-distributions.png"), width = 2 * 480, height = 2 * 480)
print(g1)
d <- dev.off()
g1 <- plot.features(filtered.objs[["EcP-33-167"]], features = c("CD86", "CTLA4"))
g1 <- add.title.to.plot(g1, "EcP-33-167")
# g2 <- plot.features(filtered.objs[["EcP-33-166"]], features = c("CD86", "CTLA4"))
g2 <- plot.features(filtered.objs[["EuE-31-236"]], features = c("CD86", "CTLA4"))
g2 <- add.title.to.plot(g2, "EuE-31-236")
g <- plot_grid(g1, g2, labels="AUTO", ncol=2)
png(paste0(plots_dir, "/", analysis_file_prefix, "cd86-ctla4.png"), width = 2 * 480, height = 1 * 480)
print(g)
d <- dev.off()
g1 <- plot.gene.expression.distribution.relative.to.quantiles(filtered.objs[["EcP-33-167"]], c("CD86", "CTLA4"), probs = c(0.6, 0.7, 0.8, 0.9, 1.0))
g1 <- g1 + theme(text = element_text(size = 30), axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
g2 <- plot.gene.expression.distribution.relative.to.quantiles(filtered.objs[["EuE-31-236"]], c("CD86", "CTLA4"), probs = c(0.6, 0.7, 0.8, 0.9, 1.0))
g2 <- g2 + theme(text = element_text(size = 30), axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
g <- plot_grid(g1, g2, labels="AUTO", ncol=2)
png(paste0(plots_dir, "/", analysis_file_prefix, "cd86-ctla4-dist.png"), width = 4 * 480, height = 2 * 480)
print(g)
d <- dev.off()
# plot.features(filtered.objs[["EuE-31-235"]], features = tls.genes)
plot.features(filtered.objs[["EuE-31-236"]], features = tls.genes)
indices <- 1:length(datasets)
names(indices) <- names(datasets)
gs <- llply(indices,
.fun = function(indx) {
g <- plot.features(filtered.objs[[indx]], features = c("CD68", "PECAM1","EPCAM", "main.myeloid", "main.endothelial", "main.epithelial"), feature.names = c("CD68", "PECAM1", "EPCAM", "myeloid", "endo", "epi"), include.hne=FALSE)
g <- add.title.to.plot(g, ggtitle(paste0(names(dataset.labels)[indx], " (", dataset.labels[[indx]], ")")))
g
})
g <- plot_grid(gs[["EcP-33-167"]], gs[["EuE-31-165"]], labels="AUTO", ncol=2)
png(paste0(plots_dir, "/", analysis_file_prefix, "pop-markers-in-EuE-and-EcP.png"), width = 4 * 480, height = 1 * 480)
print(g)
d <- dev.off()
g1 <- plot.gene.expression.distribution.relative.to.quantiles(filtered.objs[["EcP-33-167"]], c("CD68", "COL1A1", "EPCAM", "PECAM1"), probs = c(0.8, 0.9, 1), nrow=2)
g1 <- g1 + theme(text = element_text(size = 30), axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
g2 <- plot.gene.expression.distribution.relative.to.quantiles(filtered.objs[["EuE-31-165"]], c("CD68", "COL1A1", "EPCAM", "PECAM1"), probs = c(0.8, 0.9, 1), nrow=2)
g2 <- g2 + theme(text = element_text(size = 30), axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
png(paste0(plots_dir, "/", analysis_file_prefix, "pop-marker-distributions.png"), width = 4 * 480, height = 2 * 480)
g <- plot_grid(g1, g2, labels="AUTO", ncol=2)
print(g)
d <- dev.off()
df_RCTD <- ldply(datasets, .fun = function(dataset) { format.rctd.output_(main.rctds[[dataset]]) })
sample.df <- data.frame(sample = names(dataset.labels), label = as.character(dataset.labels))
df_RCTD <- merge(df_RCTD, sample.df, by.x = c(".id"), by.y = c("sample"))
g <- plot.population.fractions.across.samples(df_RCTD, id.cols = c(".id", "label"), sample.col = c(".id"), fill = "label") + labs(fill="Protocol") + theme(text = element_text(size = 20))
png(paste0(plots_dir, "/", analysis_file_prefix, "main-pop-marker-distribution.png"), width = 2 * 480)
print(g)
d <- dev.off()
png(paste0(plots_dir, "/", analysis_file_prefix, "EuE-31-235-pop-markers.png"), width = 2 * 480, height = 1 * 480)
print(gs[["EuE-31-235"]])
d <- dev.off()
# plot.features(filtered.objs[[1]], features = c("EPCAM", "COL1A1", "PECAM1", "main.epithelial", "main.stromal", "main.endothelial"), feature.names = c("EPCAM", "COL1A1", "PECAM1", "epi", "stromal", "endo"), include.hne=FALSE)
# plot.features(filtered.objs[[6]], features = c("EPCAM", "COL1A1", "PECAM1", "main.epithelial", "main.stromal", "main.endothelial"), feature.names = c("EPCAM", "COL1A1", "PECAM1", "epi", "stromal", "endo"), include.hne=FALSE)
#plot.features(filtered.objs[[6]], features = angio.genes)
#plot.features(filtered.objs[[1]], features = angio.genes)
plot.features(filtered.objs[[3]], features = c("epithelial", "EPCAM", "CDH1", "KRT18", "KRT8"), include.hne=TRUE)
plot.features(filtered.objs[[4]], features = c("stromal", "THY1", "COL1A1", "OGN", "AIFM2", "POSTN", "IGFBP1", "PRL", "MME"), include.hne=TRUE)
plot.features(filtered.objs[[4]], features = c("stromal", "COL1A1", "MME"), include.hne=TRUE)
# plot.features(obj, features=c("total", "HBA2"))
# plot.features(filtered.objs[[1]], features = c("COL1A1", "MME", "TOP2A", "MKI67", "nCount_Spatial"), include.hne=TRUE)
plot.features(filtered.objs[[3]], features = c("COL1A1", "MKI67", "OGN", "nCount_Spatial"), include.hne=TRUE)
```
```{r}
# HERE
dataset <- "EcP-33-167"
g1 <- plot.features(unfiltered.objs[[dataset]], features = NULL, include.hne=TRUE, include.umi.cnts=TRUE, include.feature.cnts=TRUE, nrow=1)
g1 <- add.title.to.plot(g1, ggtitle(paste0(dataset, " (", dataset.labels[[dataset]], ")")))
dataset <- "EuE-31-235"
g2 <- plot.features(unfiltered.objs[[dataset]], features = NULL, include.hne=TRUE, include.umi.cnts=TRUE, include.feature.cnts=TRUE, nrow=1)
g2 <- add.title.to.plot(g2, ggtitle(paste0(dataset, " (", dataset.labels[[dataset]], ")")))
png(paste0(plots_dir, "/", analysis_file_prefix, "umi-gene-overlays.png"), width = 2 * 480, height = 1 * 480)
g <- plot_grid(g1, g2, labels="AUTO", nrow=2)
print(g)
d <- dev.off()
```
```{r}
# Compare FFPE to FF transcriptome wide
names(biological.replicates) <- biological.replicates
bio.rep.data.raw <-
llply(biological.replicates,
.fun = function(biological.replicate) {
bio.mat <-
ldply(datasets[grepl(datasets, pattern=biological.replicate)],
.fun = function(dataset) {
obj <- filtered.objs[[dataset]]
mat <- as.matrix(GetAssayData(obj, slot="counts", assay="Spatial"))
data.frame(gene = rownames(mat), expr.sum = as.vector(rowSums(mat)))
})
expr.ffpe <-
ddply(subset(bio.mat, .id %in% names(datasets)[dataset.labels=="FFPE"]),
.variables = c("gene"),
.fun = function(df) {
data.frame(mean.expr.ffpe = mean(df$expr.sum))
})
expr.frozen <-
ddply(subset(bio.mat, .id %in% names(datasets)[dataset.labels=="Frozen"]),
.variables = c("gene"),
.fun = function(df) {
data.frame(mean.expr.frozen = mean(df$expr.sum))
})
m <- merge(expr.ffpe, expr.frozen, all = TRUE)
m[is.na(m)] <- 0
# g <- ggplot(data = m, aes(x = mean.expr.ffpe, y = mean.expr.frozen)) + geom_hex()
m
})
```
```{r}
m <- bio.rep.data.raw[[1]]
m <- subset(m, mean.expr.frozen > 0 | mean.expr.ffpe > 0)
# See https://github.com/daattali/ggExtra/issues/27
g <- ggplot(data = m, aes(x = log2(1+mean.expr.frozen), y = log2(1+mean.expr.ffpe)))
g <- g + xlab("Frozen Log2(1 + Total UMIs Across Spots)") + ylab("FFPE Log2(1 + Total UMIs Across Spots)")
g <- g + geom_point(col="transparent")+geom_hex()+labs(fill="UMIs")
g <- g + theme(text = element_text(size = 15))
g.leg <- get_legend(g)
g <- ggMarginal(g + theme(legend.position = "none"), type="histogram")
g <- plot_grid(g, g.leg, rel_widths=c(10,1))
png(paste0(plots_dir, "/", analysis_file_prefix, "ffpe-vs-frozen-scatter.png"), width = 1 * 480)
print(g)
d <- dev.off()
uniq.ffpe <- subset(m, mean.expr.frozen == 0 & mean.expr.ffpe > 0)$gene
uniq.frozen <- subset(m, mean.expr.frozen > 0 & mean.expr.ffpe == 0)$gene
```
```{r}
do.kegg <- function(geneset) {
gene.df <- bitr(geneset, fromType = "SYMBOL", toType = c("ENTREZID" ), OrgDb = org.Hs.eg.db)
mkk <- enrichKEGG(gene = gene.df$ENTREZID , organism = 'hsa', pvalueCutoff = 1, qvalueCutoff = 1)
mkk
}
```
```{r}
uniq.ffpe.go <- enrichGO(uniq.ffpe, OrgDb=org.Hs.eg.db, keyType= 'SYMBOL', ont = "ALL", pvalueCutoff=0.01, qvalueCutoff = 0.05)
uniq.frozen.go <- enrichGO(uniq.frozen, OrgDb=org.Hs.eg.db, keyType= 'SYMBOL', ont = "ALL", pvalueCutoff=0.01, qvalueCutoff = 0.05)
uniq.ffpe.kegg <- do.kegg(uniq.ffpe)
uniq.frozen.kegg <- do.kegg(uniq.frozen)
# dotplot(yy,showCategory=30)
```
```{r}
p.ffpe.go <- pairwise_termsim(uniq.ffpe.go)
p.frozen.go <- pairwise_termsim(uniq.frozen.go)
```
```{r}
g.go <- emapplot(p.frozen.go)
```
```{r}
png(paste0(plots_dir, "/", analysis_file_prefix, "frozen-enrichment-go.png"), width = 2 * 480)
print(g.go + theme(text = element_text(size = 25)))
d <- dev.off()
```
```{r}
file <- "/projects/compsci/jgeorge/USERS/alizae/PDAC/plots/BR6526/BR6526_Downsampling_coor.csv"
df <- read.table(file, sep=",", header=TRUE)
df <- subset(df, tissue == "12min" & Type %in% c("neoplastic", "connective", "non-neoplastic", "Totall_nCells"))
# g <- ggplot(df, aes(x = nCells_40X, y = nCells_20X)) + facet_wrap(~ Type, scales="free") + geom_point() + xlab("Number of Cells (40X)") + ylab("Number of Cells (20X)") + theme(text = element_text(size = 20))
p_load(ggpubr)
labels <- c("connective" = "stromal", "neoplastic" = "neoplastic", "non-neoplastic" = "normal epithelial", "Totall_nCells" = "all cells")
g <- ggscatter(df, x = "nCells_40X", y = "nCells_20X", add = "reg.line", add.params = list(color = "blue"), cor.coef = TRUE, cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n"), cor.coef.size = 5, color="gray") + facet_wrap(~ Type, scales="free", labeller = as_labeller(labels)) + xlab("Number of Cells (40X)") + ylab("Number of Cells (20X)") + theme(text = element_text(size = 20))
png(paste0(plots_dir, "/", analysis_file_prefix, "hov-40x-vs-20x-12min.png"))
print(g)
d <- dev.off()
```
```{r}
bio.rep.data <-
llply(biological.replicates,
.fun = function(biological.replicate) {
bio.mat <-
ldply(datasets[grepl(datasets, pattern=biological.replicate)],
.fun = function(dataset) {
obj <- filtered.objs[[dataset]]
mat <- as.matrix(GetAssayData(obj, slot="counts", assay="cpm"))
df <- reshape2::melt(mat)
colnames(df) <- c("gene", "spot", "expr")
ddply(df, .variables = c("gene"),
.fun = function(sub.df) {
data.frame(mean.expr = mean(sub.df$expr))
})
})
expr.ffpe <-
ddply(subset(bio.mat, .id %in% names(datasets)[dataset.labels=="FFPE"]),
.variables = c("gene"),
.fun = function(df) {
data.frame(mean.expr.ffpe = mean(df$mean.expr))
})
expr.frozen <-
ddply(subset(bio.mat, .id %in% names(datasets)[dataset.labels=="Frozen"]),
.variables = c("gene"),
.fun = function(df) {
data.frame(mean.expr.frozen = mean(df$mean.expr))
})
m <- merge(expr.ffpe, expr.frozen)
# g <- ggplot(data = m, aes(x = mean.expr.ffpe, y = mean.expr.frozen)) + geom_hex()
m
})
```
```{r}
hov.json.paths <-
list("EuE-31-235" = "/projects/compsci/jgeorge/whitebr/cancer-cube/endometrium/analysis/hov/SC2200235/json/SC2200235-pyramid.json",
"EuE-31-165" = "/projects/compsci/jgeorge/whitebr/cancer-cube/endometrium/analysis/hov/SC2200165/json/SC2200165-pyramid.json")
hov.tbls <-
llply(hov.json.paths,
.fun = function(hov.json.path) {
json.type.info.path <- "/projects/chuang-lab/USERS/whitebr/hover_net/type_info.json"
tbl <- extract.cell.info.from.hovernet.output(hov.json.path)[["df"]]
cell.info.tbl <- extract.hovernet.cell.info(json.type.info.path)
stbl <- merge(tbl, cell.info.tbl, by.x="type", by.y="id")
})
```
```{r}
nms <- names(hov.tbls)
names(nms) <- nms
hov.mats <-
llply(nms,
.fun = function(dataset) {
deltas = get.visium.spot.distance.separation(unfiltered.objs[[dataset]])
dx <- deltas[1]
dy <- deltas[2]
scale <- 2
col_offset <- -15
row_offset <- -9
#col_offset <- 0
#row_offset <- 0
stbl <- hov.tbls[[dataset]]
stbl$col <- unlist(lapply(stbl$centroid.x, function(x) (floor(x * scale / dx ) + 1 + col_offset)))
stbl$row <- unlist(lapply(stbl$centroid.y, function(y) (floor(y * scale / dy ) + 1 + row_offset)))
all.cell.types <- unique(stbl$label)
stbl$pos.id <- paste0(stbl$row, "-", stbl$col)
cnt.tbl <- acast(stbl[, c("pos.id", "label")], pos.id ~ label)
pct.tbl <- t(apply(cnt.tbl, 1, function(row) row / sum(row)))
cnt.tbl <- cbind(cnt.tbl, total = rowSums(cnt.tbl))
colnames(pct.tbl) <- paste0(colnames(pct.tbl), ".frac")
all.tbl <- rbind(reshape2::melt(cnt.tbl), reshape2::melt(pct.tbl))
colnames(all.tbl) <- c("pos.id", "label", "value")
mat <- ddply(all.tbl, .variables = c("pos.id"), .fun = function(df) { vec <- df$value; names(vec) <- df$label; vec})
mat$pos.id <- as.character(mat$pos.id)
mat$row <- unlist(lapply(mat$pos.id, function(str) as.numeric(strsplit(str, split="-")[[1]][1])))
mat$col <- unlist(lapply(mat$pos.id, function(str) as.numeric(strsplit(str, split="-")[[1]][2])))
mat
})
```
```{r}
sample <- "EuE-31-235"
#sample <- "EuE-31-165"
mat <- hov.mats[[sample]]
obj <- filtered.objs[[sample]]
meta <- obj[[]]
meta$row.name <- rownames(meta)
meta <- merge(meta[, c("row.name", "row", "col")], mat, by = c("row", "col"), all.x=TRUE)
rownames(meta) <- meta$row.name
meta <- meta[, !(colnames(meta) %in% c("row.name"))]
meta[is.na(meta)] <- 0
obj <- add.metadata.to.seurat.obj(obj, meta)
obj[["percent.mt"]] <- PercentageFeatureSet(obj, pattern = "^MT-")
plot.features(obj, features="connec")
```
```{r}
mol.info.file <- paste0(hr, "molecule_info.h5")
h5ls(mol.info.file)
probe.gene.ids <- h5read(mol.info.file, "/features/target_sets/Visium Human Transcriptome Probe Set")
gene.ensg.ids <- h5read(mol.info.file, "/features/id")
gene.names <- h5read(mol.info.file, '/features/name')
gene.indices <- 1:length(gene.ensg.ids)
nonprobe.gene.ids <- gene.indices[!(gene.indices %in% probe.gene.ids)]
nonprobe.gene.names <- sort(gene.names[nonprobe.gene.ids])
probe.gene.names <- gene.names[probe.gene.ids]
# Calculate per-spot saturation, per-spot num reads, per-spot num unmapped reads
#gene <- h5read(mol.info.file, "/features/name")[h5read(mol.info.file, "/feature_idx")+1]
#umi.type <- h5read(mol.info.file, "/umi_type")
mol.info.file <- paste0(spaceranger_dirs[[3]], "molecule_info.h5")
df <- data.frame(barcode = h5read(mol.info.file, "/barcodes")[h5read(mol.info.file, "/barcode_idx")+1], count = h5read(mol.info.file, "/count"))
spot.saturation <- ddply(df,
.variables = c("barcode"),
.fun = function(tbl) {
tot.reads <- sum(tbl$count)
umis <- nrow(tbl)
saturation <- (tot.reads - umis) / tot.reads
data.frame(tot.reads = tot.reads, tot.umis = umis, saturation = saturation)
})
# /projects/compsci/jgeorge/USERS/somara/endometrium/SC2200236_EuE-31/spaceranger/probe_set.csv
# Run hovernet on these 4 guys
# Align hovernet
# Goal: disentangle issue with markers / deconvolution
# Write function for saturation
# Store alignment metrics
# Merge saturation and alignment metrics to one another
# Append both to metadata
# Plot background and foreground saturation for FFPE and FF.
# Annotated gene as belonging to probe set or not
# Function to get probe and non-probe genes
```
```{r}
ffpe.dataset <- "EuE-31-236"
frozen.dataset <- "EuE-31-164"
ffpe.bam.file <- paste0(spaceranger_dirs[[ffpe.dataset]], "/possorted_genome_bam.bam")