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check-poisson-assumption.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))
suppressPackageStartupMessages(p_load(ggExtra))
```
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")
source("qc_pipeline/util_functions.R")
```
Setup a parallel execution environment.
```{r}
num.cores <- setup.parallel.environment(max.cores=10)
```
```{r}
unregister_dopar <- function() {
env <- foreach:::.foreachGlobals
rm(list=ls(name=env), pos=env)
}
```
```{r}
init.file <- '/pod/2/activities/jaxcc/CATch-project/raw-data/visium/analysis_dir/Endometrium/Endometrium.csv'
output.dir <- '/projects/compsci/jgeorge/whitebr/cancer-cube/'
dir.create(output.dir, recursive=TRUE)
```
```{r}
dataset.data <- load.init.file(init.file)
```
```{r}
source("https://raw.githubusercontent.com/saketkc/scRNA_NB_comparison/master/code/01_run_glm.R")
```
```{r}
# Modified version of GetFit from
# https://raw.githubusercontent.com/saketkc/scRNA_NB_comparison/master/code/01_run_glm.R")
# to add parallelism (via llply)
my.GetFit <- function(cm, ncells = dim(cm)[2], type = "poisson") {
cm <- cm[rowSums(cm) > 0, colSums(cm) > 0]
ncells <- min(ncells, dim(cm)[2])
total_umi <- colSums(cm)
if (ncells == dim(cm)[2]) {
# No sampling
sampled_cells <- colnames(cm)
} else {
sampled_cells <- dds(total_umi, colnames(cm), ncells)
}
cm_sampled <- cm[, sampled_cells]
total_umi_sampled <- colSums(cm_sampled)
genes <- rownames(cm_sampled)
names(genes) <- genes
#pvals_list <- mclapply(genes, FUN = function(gene) {
# gene_umi <- as.vector(cm_sampled[gene, ])
# pvals <- RunGLM(gene_umi = gene_umi, cell_umi = total_umi_sampled, type = type)
# pvals
#}, mc.cores = mc.cores)
# pvals_list <- lapply(genes, FUN =
pvals_list <- llply(genes, .parallel = TRUE, .fun =
function(gene) {
gene_umi <- as.vector(cm_sampled[gene, ])
pvals <- RunGLM(gene_umi = gene_umi, cell_umi = total_umi_sampled, type = type)
pvals
})
names(pvals_list) <- genes
pvalsfit <- bind_rows(pvals_list, .id = "gene")
meanvar <- MeanVarFit(cm)
meanvar$total_gene_umi <- rowSums(cm)
meanvar$total_cell_umi <- sum(total_umi_sampled)
meanvar$median_cell_umi <- median(total_umi_sampled)
meanvar$mean_cell_umi <- mean(total_umi_sampled)
meanvarfit <- inner_join(meanvar, pvalsfit, by = "gene")
qval_cols <- c("qval_ssr_deviance", "qval_ssr_pearson", "qval_ssr_quantile", "qval_ssrstd_deviance", "qval_ssrstd_pearson", "qval_ssrstd_quantile")
for (qval_col in qval_cols) {
pval_col <- gsub("qval", "pval", qval_col)
meanvarfit[, qval_col] <- GetQvalues(meanvarfit[, pval_col])
}
return(list(meanvarfit = meanvarfit, cell_attr = CellSummary(cm_sampled), gene_attr = GeneSummary(cm_sampled)))
}
```
```{r}
filtered.objs <- dataset.data$filtered.objs
```
```{r}
p.fits <-
llply(1:length(filtered.objs), .parallel = FALSE,
.fun = function(indx) {
print(names(filtered.objs)[indx])
fit.p <- my.GetFit(GetAssayData(filtered.objs[[indx]], assay="Spatial"), type="poisson")
#fit.p$meanvarfit[, c("gene", "mean", "variance", "pval_ssrstd_quantile")]
print(table(fit.p$meanvarfit$pval_ssrstd_quantile < 0.05))
flag <- fit.p$meanvarfit$pval_ssrstd_quantile < 0.05
non.p.genes <- fit.p$meanvarfit[flag, "gene"]
fit.p
})
```
```{r}
stop("stop")
```
```{r}
filtered.objs <-
llply(filtered.objs, .parallel = FALSE,
.fun = function(obj) SCTransform(obj, assay = "Spatial", verbose = TRUE))
```
```{r}
# Read in Endometrium scRNA-seq data
read.endometrium.scrna.seq.data <- function(expr.file, cell.label.file) {
counts_sc <- as.data.frame(fread(expr.file))
rownames(counts_sc) <- counts_sc$index
counts_sc <- t(counts_sc[,-1])
# Note: using "main" cell types. These are a coarse set of cell types.
cell_types <- read.csv(cell.label.file)
cell_types <- setNames(cell_types[[2]], cell_types[[1]])
cell_types <- as.factor(cell_types) # convert to factor data type
return(list(counts=counts_sc, cell_labels=cell_types))
}
```
```{r}
# Confirm that all samples are using the sample single-cell dataset
sc.counts.file <- dataset.data$metadata[1, "single_cell_expr"]
cell.label.file <- dataset.data$metadata[1, "single_cell_cell_types"]
stopifnot(all(dataset.data$metadata$single_cell_expr == sc.counts.file))
stopifnot(all(dataset.data$metadata$single_cell_cell_types == cell.label.file))
sc.res <- read.endometrium.scrna.seq.data(sc.counts.file, cell.label.file)
```
```{r}
indices <- 1:nrow(dataset.data$metadata)
names(indices) <- dataset.data$metadata$names
rctd.res <-
llply(indices, .parallel = FALSE,
.fun = function(i) {
print(dataset.data$metadata[i,"names"])
rctd.wrapper(st.obj = dataset.data$filtered.objs[[i]], sc.counts = sc.res$counts, sc.cell.types = sc.res$cell_labels,
rds.output.file = paste0(output.dir, "/", dataset.data$metadata[i, "names"], "-rctd.rds"))
})
```
```{r}
rctd.res.no.int <-
llply(indices, .parallel = FALSE,
.fun = function(i) {
print(dataset.data$metadata[i,"names"])
rctd.wrapper(st.obj = dataset.data$filtered.objs[[i]], sc.counts = sc.res$counts, sc.cell.types = sc.res$cell_labels,
rds.output.file = paste0(output.dir, "/", dataset.data$metadata[i, "names"], "-no-intersect-rctd.rds"), intersect.sc.and.st.genes = FALSE)
})
```
```{r}
subset.indices <- indices[c(1,3)]
rctd.objs <-
llply(subset.indices, .parallel = FALSE,
.fun = function(i) {
print(dataset.data$metadata[i,"names"])
rctd.wrapper(st.obj = dataset.data$filtered.objs[[i]], sc.counts = sc.res$counts, sc.cell.types = sc.res$cell_labels,
rds.output.file = NULL, run.rctd = FALSE, intersect.sc.and.st.genes = TRUE)
})
```
```{r}
rctd.objs.no.int <-
llply(subset.indices, .parallel = FALSE,
.fun = function(i) {
print(dataset.data$metadata[i,"names"])
rctd.wrapper(st.obj = dataset.data$filtered.objs[[i]], sc.counts = sc.res$counts, sc.cell.types = sc.res$cell_labels,
rds.output.file = NULL, run.rctd = FALSE, intersect.sc.and.st.genes = FALSE)
})
```
```{r}
# Extract RCTD markers
rctd.markers <-
llply(rctd.res.no.int,
.fun = function(res) res@internal_vars$gene_list_reg)
```
```{r}
indx <- 1
puck <- rctd.res[[indx]]@originalSpatialRNA
config <- rctd.res[[indx]]@config
cell_type_info <- rctd.res[[indx]]@cell_type_info$info
puck <- rctd.res.no.int[[indx]]@originalSpatialRNA
config <- rctd.res.no.int[[indx]]@config
cell_type_info <- rctd.res.no.int[[indx]]@cell_type_info$info
fc_thresh = config$fc_cutoff_reg
expr_thresh = config$gene_cutoff_reg
MIN_OBS = config$MIN_OBS
de.genes <- get_de_genes(cell_type_info, puck, fc_thresh, expr_thresh, MIN_OBS)
```
```{r}
# Modified version of GetFit from
# https://raw.githubusercontent.com/saketkc/scRNA_NB_comparison/master/code/01_run_glm.R")
# to add parallelism (via llply)
my.GetFit <- function(cm, ncells = dim(cm)[2], type = "poisson") {
cm <- cm[rowSums(cm) > 0, colSums(cm) > 0]
ncells <- min(ncells, dim(cm)[2])
total_umi <- colSums(cm)
if (ncells == dim(cm)[2]) {
# No sampling
sampled_cells <- colnames(cm)
} else {
sampled_cells <- dds(total_umi, colnames(cm), ncells)
}
cm_sampled <- cm[, sampled_cells]
total_umi_sampled <- colSums(cm_sampled)
genes <- rownames(cm_sampled)
names(genes) <- genes
#pvals_list <- mclapply(genes, FUN = function(gene) {
# gene_umi <- as.vector(cm_sampled[gene, ])
# pvals <- RunGLM(gene_umi = gene_umi, cell_umi = total_umi_sampled, type = type)
# pvals
#}, mc.cores = mc.cores)
# pvals_list <- lapply(genes, FUN =
pvals_list <- llply(genes, .parallel = TRUE, .fun =
function(gene) {
gene_umi <- as.vector(cm_sampled[gene, ])
pvals <- RunGLM(gene_umi = gene_umi, cell_umi = total_umi_sampled, type = type)
pvals
})
names(pvals_list) <- genes
pvalsfit <- bind_rows(pvals_list, .id = "gene")
meanvar <- MeanVarFit(cm)
meanvar$total_gene_umi <- rowSums(cm)
meanvar$total_cell_umi <- sum(total_umi_sampled)
meanvar$median_cell_umi <- median(total_umi_sampled)
meanvar$mean_cell_umi <- mean(total_umi_sampled)
meanvarfit <- inner_join(meanvar, pvalsfit, by = "gene")
qval_cols <- c("qval_ssr_deviance", "qval_ssr_pearson", "qval_ssr_quantile", "qval_ssrstd_deviance", "qval_ssrstd_pearson", "qval_ssrstd_quantile")
for (qval_col in qval_cols) {
pval_col <- gsub("qval", "pval", qval_col)
meanvarfit[, qval_col] <- GetQvalues(meanvarfit[, pval_col])
}
return(list(meanvarfit = meanvarfit, cell_attr = CellSummary(cm_sampled), gene_attr = GeneSummary(cm_sampled)))
}
```
```{r}
lapply(rctd.markers, length)
source("https://raw.githubusercontent.com/saketkc/scRNA_NB_comparison/master/code/01_run_glm.R")
p.fits <-
llply(1:length(rctd.markers), .parallel = FALSE,
.fun = function(indx) {
print(names(filtered.objs)[indx])
markers <- rctd.markers[[indx]]
fit.p <- my.GetFit(GetAssayData(filtered.objs[[indx]], assay="Spatial")[markers,], type="poisson")
#fit.p$meanvarfit[, c("gene", "mean", "variance", "pval_ssrstd_quantile")]
print(table(fit.p$meanvarfit$pval_ssrstd_quantile < 0.05))
})
```
```{r}
# "EPCAM" %in% rctd.res[[3]]@internal_vars$gene_list_reg
```
```{r}
fit.poisson.to.gene.count <- function(mat, gene) {
df <- data.frame(gene_umi = unname(mat[gene,]), total_umi = unname(colSums(mat)))
df <- data.frame(gene_umi = unname(mat[gene,]), total_umi = unname(colSums(mat)), log_total_umi = log(unname(colSums(mat))))
# Fit a Poisson distribution to gene UMIs, as described in
# Comparison and evaluation of statistical error models for scRNA-seq
# res <- glm(gene_umi ~ 1, offset = log(total_umi), family = poisson(link="log"), data = df)
res <- glm(gene_umi ~ 1, offset = log_total_umi, family = poisson(link="log"), data = df)
# res <- glm(gene_umi ~ 1, family = poisson(link="log"), data = df)
res
}
```
```{r}
gene <- "EPCAM"
res <- fit.poisson.to.gene.count(mat, gene)
df <- data.frame(gene_umi = unname(mat[gene,]), total_umi = unname(colSums(mat)), log_total_umi = log(unname(colSums(mat))))
ggplot(df, aes(gene_umi)) + geom_histogram(aes(y = ..density..)) + stat_function(fun = function(x) dpois(x,lambda=exp(res$coefficients[1])))
pred_pois <- dpois(df$gene_umi, lambda=exp(res$coefficients[1]))
max(predict(res, newdata=df))
```
```{r}
markers <- rctd.res[[3]]@internal_vars$gene_list_reg
names(markers) <- markers
```
```{r}
models <-
llply(markers, .fun = function(gene) fit.poisson.to.gene.count(mat, gene))
```
```{r}
calculate.glm.gof <- function(model) {
pchisq(sum(statmod::qresid(model)^2), df=model$df.residual, lower.tail=FALSE)
}
```
```{r}
gofs <- llply(models, .fun = function(model) calculate.glm.gof(model))
gof.tbl <- data.frame(marker = names(gofs), gof = unname(unlist(gofs)))
gof.tbl[which.max(gof.tbl$gof),]
```
```{r}
# pchisq(sum(statmod::qresid(model)^2), df=model$df.residual, lower.tail=FALSE)
```
```{r}
if(FALSE) {
l_ply(indices, .parallel = FALSE,
.fun = function(i) {
obj <- rctd.res[[i]]
rds.output.file = paste0(output.dir, "/", dataset.data$metadata[i, "names"], "-rctd.rds")
saveRDS(obj, rds.output.file)
})
}
```
```{r}
filtered.objs <-
llply(indices, .parallel = FALSE,
.fun = function(i) {
# md.df <- format.rctd.output_(rctd.res[[i]], normalize = FALSE)
md.df <- format.rctd.output_(rctd.res.no.int[[i]], normalize = FALSE)
md.df <- md.df[, !(colnames(md.df) %in% c("x","y"))]
colnames(md.df) <- paste0("main.", colnames(md.df))
obj <- add.metadata.to.seurat.obj(dataset.data$filtered.objs[[i]], md.df)
obj
})
```
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[2]
curr_datatypes=data_types[4] # Ren breastcancer
# curr_datatypes=data_types[5] # Ren lung
# curr_datatypes='endometrium_and_peritoneum'
if (curr_datatypes==data_types[1]){
base_dir<-'/projects/compsci/USERS/somara/pdac/'
base_dir <- '/pod/2/activities/jaxcc/CATch-project/raw-data/visium/pilot-1/Tuveson/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/'
base_dir <- '/pod/2/activities/jaxcc/CATch-project/raw-data/visium/pilot-1/Courtois/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/USERS/somara/breast_cancer/'
base_dir <- '/pod/2/activities/jaxcc/CATch-project/raw-data/visium/pilot-1/Ren/breast/'
output_base_dir <- '/projects/compsci/jgeorge/whitebr/cancer-cube/Ren-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/'
base_dir <- '/pod/2/activities/jaxcc/CATch-project/raw-data/visium/pilot-1/Ren/lung/'
output_base_dir <- '/projects/compsci/jgeorge/whitebr/cancer-cube/Ren-lung/'
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}
# Make some plots for Sue's DIF
# This is for endometrium:
# indx <- 3
# This is for breast
indx <- 3
obj <- filtered.objs[[indx]]
p <- plot.spatial(obj, "nFeature_Spatial", "# Genes (K)", rescale.legend = TRUE)
png(paste0(make.names(names(filtered.objs)[indx]),"-gene-counts.png"))
print(p)
dev.off()
g <- plot.features(obj, features=NULL, include.hne=TRUE, include.feature.cnts = TRUE)
png(paste0(make.names(names(filtered.objs)[indx]),"-gene-counts-and-hne.png"), width = 1.4 * 480)
print(g)
dev.off()
print(dim(GetAssayData(obj)))
```
```{r}
df <-
ldply(filtered.objs,
.fun = function(obj) {
obj[[]][, c("nCount_Spatial", "nFeature_Spatial")]
})
df <- merge(df, data.frame(.id = names(dataset.labels), protocol = as.vector(unlist(dataset.labels))))
g <- ggplot(data = df, aes(x = .id, y = nCount_Spatial, fill = protocol)) + geom_violin() + scale_y_continuous(trans="log10")
g <- ggplot(data = df, aes(x = .id, y = nFeature_Spatial, fill = protocol)) + geom_violin() + scale_y_continuous(trans="log10")
```
```{r}
get.gene.avg.sd <- function(obj, normalize = FALSE) {
mat <- as.matrix(GetAssayData(obj))
if(normalize) {
mat <- sweep(mat,2,colSums(mat),`/`)
}
mean_counts <- apply(mat, 1, mean)
variance_counts <- apply(mat, 1, var)
df <- data.frame(mean_counts, variance_counts)
df
}
```
```{r}
# Calculate dispersions
disps <-
llply(filtered.objs, .parallel = TRUE,
.fun = function(obj) {
mat <- as.matrix(GetAssayData(obj))
na.flag <- apply(mat, 1, function(r) any(is.na(r)))
mat <- mat[!na.flag,]
na.flag <- apply(mat, 2, function(c) any(is.na(c)))
mat <- mat[,!na.flag]
expressed <- apply(mat, 1, function(r) any(r > 0))
counts <- mat[expressed, ]
# plot mean variance plot using function provided by edgeR
d <- DGEList(counts)
d <- estimateCommonDisp(d)
d
})
```
```{r}
genes <- lapply(filtered.objs, function(x) rownames(GetAssayData(x)))
common.genes <- Reduce(intersect, genes)
disps.common.genes <-
llply(filtered.objs, .parallel = TRUE,
.fun = function(obj) {
mat <- as.matrix(GetAssayData(obj))[common.genes,]
na.flag <- apply(mat, 1, function(r) any(is.na(r)))
mat <- mat[!na.flag,]
na.flag <- apply(mat, 2, function(c) any(is.na(c)))
mat <- mat[,!na.flag]
expressed <- apply(mat, 1, function(r) any(r > 0))
counts <- mat[expressed, ]
# plot mean variance plot using function provided by edgeR
d <- DGEList(counts)
d <- estimateCommonDisp(d)
d
})
```
```{r}
mat <- as.matrix(GetAssayData(obj))
na.flag <- apply(mat, 1, function(r) any(is.na(r)))
mat <- mat[!na.flag,]
na.flag <- apply(mat, 2, function(c) any(is.na(c)))
mat <- mat[,!na.flag]
expressed <- apply(mat, 1, function(r) any(r > 0))
counts <- mat[expressed, ]
# plot mean variance plot using function provided by edgeR
d <- DGEList(counts)
d <- estimateCommonDisp(d)
plotMeanVar(d, show.raw.vars = TRUE, show.ave.raw.vars = FALSE, NBline = TRUE)
legend("topleft", legend = c("Poisson", "Negative Binomial"), fill = c("black", "dodgerblue"), border = c("black", "dodgerblue"), bty = "n")
```
```{r}
# Add normalized (non-log) Counts Per Million (CPM) data to the filtered objects
filtered.objs <-
llply(filtered.objs,
.fun = function(obj) {
cpm.mat <- cpm(GetAssayData(obj, slot="counts", assay="Spatial"), log = FALSE)
obj[["cpm"]] <- CreateAssayObject(counts = cpm.mat)
obj
})
```
```{r}
# Plot the biotypes (e.g., protein coding, ribosomal, mitochondrial, etc) across samples
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}
# Compute sequencing alignment metrics for all samples.
# This includes things like number and fraction of mapped reads, unmapped reads,
# exonic, intronic, and intergenic reads. It is slow because it parses the BAM file.
# As such, save the results and don't recompute them if they exist.
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}
# Compute per-spot saturation metrics.
# This isn't quick, but isn't nearly as slow as calculating the alignment 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}
# If this is a "secondary label" [e.g., endometrium (EuE) vs peritoneum (Ecp) for the combined
# endometrium / peritoneum dataset], add it to the metadata
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)
```
```{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}
# Beyond this point, things get a little "junky" and endometrium specific.
```
```{r}
# Plot per-spot saturation rate.
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
# Read in the single-cell data.
counts_sc_full <- fread(paste0("/projects/compsci/jgeorge/USERS/somara/datasets/endometrium/","endo-2022_global.csv"))
counts_sc <- counts_sc_full
foo <- as.data.frame(counts_sc)
rownames(foo) <- foo$index
counts_sc <- t(foo[,-1])
# Note: using "main" cell types. These are a coarse set of cell types.
cell_types_main <- read.csv(paste0("/projects/compsci/jgeorge/USERS/somara/datasets/endometrium/","endo-2022_global_celltype_main.csv"))
cell_types_main <- setNames(cell_types_main[[2]], cell_types_main[[1]])
cell_types_main <- as.factor(cell_types_main) # convert to factor data type
cell_types_full <- fread(paste0("/projects/compsci/jgeorge/USERS/somara/datasets/endometrium/","endo-2022_global_celltype.csv"))
cell_types_full <- setNames(cell_types_full[[2]], cell_types_full[[1]])
cell_types_full <- as.factor(cell_types_full) # convert to factor data type
```
```{r}
# Run RCTD spot deconvolution -- first using the "main" (coarse-grained) endometrium cell types
main.rctds <-
llply(datasets, .parallel = FALSE,
.fun = function(dataset) {
print(dataset)
rds.file <- paste0(analysis_dir, "/", dataset, "-rctd-main.rds")
if(!file.exists(rds.file)) {
obj <- filtered.objs[[dataset]]
mol.info.file <- paste0(spaceranger_dirs[[dataset]], "molecule_info.h5")
# Get the genes that are included in the ST spot data for this sample.
gt <- get.targeted.genes(mol.info.file)
st.counts <- GetAssayData(obj, assay="Spatial", slot="counts")
st.counts <- st.counts[rownames(st.counts) %in% gt$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)
# Create a reference for _each_ sample. Why? Because the Reference is
# dependent on a single-cell RNA-seq reference (sc_mat) and we want
# to ensure that those genes are in the spots. If they weren't, we
# may have genes selected as markers that aren't actually in the ST
# data. This concern has not actually been confirmed by examining the code,
# which may handle this case.
sc_mat <- counts_sc[rownames(counts_sc) %in% gt$gene.targets,]
cell_types <- cell_types_main[colnames(sc_mat)]
nUMI <- colSums(sc_mat)
reference <- Reference(sc_mat, cell_types, nUMI, n_max_cells = ncol(sc_mat) + 1)
max_cores <- min(10, num.cores - 1)
# Note that we have keep_reference = TRUE here. Without it, the default is
# keep_reference = FALSE, which would ignore the Reference we have
# intentionally created above.
myRCTD <- create.RCTD(puck, reference, max_cores = max_cores, keep_reference = TRUE)
myRCTD <- suppressPackageStartupMessages(run.RCTD(myRCTD, doublet_mode = 'full'))
saveRDS(myRCTD, rds.file)
}
cat(paste0("Reading ", rds.file, "\n"))
myRCTD <- readRDS(rds.file)
gc()
myRCTD
})
# Run deconvolution again, this time using the "full" (fine-grained) cell types.
full.rctds <-
llply(datasets, .parallel = FALSE,
.fun = function(dataset) {
print(dataset)
rds.file <- paste0(analysis_dir, "/", dataset, "-rctd-full.rds")
if(!file.exists(rds.file)) {
obj <- filtered.objs[[dataset]]
mol.info.file <- paste0(spaceranger_dirs[[dataset]], "molecule_info.h5")
gt <- get.targeted.genes(mol.info.file)
st.counts <- GetAssayData(obj, assay="Spatial", slot="counts")
st.counts <- st.counts[rownames(st.counts) %in% gt$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)
# 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
})