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M01_QCPreprocessing_public.Rmd
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M01_QCPreprocessing_public.Rmd
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---
title: "QC and Preprocessing"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
source_code: embed
theme:
bootswatch: flatly
navbar:
- { title: "scMiko", href: "https://nmikolajewicz.github.io/scMiko/" }
editor_options:
chunk_output_type: inline
knit: (function(inputFile, encoding) {
rmarkdown::render(input = inputFile,
encoding = encoding,
output_file = if (exists("user")){paste0(
xfun::sans_ext(inputFile), '_',user, "_",
paste0(format(Sys.time(), "%d_%b_"), gsub(" ", "_", gsub(":", "_", format(Sys.time(), "%X"))), format(Sys.time(), "_%Y")), '.html'
)} else {paste0(xfun::sans_ext(inputFile), '_',"Guest", "_",
paste0(format(Sys.time(), "%d_%b_"), gsub(" ", "_", gsub(":", "_", format(Sys.time(), "%X"))), format(Sys.time(), "_%Y")), '.html')},
output_dir = if (exists("data.path")) paste0(data.path, "/HTML_Reports") else NULL
)
})
---
```{r setup, include=FALSE}
# clear global enviroment
rm(list = setdiff(ls(), c("data.path", "user")))
invisible({gc()})
# initiate timer
start.time <- proc.time()
# list of packages to load
packages2load <- c('viridis', 'scMiko', 'ggpubr', 'dplyr', 'ggthemes', 'WGCNA', 'cowplot', 'schex', 'Seurat', 'sctransform', 'plyr', 'tidyr', 'reshape2', 'Matrix', 'RColorBrewer', 'ggplot2', 'gridExtra', 'DT', 'flexdashboard', 'future', 'gplots', 'reticulate', 'glmGamPoi')
# load packages
invisible(lapply(packages2load, library, character.only = TRUE, quietly = T))
```
```{r c2 - parameter_specification}
# specify parameters
parameter.list <- list(
# input parameters
input.object = "C:/Users/Owner/Dropbox/PDF Projects - JM/Data/scRNA-seq/01_sci-RNA-seq3_Hong_Kevin_Jason/NM_HH/Data/Preprocessed_Datasets/R6_M02_BC2_allGBM_271020.Rdata",
input.cell_ranger = NULL,
input.matrix = NULL,
# output parameters
save.flag = T,
output.object.directory = "Preprocessed_Datasets/",
output.object.file = "murine_allGBM_reboot_170322.Rdata",
# analysis parameters
subsample.n = NA,
cluster.resolution = 1,
gene.upperlimit = 9000,
gene.lowerlimit = 200,
mt.upperlimit = 10,
unmatched.rate.filter.flag = T,
vars2regress = c("percent.mt"),
correct.artifact = T,
feature.select.method = "hvg", # options: hvg (recommended), deviance
scale.method = "sct", # option: "sct" (recommended), "null_residuals" or "nfs"
normalization_method_ps = "SCT", # "NFS" or "SCT"
pca.method = "pca", # option: pca (recommended), glmpca
pca.component.select.method = "cum_var", # options" cum_var, elbow
pca.var.threshold = 0.9,
conserve.memory = T,
# specify species
species.filter.flag = F,
species.include = "Mm", # required; options: Hs, Mm
# miscillaneous parameters
nworkers = 1,
save.pdf = F,
print.inline = F,
developer = F
)
```
```{r developer input}
# developer specific input formats
do.dev <- parameter.list$developer
if (do.dev){
parameter.list$which.data = c("pilot14.Hs") # c("16-O", "17-O")
parameter.list$which.strata = "PR" # NA
parameter.list$update.log = T
which.data <- parameter.list$which.data # REQUIRED
if ("which.strata" %in% names(parameter.list)){
which.strata <- parameter.list$which.strata # NA if no stratification
} else {
which.strata <- NA
}
if (!exists("data.path") & !exists("user")) {
stop("data.path and user variables do not exist in global enviroment. Ensure that these are specified in .Rprofile. If specified, try restarting Rstudio.")
}
# get input data files paths
inputDataSpecification <- function(inputs = paste0(data.path,"/Raw_Datasets/pilots/", "M01_inputs.csv"),
subsets = paste0(data.path,"/Raw_Datasets/pilots/", "M01_subsets.csv")){
input.data <- read.csv(inputs, stringsAsFactors = F)
colnames(input.data) <- rmvCSVprefix(colnames(input.data))
input.data <- apply(input.data, 2, function(x) gsub("Â", "", x))
input.data <- as.data.frame(input.data)
# get subsets
input.subset <- read.csv(subsets, stringsAsFactors = F)
# wrangle into list
u.samples <- unique(input.subset$sample)
input.barcode.strata <- list()
for (i in 1:length(u.samples)){
subset.strata <- input.subset[input.subset$sample %in% u.samples[i], ]
current.strata <- list()
for (j in 1:nrow(subset.strata)){
current.barcodes <- subset.strata$barcodes[j]
current.barcodes <- strsplit(current.barcodes, ", ")[[1]]
current.strata[[subset.strata$set[j]]] <- current.barcodes
}
input.barcode.strata[[u.samples[i]]] <- current.strata
}
return(list(
input.data = input.data,
input.barcode.strata = input.barcode.strata
))
}
input.list <- inputDataSpecification()
input.data <- input.list$input.data
input.barcode.strata <- input.list$input.barcode.strata
# specify input
stopifnot(all(which.data %in% as.vector(input.data$sample)))
specified.input <- input.data[as.vector(input.data$sample) %in% which.data, ]
# get input data specifications
input_data <- as.vector(specified.input$files)
input_pcr_barcodes <- as.vector(specified.input$pcr.barcodes)
input_rt_barcodes <- as.vector(specified.input$rt.barcodes)
input_set <- c(input_data, input_pcr_barcodes, input_rt_barcodes)
input.type <- as.vector(specified.input$input.type)
all_input_organisms <- as.vector(specified.input$species)
dir <- as.vector(specified.input$directory)
# ensure correct path is used for data input
if (exists("data.path")){
dir <- paste0(data.path, dir)
}
# ensure regression variables are correctly specified
if (length(which.data) == 1){
parameter.list$vars2regress <- parameter.list$vars2regress[!(parameter.list$vars2regress %in% "dataset")]
}
# get stratification details
if (!is.na(which.strata)){
which.strata.specified <- which.strata
which.strata <- c()
for (i in 1:length(which.strata.specified)){
which.strata <- c(which.strata, input.barcode.strata[[which.data[i]]][[which.strata.specified[i]]])
}
}
# ensure correct directory is specified
stopifnot(exists("input.type"))
# ensure input data are specified
stopifnot(exists("input_set"))
try({
parameter.list$species.include <- rep(parameter.list$species.include, length(input_data))
}, silent = T)
# if set_names are not specified, set_names <- ""
if (exists("parameter.list$set_names")){
stopifnot(is.character(parameter.list$set_names))
} else {parameter.list$set_names <- ""}
# if pilot input are not specified, pilot_input <- ""
if (exists("pilot_input")){
stopifnot(length(pilot_input) == 1)
} else {pilot_input <- ""}
total_sets <- input_set
} else {
parameter.list$which.data = NA
parameter.list$which.strata = NA
all_input_organisms <- NULL
# input.type <- NA
dir <- ""
which.data <- parameter.list$which.data # REQUIRED
if ("which.strata" %in% names(parameter.list)){
which.strata <- parameter.list$which.strata # NA if no stratification
} else {
which.strata <- NA
}
}
```
```{r specify input pathways}
if (!exists("input.type")){
if ("input.object" %in% names(parameter.list)){
if (!is.na(parameter.list$input.object) && !is.null(parameter.list$input.object)){
input_data <- total_sets <- parameter.list$input.object
input.type <- 0
}
}
if ("input.cell_ranger" %in% names(parameter.list)){
if (!is.na(parameter.list$input.cell_ranger) && !is.null(parameter.list$input.cell_ranger)){
input_data <- total_sets <- parameter.list$input.cell_ranger
input.type <- 2
}
}
if ("input.matrix" %in% names(parameter.list)){
if (!is.na(parameter.list$input.matrix) && !is.null(parameter.list$input.matrix)){
input_data <- total_sets <- parameter.list$input.matrix
input.type <- 3
}
}
}
```
```{r c4 - analysis log}
df.log <- initiateLog("Preprocessing & QC")
if (input.type == 1){
df.log <- addLogEntry("Input Data (.Rdata)", input_data, df.log, "input_data")
df.log <- addLogEntry("Input RT Barcode (.txt)", input_rt_barcodes, df.log, "input_rt_barcodes")
df.log <- addLogEntry("Input PCR Barcode (.txt)", input_pcr_barcodes, df.log, "input_pcr_barcodes")
} else if (input.type == 2){
df.log <- addLogEntry("Input Cell Ranger", input_data, df.log, "input_data")
} else if (input.type == 3){
df.log <- addLogEntry("Input Expression Matrix", input_data, df.log, "input_data")
} else if (input.type == 0){
df.log <- addLogEntry("Seurat object", input_data, df.log, "input_data")
}
df.log <- addLogEntry("gene/cell Upper Limit (N/cell)",
as.character(parameter.list$gene.upperlimit), df.log, "gene.upperlimit")
df.log <- addLogEntry("gene/cell Lower Limit (N/cell)",
as.character(parameter.list$gene.lowerlimit), df.log, "gene.lowerlimit")
df.log <- addLogEntry("Filtered by unmatched reads",
as.character(parameter.list$unmatched.rate.filter.flag), df.log, "unmatched.rate.filter.flag")
df.log <- addLogEntry("Mitochondrial Content Upper Limit (%/cell)",
as.character(parameter.list$mt.upperlimit), df.log, "mt.upperlimit")
if ("subsample_factor" %in% names(parameter.list)){
if (parameter.list$subsample_factor == 1){subsampled <- FALSE} else {subsampled <- TRUE}
} else {
parameter.list$subsample_factor <- 1
subsampled <- FALSE
}
df.log <- addLogEntry("Data Subsampled", subsampled, df.log, "subsampled")
df.log <- addLogEntry("Cluster Resolution", as.character(parameter.list$cluster.resolution),
df.log, "cluster.resolution")
df.log <- addLogEntry("Filtered by species",
as.character(parameter.list$species.filter.flag), df.log, "species.filter.flag")
if (parameter.list$species.filter.flag) {
df.log <- addLogEntry("Species Included",
as.character(parameter.list$species.include), df.log, "species.include")
}
df.log <- addLogEntry("Number of Workers", as.character(parameter.list$nworkers), df.log, "normScale")
if (!is.na(which.strata)) {
df.log <- addLogEntry("Barcode(s) Included", which.strata, df.log, "which.strata")
}
df.log <- addLogEntry("Variables regressed out",
as.character(parameter.list$vars2regress), df.log, "vars2regress")
df.log <- addLogEntry("Input Species", all_input_organisms, df.log, "all_input_organisms")
df.log <- addLogEntry("Feature selection method",
parameter.list$feature.select.method, df.log, "feature.select.method")
df.log <- addLogEntry("PCA component selection method",
parameter.list$pca.component.select.method, df.log, "pca.component.select.method")
```
```{r c6 - load_data}
# Run import function
so.list <- list()
gNames.list.list <- list()
# sequence indices
idx.seq <- c(1, 1+length(input_data), 1+(2*length(input_data)))
# assertions
stopifnot(length(dir) == length(input_data))
for (i in 1:length(input_data)){
if (input.type[i] == 0){
if (grepl(".rds", input_data[i])){
output.list <- list(so = readRDS(file = input_data[i]),
gnames = NULL)
output.list$so <- CreateSeuratObject(counts = output.list[["so"]]@assays[["RNA"]]@counts,
meta.data = output.list[["so"]]@meta.data)
if (!("Barcode" %in% colnames(output.list$so@meta.data))) output.list$so@meta.data$Barcode = "unspecified"
if (!("Species" %in% colnames(output.list$so@meta.data))) output.list$so@meta.data$Species = "unspecified"
names(output.list) <- c("object","genes")
} else if (grepl(".Rdata", input_data[i])){
load(file = input_data[i])
if (exists("so")){
output.list <- list()
output.list$so <- CreateSeuratObject(counts = so@assays[["RNA"]]@counts,
meta.data = so@meta.data)
output.list$gnames = NULL
if (!("Barcode" %in% colnames(output.list$so@meta.data))) output.list$so@meta.data$Barcode = "unspecified"
if (!("Species" %in% colnames(output.list$so@meta.data))) output.list$so@meta.data$Species = "unspecified"
names(output.list) <- c("object","genes")
try({rm(so)}, silent = T); invisible({gc()})
} else {
stop("If importing seurat object from .Rdata file, seurat object must be named 'so'.")
}
} else {
stop("Input format is not recognized.")
}
} else {
cur.total_sets <- total_sets[idx.seq + (i-1)]
io <- NULL
try({
io <- stringr::str_trim(unlist(strsplit(all_input_organisms[i], ",")))
}, silent = T)
if (input.type[i] == 1) {
output.list <- loadMoffat(import_set = cur.total_sets,
subsample_factor = parameter.list$subsample_factor,
input_organisms = io ,
organism_include = parameter.list$species.include[i],
dir = dir[i])
} else if (input.type[i] == 2){
output.list <- loadCellRanger(import_set = cur.total_sets,
input_organisms = io,
dir = dir[i])
} else if (input.type[i] == 3){
output.list <- loadMat(file = cur.total_sets,
dir = dir[i])
}
}
# unlist outputs
so.list[[i]] <- output.list[["object"]]
if ("genes" %in% names(output.list)){
gNames.list.list[[i]] <- output.list[["genes"]]
} else {
gNames.list.list[[i]] <- prepGeneList(so = so.list[[i]], NULL)
}
if (all((unique(so.list[[i]]@meta.data[["Barcode"]]) == "unspecified") )){
so.list[[i]]@meta.data[["Barcode"]] <- paste("T", i, sep = "")
} else if (all(is.na(unique(so.list[[i]]@meta.data[["Barcode"]])))){
stop("Barcodes incorrectly specified")
}
}
# assign set labels and merge seurat objects if multiple present
for (i in 1:length(so.list)){
if (length(which.data) == length(which.strata)){
set.label <- paste0(which.data[i],"_", which.strata[i])
} else {
set.label <- which.data[i]
}
so.list[[i]][["dataset"]] <- set.label
}
so <- mergeSeuratList(so.list)
rm(so.list);
rm(output.list);
invisible({gc()})
# consolidate gene lists
try({
df.gname <- unique(bind_rows(lapply(gNames.list.list, function(x) data.frame(ensembl = names(x), gene = as.character(x)))))
gNames.list <- df.gname$gene
names(gNames.list) <- df.gname$ensembl
so@misc[["g2e"]] <- gNames.list
}, silent = T)
```
```{r c8 - filter_data_by_subset, include = FALSE}
# define subset parameters
if ("species.include" %in% names(parameter.list)){
if (sum(c("Mm", "Hs") %in% parameter.list$species.include) > 0){
subset.df <- data.frame(field = "Species", subgroups = parameter.list$species.include)
}
# subset data
if (sum(parameter.list$species.include %in% so@meta.data$Species) > 0){
so <- subsetSeurat(so, subset.df)
}
}
if ("subsample.n" %in% names(parameter.list)){
so <- downsampleSeurat(object = so, subsample.n = parameter.list$subsample.n)
}
miko_message(paste0(ncol(so), " cells available for preprocessing..."))
```
```{r c9 - subset barcodes, include = FALSE}
if (!is.na(which.strata)) so <- barcodeLabels(so, which.strata)
```
```{r c10 - get_mt_content, include = FALSE}
# MITOCHONDRIAL CONTENT
so <- getMitoContent(so, gNames = so@misc[["g2e"]])
```
```{r c11 - barcode rankings, fig.width= 15, fig.height=5, include = FALSE}
# get metadata
df.meta.qc <- so@meta.data
# rank QC metrics
df.meta.qc$rank.umi <- rank(-df.meta.qc$nCount_RNA)
df.meta.qc$rank.gene <- rank(-df.meta.qc$nFeature_RNA)
df.meta.qc$rank.mt <- rank(-df.meta.qc$percent.mt)
df.meta.qc$mitoOmit <- df.meta.qc$percent.mt > parameter.list$mt.upperlimit
df.meta.qc$mitoColor <- "black"
df.meta.qc$mitoColor[df.meta.qc$mitoOmit] <- "tomato"
line.col <- '#2C3E50'
plt.gene.umi.mito <- df.meta.qc %>%
dplyr::arrange(mitoOmit) %>%
ggplot(aes(x = nCount_RNA, y = (nFeature_RNA))) +
geom_point(aes(color = mitoOmit)) +
scale_y_continuous(trans='log10') +
scale_x_continuous(trans='log10') +
geom_hline(yintercept = parameter.list$gene.upperlimit , color = line.col, linetype = "dashed") +
geom_hline(yintercept = parameter.list$gene.lowerlimit, color = line.col, linetype = "dashed") +
xlab("Transcript counts (n/cell)") + ylab("Gene counts (n/cell)") +
theme_classic() + labs(title = "Transcript vs Gene Counts", caption = "dashed line: gene count thresholds\norange points: high mt content.") +
theme_miko(color.palette = "flatly3", center.title = T)
# generate ranking plots
plt.umi.rank.mito <- df.meta.qc %>%
dplyr::arrange(mitoOmit) %>%
ggplot(aes(x = rank.umi, y = (nCount_RNA))) +
geom_point(aes(color = mitoOmit)) +
scale_y_continuous(trans='log10') +
xlab("Cell Rank") + ylab("Transcript counts (n/cell)") +
theme_classic() + labs(title = "Transcript count ranking", caption = "orange points: high mt content.") +
theme_miko(color.palette = "flatly3", center.title = T)
plt.gene.rank.mito <- df.meta.qc %>%
dplyr::arrange(mitoOmit) %>%
ggplot(aes(x = rank.gene, y = (nFeature_RNA))) +
geom_point(aes(color = mitoOmit)) + scale_y_continuous(trans='log10') +
geom_hline(yintercept = parameter.list$gene.upperlimit , color = line.col, linetype = "dashed") +
geom_hline(yintercept = parameter.list$gene.lowerlimit, color = line.col, linetype = "dashed") +
xlab("Cell Rank") + ylab("Gene counts (n/cell)") +
theme_classic() + labs(title = "Gene count ranking", caption = "dashed line: gene count thresholds\norange points: high mt content.") +
theme_miko(color.palette = "flatly3", center.title = T)
plt.mt.rank.mito <- df.meta.qc %>%
dplyr::arrange(mitoOmit) %>%
ggplot(aes(x = rank.mt, y = (percent.mt))) +
geom_point(aes(color = mitoOmit)) + scale_y_continuous(trans='log10') +
geom_hline(yintercept = parameter.list$mt.upperlimit, color = line.col, linetype = "dashed") +
xlab("Cell Rank") + ylab("Mitochondrial Content (%/cell)") +
theme_classic() + labs(title = "Mitochondrial content ranking", caption = "orange: high mt content.") +
theme_miko(color.palette = "flatly3", center.title = T)
if (input.type == 1){
unmatch.median <- median(df.meta.qc$unmatched.rate)
unmatch.mad <- mad(df.meta.qc$unmatched.rate, unmatch.median)
if (parameter.list$unmatched.rate.filter.flag){
unmatch.high <- unmatch.median + (1.96*unmatch.mad)
unmatch.low <- unmatch.median - (1.96*unmatch.mad)
} else {
unmatch.high <- 1
unmatch.low <- 0
}
df.meta.qc$matchOmit <- (df.meta.qc$unmatched.rate > unmatch.high) | (df.meta.qc$unmatched.rate < unmatch.low)
# df.meta.qc$matchColor <- "black"
# df.meta.qc$matchColor[df.meta.qc$matchOmit] <- "skyblue"
plt.unmatch.umi <- df.meta.qc %>%
dplyr::arrange(matchOmit) %>%
ggplot(aes(x = nCount_RNA, y = (unmatched.rate))) +
geom_point(aes(color = matchOmit)) +
xlab("Transcript Count") + ylab("Unmatched Rate") +
geom_hline(yintercept = unmatch.high, color = line.col, linetype = "dashed") +
geom_hline(yintercept = unmatch.low, color = line.col, linetype = "dashed") +
theme_classic() + labs(title = "Transcript count vs. unmatched rate", caption = "orange: unmatched rate outliers") +
theme_miko(color.palette = "flatly3", center.title = T)
df.meta.qc$unmatch.rank <- rank(-df.meta.qc$unmatched.rate)
plt.umatch.rank <- df.meta.qc %>%
dplyr::arrange(matchOmit) %>%
ggplot(aes(x = unmatch.rank, y = (unmatched.rate))) +
geom_point(aes(color = matchOmit)) +
scale_y_continuous(trans='log10') +
geom_hline(yintercept = unmatch.high, color = line.col, linetype = "dashed") +
geom_hline(yintercept = unmatch.low, color = line.col, linetype = "dashed") +
xlab("Cell Rank") + ylab("Unmatched Rate") +
theme_classic() + labs(title = "Unmatched rate ranking", caption = "orange: unmatched rate outliers") +
theme_miko(color.palette = "flatly3", center.title = T)
plt.gene.umi.match <- df.meta.qc %>%
dplyr::arrange(matchOmit) %>%
ggplot(aes(x = nCount_RNA, y = (nFeature_RNA))) +
geom_point(aes(color = matchOmit)) +
scale_y_continuous(trans='log10') +
scale_x_continuous(trans='log10') +
geom_hline(yintercept = parameter.list$gene.upperlimit , color = line.col, linetype = "dashed") +
geom_hline(yintercept = parameter.list$gene.lowerlimit, color = line.col, linetype = "dashed") +
xlab("Transcript counts (n/cell)") + ylab("Gene counts (n/cell)") +
theme_classic() + labs(title = "Transcript vs Gene Counts", caption ="orange: unmatched rate outliers") +
theme_miko(color.palette = "flatly3", center.title = T)
# generate ranking plots
plt.umi.rank.match <- df.meta.qc %>%
dplyr::arrange(matchOmit) %>%
ggplot(aes(x = rank.umi, y = (nCount_RNA))) +
geom_point(aes(color = matchOmit)) +
scale_y_continuous(trans='log10') +
xlab("Cell Rank") + ylab("Transcript counts (n/cell)") +
theme_classic() + labs(title = "Transcript count ranking", caption = "orange: unmatched rate outliers") +
theme_miko(color.palette = "flatly3", center.title = T)
plt.gene.rank.match <- df.meta.qc %>%
dplyr::arrange(matchOmit) %>%
ggplot(aes(x = rank.gene, y = (nFeature_RNA))) +
geom_point(aes(color = matchOmit)) + scale_y_continuous(trans='log10') +
geom_hline(yintercept = parameter.list$gene.upperlimit , color = line.col, linetype = "dashed") +
geom_hline(yintercept = parameter.list$gene.lowerlimit, color = line.col, linetype = "dashed") +
xlab("Cell Rank") + ylab("Gene counts (n/cell)") +
theme_classic() + labs(title = "Gene count ranking", caption = "orange: unmatched rate outliers") +
theme_miko(color.palette = "flatly3", center.title = T)
plt.mt.rank.match <- df.meta.qc %>%
dplyr::arrange(matchOmit) %>%
ggplot(aes(x = rank.mt, y = (percent.mt))) +
geom_point(aes(color = matchOmit)) + scale_y_continuous(trans='log10') +
geom_hline(yintercept = parameter.list$mt.upperlimit, color = line.col, linetype = "dashed") +
xlab("Cell Rank") + ylab("Mitochondrial Content (%/cell)") +
theme_classic() + labs(title = "Mitochondrial content ranking", caption = "orange: unmatched rate outliers") +
theme_miko(color.palette = "flatly3", center.title = T)
} else {
plt.umatch.rank <- NULL
plt.unmatch.umi <- NULL
unmatch.high <- 1
unmatch.low <- 0
}
if (parameter.list$print.inline) {
cowplot::plot_grid(plt.gene.umi.mito, plt.umi.rank.mito, plt.gene.rank.mito, plt.mt.rank.mito, ncol = 4, align = "h")
if (input.type == 1){
ggpubr::ggarrange(plt.umi.rank.match, plt.gene.rank.match, plt.mt.rank.match,
plt.unmatch.umi, plt.gene.umi.match, plt.umatch.rank,ncol = 3, nrow = 2)
}
}
```
```{r c12 - Density plot of % mitochondrial genes, warning = FALSE, include = FALSE}
# MITOCHONDRIAL CONTENT DISTRIBUTIONS
calc_mito_content_by_bc <- function(so, set_name, bc_info_flag, input.type = 1){
if (bc_info_flag == TRUE & input.type != 2){
# Compute mitochrondrial content for each cell type
# get unique cell types
barcodes <-unique(so@meta.data[["Barcode"]])
# create dataframe to store mitochondrial content, stratified by cell type
df_by_bc <- NULL
# for each cell type
for (i in 1:length(barcodes)) {
percent.mt_by_bc <- as.vector(so$percent.mt[so$Barcode == barcodes[i]])
count.feature_by_bc <- as.vector(so$nFeature_RNA[so$Barcode == barcodes[i]])
count.rna_by_bc <- as.vector(so$nCount_RNA[so$Barcode == barcodes[i]])
n_cells <- length(percent.mt_by_bc)
cur_length <- dim(df_by_bc)[1]
# get QC parameters for each cell sample
df_by_bc <- bind_rows(
df_by_bc,
data.frame(barcode = barcodes[i],
percent.mt = percent.mt_by_bc,
count.feature = count.feature_by_bc,
count.rna = count.rna_by_bc)
)
}
} else { # if no cell type information provided, pool everything together
df_by_bc <- data.frame()
percent.mt_by_bc <- as.vector(so$percent.mt)
count.feature_by_bc <- as.vector(so$nFeature_RNA)
count.rna_by_bc <- as.vector(so$nCount_RNA)
df_by_bc <- data.frame(percent.mt_by_bc, count.feature_by_bc, count.rna_by_bc)
colnames(df_by_bc) <- c("percent.mt", "count.feature", "count.rna")
df_by_bc["barcode"] <-"unspecified"
barcodes <- "unspecified"
so@meta.data[["Barcode"]] <- "unspecified"
}
# Create summary graphs
# plot density plot
plt.handle <- ggplot(df_by_bc, aes(percent.mt, color=barcode)) +
geom_density(size = 1) +
ggtitle(paste(set_name)) +
xlab("Mitochrondrial Content (%)")
output <- list(so, plt.handle)
return(output)
}
# calculate mitochondrial content, stratified by barcode (if available)
output_by_bc <- calc_mito_content_by_bc(so, parameter.list$set_names, (length(total_sets)>1), input.type)
# retrive results
so <- output_by_bc[[1]]
plt.mito_by_bc <- output_by_bc[[2]]
# show mito filter threhsold
if ("mt.upperlimit" %in% names(parameter.list)){
if (parameter.list$mt.upperlimit >=0 & parameter.list$mt.upperlimit <= 100){
plt.mito_by_bc <- plt.mito_by_bc + geom_vline(xintercept = parameter.list$mt.upperlimit, linetype = "dashed")
}
}
try({
if (ulength(so$Barcode) == 1){
plt.mito_by_bc <- plt.mito_by_bc + theme_miko(color.palette = "flatly2")
}
}, silent = T)
if (parameter.list$print.inline){
print(plt.mito_by_bc)
}
```
```{r c13 - qc_violin_plots, fig.height= 5, fig.width = 12, include = FALSE}
if (length(unique(so@meta.data[["subset_group"]]))> 1){
df.qc <- data.frame(so@meta.data[["subset_group"]],
so@meta.data[["nFeature_RNA"]],
so@meta.data[["nCount_RNA"]],
so@meta.data[["percent.mt"]])
colnames(df.qc) <- c("group", "nFeature_RNA", "nCount_RNA", "percent.mt")
df.qc_sum <- df.qc %>%
group_by(group) %>%
summarize(nFeature_mean = round(mean(nFeature_RNA),2),
nCount_mean = round(mean(nCount_RNA),2),
percent.mt_mean = round(mean(percent.mt),2),
nFeature_median = round(median(nFeature_RNA),2),
nCount_median = round(median(nCount_RNA),2),
percent.mt_median = round(median(percent.mt),2))
nFeat_aov <- summary(aov(nFeature_RNA ~ group, data = df.qc))
nCount_aov <- summary(aov(nCount_RNA ~ group, data = df.qc))
mT_aov <- summary(aov(percent.mt ~ group, data = df.qc))
} else {
df.qc <- data.frame()
df.qc_sum <- data.frame()
nFeat_aov <- c()
nCount_aov <- c()
mT_aov <- c()
}
plt.QC_violin <- tryCatch({
violin.list <- QC.violinPlot(so, plt.log.flag = T, cols = alpha('#2C3E50', alpha = 0.5))
cowplot::plot_grid(violin.list[[1]])
}, error = function(e){
violin.list <- QC.violinPlot(so, plt.log.flag = T)
return(cowplot::plot_grid(violin.list[[1]]))
})
if (parameter.list$print.inline){
print(plt.QC_violin)
}
```
```{r c14 - QC scatterplots, fig.height= 5, fig.width = 12, include = FALSE}
# Generate QC scatterplots
max.cells <- 40000
if (ncol(so) > max.cells) {
plt.QC_scatter <- QC.scatterPlot(downsampleSeurat(so, subsample.n = max.cells))
} else {
plt.QC_scatter <- QC.scatterPlot(so)
}
if (parameter.list$print.inline) {
print(plt.QC_scatter)
}
```
```{r c15 - filter_QC, fig.width = 5, fig.height = 5, include = FALSE}
if (length(parameter.list$set_names) == 2) {
set.n <- NULL
} else {
set.n <- parameter.list$set_names
}
filter_output_list <- filterSeurat(so = so,
RNA.upperlimit = parameter.list$gene.upperlimit,
RNA.lowerlimit = parameter.list$gene.lowerlimit,
mt.upperlimit = parameter.list$mt.upperlimit,
unmatch.low = unmatch.low,
unmatch.high = unmatch.high,
set_names = set.n)
so <- filter_output_list[["seurat"]]
plt.filter_pre_post <- filter_output_list[["plot"]]
plt.filter_pre_post <- plt.filter_pre_post + theme_miko(fill.palette = "flatly2", legend = T)
filter.breakdown.list <- filter_output_list[["filter.breakdown"]]
rm(filter_output_list)
# colfunc <- colorRampPalette(c('#18BC9C','#2C3E50','#F39C12'))
plt.filter.venn <- NULL
try({
plt.filter.venn <- ggVennDiagram::ggVennDiagram(filter.breakdown.list) +
scale_color_manual(values = rep("white", length(filter.breakdown.list))) +
scale_fill_gradient(low = "#F4FAFE", high = '#2C3E50')
}, silent = T)
if (parameter.list$print.inline){
print(plt.filter.venn)
print(plt.filter_pre_post)
}
```
```{r c16 - normalize_data, warning = FALSE, include = FALSE}
# Run data normalization
if ("conserve.memory" %in% names(parameter.list) ){
if (is.logical(parameter.list$conserve.memory)){
conserve.memory <- parameter.list$conserve.memory
} else {
conserve.memory <- F
}
} else {
conserve.memory <- F
}
enable.parallelization <- F
if (parameter.list$nworkers == 1) enable.parallelization <- F
parameter.list$clip.range <- c(-sqrt(x = ncol(x = so[["RNA"]])/30),
sqrt(x = ncol(x = so[["RNA"]])/30))
max.memory <- 200 # numeric, in terms of Gb
# ensure regression variables have non-zero variance
cov <- parameter.list$vars2regress
df.meta <- so@meta.data
cov <- cov[cov %in% colnames(df.meta)]
cov.regress <- c()
if (length(cov) > 0){
for (i in 1:length(cov)){
x <- unlist(df.meta[ , cov[i]])
if (is.numeric(x)){
if (var(x) > 0){
cov.regress <- c(cov.regress, cov[i])
}
} else {
cov.regress <- c(cov.regress, cov[i])
}
}
}
parameter.list$vars2regress <- cov.regress
so <- tryCatch({
scNormScale(object = so,
method = parameter.list$normalization_method_ps,
vars2regress = parameter.list$vars2regress,
n.workers = parameter.list$nworkers,
max.memory = (max.memory*20480/20 * 1024^2),
enable.parallelization = enable.parallelization,
conserve.memory = conserve.memory,
clip.range = parameter.list$clip.range)
}, error = function(e){
if (!conserve.memory){
conserve.memory <- T
is.success <- F
try({
so <- scNormScale(object = so,
method = parameter.list$normalization_method_ps,
vars2regress = parameter.list$vars2regress,
n.workers = parameter.list$nworkers,
max.memory = (max.memory*20480/20 * 1024^2),
enable.parallelization = enable.parallelization,
conserve.memory = conserve.memory,
clip.range = parameter.list$clip.range)
is.success <- T
}, silent = T)
if (!is.success){
parameter.list$normalization_method_ps <- "NFS" # "NFS"
so <- scNormScale(object = so,
method = parameter.list$normalization_method_ps,
vars2regress = parameter.list$vars2regress,
n.workers = parameter.list$nworkers,
max.memory = (max.memory*20480/20 * 1024^2),
enable.parallelization = enable.parallelization,
conserve.memory = conserve.memory,
clip.range = parameter.list$clip.range)
}
} else {
return(NULL)
}
})
if (is.null(so)) stop("Failed to normalized data. Troubleshooting required.")
nassay <- DefaultAssay(so)
var.gene.orig <- so@assays[[nassay]]@var.features
if (nassay == "RNA"){
parameter.list$normalization_method_ps = "NFS"
parameter.list$scale.method <- "nfs"
} else if (nassay == "SCT"){
parameter.list$normalization_method_ps = "SCT"
parameter.list$scale.method <- "sct"
}
```
```{r c17 - per cell statistics, include = FALSE}
# ptm <- proc.time()
df_nCount <- data.frame(so@meta.data[["nCount_RNA"]], so@meta.data[[paste0("nCount_", nassay)]])
colnames(df_nCount) <- c("UMI/cell (raw)", "UMI/cell (normalized)")
df_nCount_m <- melt(df_nCount)
df_nFeat <- data.frame(so@meta.data[["nFeature_RNA"]], so@meta.data[[paste0("nFeature_", nassay)]])
colnames(df_nFeat) <- c("genes/cell (raw)","genes/cell (normalized)")
df_nFeat_m <- melt(df_nFeat)
plt.UMI_per_cell <- ggplot(df_nCount_m, aes(x = value, fill = variable)) +
geom_density(alpha = 0.3) + scale_x_log10()+ xlab("UMI/cell") +
theme_miko(fill.palette = "flatly", legend= T)
plt.genes_per_cell <- ggplot(df_nFeat_m, aes(x = value, fill = variable)) +
geom_density(alpha = 0.3) + scale_x_log10()+ xlab("genes/cell") +
theme_miko(fill.palette = "flatly", legend= T)
plt.pre_post_norm <- tryCatch({
plt.pre_post_1 <- FeatureScatter(so, feature1 = "nCount_RNA", feature2 = paste0("nCount_", nassay)) +
xlab("UMI/cell (raw)") + ylab("UMI/cell (normalized)") +
theme_miko(color.palette = "flatly2", legend= T)
plt.pre_post_2 <- FeatureScatter(so, feature1 = "nFeature_RNA", feature2 = paste0("nFeature_", nassay)) +
xlab("Genes/cell (raw)") + ylab("Genes/cell (normalized)") +
theme_miko(color.palette = "flatly2", legend= T)
CombinePlots(plots = list(plt.pre_post_1, plt.pre_post_2), ncol = 2, legend = 'none')
}, error = function(e){
plt.pre_post_1 <- FeatureScatter(so, feature1 = "nCount_RNA", feature2 = paste0("nCount_", nassay)) +
xlab("UMI/cell (raw)") + ylab("UMI/cell (normalized)") +
theme_miko(legend= T)
plt.pre_post_2 <- FeatureScatter(so, feature1 = "nFeature_RNA", feature2 = paste0("nFeature_", nassay)) +
xlab("Genes/cell (raw)") + ylab("Genes/cell (normalized)") +
theme_miko(legend= T)
return(CombinePlots(plots = list(plt.pre_post_1 + theme(text = element_text(size = 15)) ,
plt.pre_post_2 + theme(text = element_text(size = 15)) ), ncol = 2, legend = 'none'))
}, silent = T)
if (parameter.list$print.inline){
print(plt.UMI_per_cell)
print(plt.genes_per_cell)
print(plt.pre_post_norm)
}
```
```{r deviance feature selection, include = FALSE}
# feature selection using deviance #############################################
# get deviant features using subset of data ####################################
if (parameter.list$feature.select.method == "deviance"){
miko_message("Performing feature selection using deviance scores...")
DefaultAssay(so) <- nassay
so.sub <- downsampleSeurat(object = so, subsample.n = 20000)
m <- GetAssayData(so.sub, slot = "data", assay = nassay)
m <- m[rownames(m) %in% rownames(so.sub), ]
m <- as.matrix(m)
batch.factor <- as.factor(so.sub@meta.data$Barcode)
devs <- scry::devianceFeatureSelection(object = m, batch = batch.factor, fam = "binomial")
df.dev <- data.frame(
gene = rownames(m),
d = devs,
is.var = rownames(m) %in% var.gene.orig
) %>% dplyr::arrange(-d)
df.dev$d.norm <- df.dev$d/sum(df.dev$d)