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M06_GeneExpressionAssociation_public.Rmd
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M06_GeneExpressionAssociation_public.Rmd
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---
title: "Gene Expression and Association"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
self_contained: true
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 load libraries, include=FALSE}
# clear global enviroment
rm(list = setdiff(ls(), c("data.path", "user")))
invisible({gc()})
# initiate timer
start.time <- proc.time()
# load packages
packages2load <- c("scMiko", "Seurat", "plyr", "dplyr", "tidyr", "reshape2", "grid", "plotly","DT", "flexdashboard", "ggpmisc", "future", "foreach", "doParallel","AnnotationDbi", "org.Mm.eg.db", "org.Hs.eg.db", "fgsea", "ggplot2", "reactome.db","schex", "RColorBrewer", "cowplot", "viridis", "viridisLite", "presto", "variancePartition", "lme4", "ggdendro", "heatmaply", "RISmed")
# load packages
invisible(lapply(packages2load, library, character.only = T, quietly = T))
```
```{r parameter specification}
# PARAMETER SPECIFICATION ######################################################
parameter.list <- list(
input.file = "output_34245PM_demo_zeisel_10000cell.Rdata",
features = NULL,
feature.format = 2, # 0 user-provided, 1 M06_features.csv, 2 discovery
feature.csv.path = NULL, # csv spreadsheet with features, called by column name. Required if feature.format = 1
discovery.method = "Wilcoxon", # options: Wilcoxon, CDI, Gini
discovery.pct.min = 0.05, # minimum expression fraction per group.
discovery.group.by = "seurat_clusters",
discovery.top.n = 10,
discovery.max.n = 40,
as.module = F,
# Correlation parameters #####################################################
cor.method = "cdi", # Options: 'cdi', 'spearman', 'pearson', 'rho_p'
cor.top.n = 100,
cor.which.data = "data",
cor.min.expr = 0.05, # min expressing fraction to be included in correlation
# General parameters #########################################################
general.cluster.resolution = 0.5, # Numeric [0, Inf]
general.subsample.factor = 1, # Numeric (0,1]
general.subset = "no.subset",
general.clean.clusters = T, # Logical.
general.save.pdf = F, # Logical.
general.rprofile.dir = F,
general.print.inline = F, # Logical.
general.barcode.recode = list(),
# General parameters #########################################################
expression.do.hex = F, # Logical. Specify whether to generate hex UMAPs (schex package)
n.workers = parallel::detectCores(),
developer = F
)
```
```{r assertions, include = FALSE}
# ensure all input specifications are met.
stopifnot(is.numeric(parameter.list$general.cluster.resolution))
stopifnot(parameter.list$general.cluster.resolution > 0)
stopifnot(is.numeric(parameter.list$general.subsample.factor))
stopifnot(parameter.list$general.subsample.factor > 0)
stopifnot(parameter.list$general.subsample.factor <= 1)
stopifnot(("data.frame" %in% class(parameter.list$general.subset)) || parameter.list$general.subset == "no.subset")
stopifnot(class(parameter.list$general.clean.clusters) == "logical")
stopifnot(class(parameter.list$general.save.pdf) == "logical")
stopifnot((class(parameter.list$general.barcode.recode)) == "list")
stopifnot(parameter.list$cor.method %in% c('pearson', 'spearman', 'rho_p', "cdi"))
stopifnot(is.numeric(parameter.list$cor.top.n))
stopifnot(parameter.list$cor.top.n > 0)
stopifnot(parameter.list$cor.which.data == "data")
stopifnot(class(parameter.list$expression.do.hex) == "logical")
```
```{r import appropriate csv files, include = FALSE}
if (parameter.list$feature.format==1){
gene.sets.type1 <- read.csv(parameter.list$feature.csv.path, header = TRUE, stringsAsFactors = F)
colnames(gene.sets.type1) <- rmvCSVprefix(colnames(gene.sets.type1))
}
```
```{r load data, warning = FALSE, include = FALSE}
# Specify data directories
if (parameter.list$general.rprofile.dir){
dir.preprocessed <- "Preprocessed_Datasets/"
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.")
}
}
miko_message("Importing data...")
if ((!grepl(".Rdata|.RData", parameter.list$input.file)) & !(grepl(".rds", parameter.list$input.file))){
parameter.list$input.file <- paste0(parameter.list$input.file, ".Rdata")
}
if (parameter.list$general.rprofile.dir){
if (grepl(".Rdata|.RData", parameter.list$input.file)){
load(paste(data.path, dir.preprocessed, parameter.list$input.file, sep = ""));
} else if (grepl(".rds", parameter.list$input.file)) {
so <- readRDS(paste(data.path, dir.preprocessed, parameter.list$input.file, sep = ""))
}
} else {
if (grepl(".Rdata|.RData", parameter.list$input.file)){
load(parameter.list$input.file);
} else if (grepl(".rds", parameter.list$input.file)) {
so <- readRDS(parameter.list$input.file);
}
}
if (!exists("gNames.list")) gNames.list <- prepGeneList(so, objects())
t2d <- c("ica", "tsne", "nmf", "corr", "gsva", "deg", "integration.anchors")
# prep seurat object
prep.list <- prepSeurat2(so, e2s = gNames.list,
species = parameter.list$general.species,
resolution= parameter.list$general.cluster.resolution,
subset.data = parameter.list$general.subset,
subsample = parameter.list$general.subsample.factor,
terms2drop = t2d, rmv.pattern = "so",
keep.default.assay.only = T)
# unpack results
if (exists("so")) try({rm(so)}, silent = T)
so.query <- prep.list$so
current.assay <- prep.list$assay
n.cells <- prep.list$n.cell
rm(prep.list)
invisible({gc()})
# clean clusters
if (parameter.list$general.clean.clusters) so.query <- cleanCluster(so.query, return.plots = F)
parameter.list$general.species <- detectSpecies(so.query)
```
```{r input subtype data specification, include = FALSE}
meta.module.helper <- function(do.module, default.set){
if (is.na(do.module)){
return(default.set)
} else if (do.module == TRUE) {
return(T)
} else if (do.module == FALSE) {
return(F)
}
}
features <- as.character(parameter.list$features)
if (parameter.list$feature.format==0){
markers_of_interest <- features
meta.module.flag <- meta.module.helper(parameter.list$as.module,FALSE)
} else if (parameter.list$feature.format==1){
stopifnot(features %in% names(gene.sets.type1))
markers_of_interest <- gene.sets.type1[[features]] # REQUIRED if feature.format == 1
meta.module.flag <- meta.module.helper( parameter.list$as.module,FALSE)
} else if ((parameter.list$feature.format==2)) {
meta.module.flag <- meta.module.helper( parameter.list$as.module,F)
features <- parameter.list$features <- c()
}
if (exists("markers_of_interest") && "data.frame" %in% class(markers_of_interest)){
markers_of_interest <- janitor::clean_names(markers_of_interest, "screaming_snake")
}
```
```{r recode barcodes, include = FALSE}
# recode barcodes ##############################################################
bc.list <- parameter.list$general.barcode.recode
df.meta <- so.query@meta.data
if (length(bc.list) == 0){
df.meta$bc <- df.meta$Barcode
} else {
df.meta$bc <- NA
for (i in 1:length(bc.list)){
df.meta$bc[grepl(bc.list[[i]], df.meta$Barcode)] <- names(bc.list)[i]
}
df.meta$bc[is.na(df.meta$bc)] <- "Other"
}
so.query@meta.data <- df.meta
```
```{r prepare query gene list (input type 1 or 2), miko_message=FALSE, warning=FALSE, include = FALSE}
importMarkers <- function(query.format, input.file = NULL, markers.of.interest = NULL, which.col = "first", which.species) {
stopifnot(which.col %in% c("first", "all"))
if (query.format == 1){
if (which.species == "Hs"){
markers.of.interest <- toupper(markers.of.interest)
} else {
markers.of.interest <- firstup(markers.of.interest)
}
marker_set_name <- ""
} else if (query.format == 2){
spreadsheet_format <- unlist(strsplit(input.file, '[.]'))[2]
if (spreadsheet_format == "csv") {
markers.of.interest <- read.csv(input.file, header = TRUE)
} else if (spreadsheet_format == "xlsx") {
markers.of.interest <- readxl::read.xlsx(input.file, header = TRUE)
}
marker_set_name <- colnames(markers.of.interest)
if (which.col == "first"){
spread_sheet_col <- 1
marker_set_name <- marker_set_name[spread_sheet_col]
markers.of.interest <- as.data.frame(as.vector(markers.of.interest[ , spread_sheet_col]))
colnames(markers.of.interest) <- marker_set_name
} else if (which.col == "all"){
marker_set_name <- ""
}
for (i in 1:dim(markers.of.interest)[2]){
if (which.species == "Hs"){
markers.of.interest[ ,i] <- toupper(as.vector(markers.of.interest[ ,i]))
} else {
markers.of.interest[ ,i] <- firstup(as.vector(markers.of.interest[ ,i]))
}
}
}
output <- list(markers.of.interest, marker_set_name)
names(output) <- c("markers.of.interest", "marker_set_name")
return(output)
}
miko_message("Preparing geneset(s)...")
# import marker sets
if (parameter.list$feature.format == 1){
output.markers <- importMarkers(query.format = parameter.list$feature.format,
markers.of.interest = markers_of_interest,
which.species = parameter.list$general.species)
} else if (parameter.list$feature.format == 0){
if (parameter.list$general.species == "Hs") {
markers_of_interest <- toupper(markers_of_interest)
} else if (parameter.list$general.species == "Mm") {
markers_of_interest <- firstup(markers_of_interest)
}
}
if (exists("output.markers")) markers_of_interest <- output.markers[[1]]
```
```{r prior log history, warning = FALSE, include = FALSE}
# get prior log history
cur.env <- objects()
module.logs <- cur.env[grep("^df.log_Module",cur.env)]
```
```{r define grouping var, warning = FALSE, include = FALSE}
grouping_var <- parameter.list$discovery.group.by
if (parameter.list$discovery.group.by == "seurat_clusters"){
group_name <- "Clusters"
} else {
group_name <- parameter.list$discovery.group.by
}
# determine unique groups
u_groups <- as.vector(unique(so.query@meta.data[[grouping_var]]))
# determine number of unique groups
n_group_members <- length(u_groups)
```
```{r analysis log, warning = FALSE, include = FALSE}
miko_message("Updating analysis logs...")
df.log <- initiateLog("Query Feature Exploration")
df.log <- addLogEntry("Query File", parameter.list$input.file, df.log, "input.file")
df.log <- addLogEntry("Gene Set Name", features, df.log, "features")
df.log <- addLogEntry("Compute geneset module score", meta.module.flag, df.log, "meta.module.flag")
df.log <- addLogEntry("Species", parameter.list$general.species, df.log, "general.species")
if ("data.frame" %in% class(parameter.list$general.subset)){
df.log <- addLogEntry("Which Subset Field", parameter.list$general.subset$field, df.log, "field")
df.log <- addLogEntry("Which Subset Groups", parameter.list$general.subset$subgroups, df.log, "subgroups")
} else if ("character" %in% class(parameter.list$general.subset)){
df.log <- addLogEntry("Which Subset Groups", parameter.list$general.subset, df.log, "subset.df")
}
df.log <- addLogEntry("Min expr. fraction for cor. analysis", parameter.list$cor.min.expr, df.log, "cor.min.expr")
df.log <- addLogEntry("Clusters cleaned", parameter.list$general.clean.clusters, df.log, "general.clean.clusters")
df.log <- addLogEntry("Cluster Resolution", parameter.list$general.cluster.resolution, df.log, "general.cluster.resolution")
df.log <- addLogEntry("Grouping Variable", group_name, df.log, "group_name")
df.log <- addLogEntry("N Groups", n_group_members, df.log, "n_group_members")
df.log <- addLogEntry("Top N correlations", parameter.list$corr.top.n, df.log, "corr.top.n")
df.log <- addLogEntry("subsample_factor", parameter.list$general.subsample.factor, df.log, "general.subsample.factor")
df.log <- addLogEntry("schex used for UMAPs", parameter.list$expression.do.hex, df.log, "expression.do.hex")
df.log <- addLogEntry("PDF saved", parameter.list$general.save.pdf, df.log, "general.save.pdf")
df.log <- addLogEntry("correlation data", parameter.list$cor.which.data , df.log, "cor.which.data")
```
```{r keep available markers, warning = FALSE, include = FALSE}
if (exists("markers_of_interest")){
if (is.character(markers_of_interest)){
which.available <- unlist(lapply(markers_of_interest, function(x){
x %in% rownames(so.query)
}))
genes.not.found <- markers_of_interest[!which.available]
try({genes.not.found <- genes.not.found[genes.not.found != ""]}, silent = T)
markers_of_interest <- unique(markers_of_interest[which.available])
markers_of_interest <- rmvCSVprefix(markers_of_interest)
if (length(markers_of_interest) == 0) stop("None of queried genes found in seurat object.")
} else if (is.data.frame(markers_of_interest)){
colnames(markers_of_interest) <- rmvCSVprefix(colnames(markers_of_interest))
}
}
```
```{r TYPE2 - prepare query gene list, warning = FALSE}
if (parameter.list$feature.format == 2){
top.n.deg <- parameter.list$discovery.top.n
max.n.deg <- parameter.list$discovery.max.n
ulg <- ulength(so.query@meta.data[ ,grouping_var])
maxpergrp <- round(max.n.deg/ulg)
if (maxpergrp < top.n.deg) top.n.deg <- maxpergrp
if (top.n.deg < 1) top.n.deg <- 1
if (toupper(parameter.list$discovery.method) == "WILCOXON"){
miko_message("Running differential gene analysis...")
deg.gene <- presto::wilcoxauc(so.query, group_by = grouping_var, seurat_assay = DefaultAssay(so.query))
deg.gene.top <- deg.gene %>%
dplyr::group_by(group) %>%
dplyr::top_n(top.n.deg, auc)
markers_of_interest <- unique(as.character(deg.gene.top$feature))
} else if (toupper(parameter.list$discovery.method) == "GINI"){
deg.gene <- findGiniMarkers(object = so.query, min.pct = parameter.list$discovery.pct.min,
group.by =grouping_var, verbose = T)
deg.gene.top <- deg.gene %>%
dplyr::group_by(group) %>%
dplyr::top_n(top.n.deg, ngini)
markers_of_interest <- unique(as.character(deg.gene.top$feature))
} else if (toupper(parameter.list$discovery.method) == "CDI"){
exprGene <- getExpressedGenes(object = so.query, min.pct = parameter.list$discovery.pct.min, group = grouping_var)
deg.gene <- findCDIMarkers(object = so.query, features.x = grouping_var, features.y = exprGene, ncell.subset = 5000,
n.workers = parameter.list$n.workers, verbose = T)
deg.gene.top <- deg.gene %>%
dplyr::group_by(feature.x) %>%
dplyr::mutate(rank.ncdi = rank(ncdi, ties.method = "random")) %>%
dplyr::top_n(top.n.deg, rank.ncdi)
markers_of_interest <- unique(as.character(deg.gene.top$feature.y))
}
miko_message(paste0(ulength(markers_of_interest), " genes identified using ", parameter.list$discovery.method, " method"))
}
```
```{r umap by cluster, warning = FALSE, include = FALSE}
miko_message("Generating UMAP plots...")
# get GGplot handle for cluster umap
plt.umap_by_cluster <- DimPlot(so.query, reduction = "umap", group.by = "seurat_clusters", label = TRUE) +
labs(title = "UMAP", subtitle = paste0(ulength(so.query@meta.data$seurat_clusters), " clusters | ", ncol(so.query), " cells")) +
xlab("UMAP 1") + ylab("UMAP 2") + theme_miko(legend = T)
plt.umap_by_barcode <- NULL
try({
plt.umap_by_barcode <- DimPlot(so.query, reduction = "umap", group.by = "Barcode", label = TRUE) +
labs(title = "UMAP", subtitle = "Stratified by Barcodes") +
xlab("UMAP 1") + ylab("UMAP 2") + theme_miko(legend = T)
}, silent = T)
if (parameter.list$general.print.inline) {
plt.umap_by_cluster
}
```
```{r meta module , warning = FALSE}
# pool genesets if modular activity is query of interest ##########################
if(!exists("moi.df")) moi.df <- (markers_of_interest)
if (meta.module.flag == T){
miko_message("Computing modular activity...")
gNames <- gNames.list
plt.clustermarkers_by_umap <- list()
available_markers <- c()
markers_of_interest <- names(moi.df)
exp.mat.new <- NULL
all.list <- list()
for (i in 1:length(markers_of_interest)){
# get meta module name
cur.marker <- markers_of_interest[i]
# clean dataset and include only those available in seurat object
cur.features <- as.character(moi.df[,i][moi.df[,i] != ""])
cur.features <- cleanFilterGenes(cur.features, so.query, parameter.list$general.species)
if (length(cur.features) == 0) next
module.name <- cur.marker
so.query.try = NA
so.query.try <- try(AddModuleScore(so.query,
features = list(cur.features),
ctrl = 50,
name = "new.module",
nbin = 5), silent = T)
if (is.na(so.query.try)) next
# assign name to modlue score
names(so.query.try@meta.data)[names(so.query.try@meta.data) %in% paste("new.module", 1, sep = "")] <- module.name
so.query@meta.data[[module.name]] <- so.query.try@meta.data[[module.name]]
# concat scores to dataframe
cur.module.df <- as.data.frame(so.query@meta.data[[module.name]])
colnames(cur.module.df) <- module.name
exp.mat.new <- bind_cols(exp.mat.new, cur.module.df)
available_markers[i] <- module.name
all.list[[module.name]] <- cur.features
}
# get all scores and cast as matrix
exp.mat.new.mat.t <- t(as.matrix(exp.mat.new))
# concat scores to seurat objects
exp.mat.1 <-so.query@assays[[DefaultAssay(so.query)]]@data
colnames(exp.mat.new.mat.t) <- colnames(exp.mat.1)
exp.mat.1 <- rbind(exp.mat.1, exp.mat.new.mat.t)
so.query@assays[[DefaultAssay(so.query)]]@data <- exp.mat.1
markers_of_interest <- available_markers[!is.na(available_markers)]
}
if (exists("so.query.try")) rm("so.query.try")
```
```{r bin cells, warning = FALSE, include = FALSE}
if (parameter.list$expression.do.hex){
n.cells <- ncol(so.query)
cells.per.bin <- 30
if (round(n.cells/cells.per.bin) > 100){
nhexbins <- 100
} else {
round(n.cells/cells.per.bin)
}
so.query <- schex::make_hexbin(so.query, nbins = round(n.cells/cells.per.bin),
dimension_reduction = "UMAP")
}
```
```{r umap expression plots, message=FALSE, warning=FALSE, include = FALSE}
miko_message("Generating UMAP expression plots...")
suppressWarnings({
suppressMessages({
# initialize variables
plt.umap.1 <- list()
plt.umap.2 <- list()
# gNames <- gNames.list
available_markers <- c()
# check if point size adjustment is necessary
if (ncol(so.query)> 50000) {
aps <- F
pt.size <- autoPointSize(ncol(so.query))
} else {
aps <- T
pt.size <- 1
}
if (class(markers_of_interest) == "data.frame") markers_of_interest <- as.vector(markers_of_interest[,1])
if (parameter.list$discovery.group.by == "seurat_clusters") u_groups <- as.character(as.numeric(u_groups)[order(as.numeric(u_groups))])
# query_genes_by_group <- data.frame(u_groups)
input.marker.list <- markers_of_interest
clustplot <- DimPlot(so.query, reduction = "umap", pt.size = aps, group.by = grouping_var,
label = TRUE, repel = TRUE) +
ggtitle(label = "UMAP") + xlab("UMAP 1") + ylab("UMAP 2")
# plot top differentially expressed genes per cluster and compare to cluster membership
pb <- txtProgressBar(max =length(markers_of_interest), style = 3)
for (i in c(1:(length(markers_of_interest)))) {
# empty list where plts will be stored
all_plts <- list()
# highlight cluster plot
all_plts[[1]] <- clustplot
cur.marker <- markers_of_interest[i]
if (!is.na(cur.marker)){
if (parameter.list$expression.do.hex){
all_plts[[length(all_plts)+1]] <- schex::plot_hexbin_feature(so.query, feature=cur.marker,
action="mean", xlab="UMAP1", ylab="UMAP2",
title=cur.marker,
mod = DefaultAssay(so.query),
type = "data")
} else {
# only use nebulosa for single gene expression profiles.
if (meta.module.flag){
all_plts[[length(all_plts)+1]] <- FeaturePlot(
object = so.query,
features = cur.marker,
pt.size = aps
) +
theme_miko(legend = T) +
scale_color_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
labs(title = "", subtitle =cur.marker, x = "UMAP 1", y = "")
} else {
is.success.plot <- F
do.nebullosa <- F
if (do.nebullosa){
try({
all_plts[[length(all_plts)+1]] <- Nebulosa::plot_density(
object = so.query,
features = cur.marker,
slot = "data",
joint = FALSE,
reduction = "umap",
dims = c(1, 2),
method = c("ks", "wkde"),
adjust = 1,
size = pt.size,
shape = 16,
combine = TRUE,
pal = "inferno"
) +
theme_miko(legend = T) +
scale_color_gradient(low = "grey95", high = "tomato") +
labs(title = "", subtitle =cur.marker, x = "UMAP 1", y = "")
theme_miko(legend = T)
is.success.plot <- T
}, silent = T)
}
if (!is.success.plot){
all_plts[[length(all_plts)+1]] <- exprUMAP(
object = so.query,
feature = cur.marker,
size = aps
) +
theme_miko(legend = T) +
labs(title = "", subtitle =cur.marker)
}
}
}
}
if (length(all_plts) == 2){
current.ind <- length(plt.umap.1)+1
plt.umap.1[[current.ind]] <- all_plts[[1]]
plt.umap.2[[current.ind]] <- all_plts[[2]]
available_markers[length(available_markers)+1] <- markers_of_interest[i]
if (parameter.list$general.print.inline) print((CombinePlots(all_plts, ncol = length(all_plts), legend = 'none')))
}
setTxtProgressBar(pb, i)
}
close(pb)
# remove NA entries
available_markers <- available_markers[!is.na(available_markers)]
# assign names marker names to each plot
names(plt.umap.1) <- names(plt.umap.2) <- available_markers
})
})
available_markers_original_order <- available_markers
available_markers <- available_markers[order(available_markers)]
```
```{r remove genes with zero variance}
miko_message("Omitting zero variance genes...")
v.mat <- so.query@assays[[DefaultAssay(so.query)]]@data
v.mat <- v.mat[rownames(v.mat) %in% available_markers, ]
# check if all genes have variance
if( ! is(v.mat, "sparseMatrix")){
# check if values are NA
countNA = sum(is.nan(v.mat)) + sum(!is.finite(v.mat))
if( countNA > 0 ){
stop("There are ", countNA, " NA/NaN/Inf values in exprObj\nMissing data is not allowed")
}
if (is.numeric(v.mat)){
rv = var(v.mat)
} else {
rv = apply(v.mat, 1, var)
}
} else{
rv = c()
for( i in seq_len(nrow(v.mat)) ){
rv[i] = var(v.mat[i,])
}
}
if( any( rv == 0) ){
omit.which = rownames(v.mat)[which(rv == 0)]
available_markers <- available_markers[!(available_markers %in% omit.which)]
}
```
```{r single cell expression, fig.width=10, fig.height=4, warning = FALSE, include = FALSE}
# include = FALSE,
miko_message("Generating expression plots...")
# clean up
try({ rm(exp.mat); rm(exp.mat.2); rm(exp.mat.complete) }, silent = T)
e.mat <- exp.mat.complete <- so.query@assays[[DefaultAssay(so.query)]]@data
e.mat.log <- log1p(e.mat)
rm(e.mat); invisible({gc()})
f.mat.clust <- avgGroupExpression(
so = so.query,
which.features = available_markers,
which.data = "data",
which.center = "fraction",
which.group = "seurat_clusters"
)
try({rownames(f.mat.clust) <- rownames(e.mat.log)}, silent = T)
invisible({gc()})
f.mat.bc <- avgGroupExpression(
so = so.query,
which.features = available_markers,
which.data = "data",
which.center = "fraction",
which.group = "bc"
)
try({rownames(f.mat.bc) <- rownames(e.mat.log)}, silent = T)
ubcl <- tryCatch(ulength(so.query@meta.data$bc), error = function(e) return(1))
plt.sgExp.all <- pbapply::pblapply(1:length(available_markers), function(x){
i <- x[[1]]
font.size <- 20
plt.sgExp.clust <- expression.Plot(so = so.query, e.mat = e.mat.log, f.mat = f.mat.clust,
show.full.axis = F, show.violin = F, font.size = font.size,
which.gene = available_markers[i], which.group = "seurat_clusters",
which.data = "data", x.label = "Cluster", verbose = F)
is.success <- F
if (ubcl > 1){
try({
plt.sgExp.barcode <- expression.Plot(so = so.query, e.mat = e.mat.log, f.mat = f.mat.bc,
show.full.axis = F, show.violin = F, font.size = font.size,
which.gene = available_markers[i], which.group = "bc", which.data = "data",
x.label = "Sample", x.label.angle = 25, verbose = F) +
labs(title = "", subtitle = "Samples",
caption = "violin = normalized expression\ndot = expression mean\nbar = expressin fraction")
is.success <- T
})
}
if (is.success){
plt.sgExp <- cowplot::plot_grid(plotlist = list(plt.sgExp.clust, plt.sgExp.barcode), ncol = 2)
} else {
plt.sgExp <-plt.sgExp.clust
}
return(plt.sgExp)
})
names(plt.sgExp.all) <- available_markers
```
```{r gene-gene associations,warning=FALSE}
miko_message("Computing feature associations...")
which.data <- parameter.list$cor.which.data
stopifnot(which.data %in% c("data", "scale"))
if (which.data == "data"){
query.cor <- "similarity.data"
} else if (which.data == "scale"){
query.cor <- "similarity.scale"
}
compute.cor <- T
# compute correlation matrix
if (compute.cor) {
# entire matrix
exp.mat.complete <- getExpressionMatrix(so.query, which.data = which.data)
# filter matrix to include expressed and query features
is.exp <- exp.mat.complete > 0
p.exp <- rowMeans(is.exp)
which.keep <- rownames(exp.mat.complete)[which(p.exp > parameter.list$cor.min.expr)]
which.keep2 <- rownames(exp.mat.complete)[rownames(exp.mat.complete) %in% available_markers]
exp.mat <- exp.mat.complete[rownames(exp.mat.complete) %in% unique(c(which.keep, which.keep2)), ]
# ensure no negative values (necessary for proportionality association metrics)
if ((min(exp.mat.complete) < 0) & (parameter.list$cor.method == "rho_p")) {
exp.mat <- exp.mat + abs(min(exp.mat.complete))
exp.mat.complete <- exp.mat.complete + abs(min(exp.mat.complete))
}
df.cor.list <- NULL
query.cor.list <- list()
which.missing <- c()
if (which.data == "data"){
t.exp.mat <- scMiko::sparse2dense(exp.mat, block.size = 10000, transpose = T)
} else {
t.exp.mat <- t(exp.mat)
}
# calculate feature associations
# rho proportionality constant ###############################################
if (tolower(parameter.list$cor.method) == "rho_p"){
query.cor.object <- propr::perb(t.exp.mat, select = colnames(t.exp.mat))
query.cor.mat <- query.cor.object@matrix
# spearman correlation coefficient ############################################
} else if (tolower(parameter.list$cor.method) == "spearman"){
exp.map.av <- t.exp.mat[ , toupper(colnames(t.exp.mat)) %in% toupper(available_markers)]
query.cor.mat <- cor(t.exp.mat, exp.map.av, method = "spearman")
# pearson correlation coefficient ############################################
} else if (tolower(parameter.list$cor.method) == "pearson"){
exp.map.av <- t.exp.mat[ , colnames(t.exp.mat) %in% available_markers]
query.cor.mat <- cor(t.exp.mat, exp.map.av)
# co-dependency index ########################################################
} else if (tolower(parameter.list$cor.method) == "cdi"){
exprGene <- tryCatch({getExpressedGenes(object = so.query, min.pct = parameter.list$cor.min.expr, group = "seurat_clusters")},
error = function(e){
return(getExpressedGenes(object = so.query, min.pct = parameter.list$cor.min.expr, group = NA))
})
cdi.df.all <- findCDIMarkers(object = so.query, features.x = available_markers,
features.y = exprGene, n.workers = parameter.list$n.workers, verbose = T)
cdi.df<- cdi.df.all %>% dplyr::select(c("feature.x", "feature.y", "ncdi"))
query.cor.mat <- pivot_wider(data = cdi.df, names_from = "feature.x", values_from = "ncdi")
query.cor.mat <- col2rowname(query.cor.mat, "feature.y")
query.cor.mat[is.na(query.cor.mat)] <- 0
}
}
# handle single-feature query
if ((dim(query.cor.mat)[2] == 1) & length(available_markers) == 1) colnames(query.cor.mat) <- available_markers
# clean up matrix
query.cor.mat.all <- query.cor.mat[,colnames(query.cor.mat) %in% available_markers]
if (!is.null(dim(query.cor.mat.all)) && ncol(query.cor.mat.all) == 0) stop("Correlation matrix is missing query features.")
# assign results
if (class(query.cor.mat.all) != "data.frame"){
df.cor.list <- as.data.frame(query.cor.mat.all)
} else {
df.cor.list <- query.cor.mat.all
}
if (ncol(df.cor.list) == 1) {
df.cor.list$genes<- rownames(query.cor.mat)
colnames(df.cor.list)[1] <- available_markers[1]
} else {
df.cor.list$genes <- rownames(df.cor.list)
}
if (!("genes" %in% colnames(df.cor.list))) df.cor.list$genes <- rownames(df.cor.list)
which.available.markers <- available_markers[available_markers %in% colnames(df.cor.list)]
df.cor.list <- df.cor.list[ ,c("genes", which.available.markers)]
# set auto-correlations to NA
for (i in 1:ncol(df.cor.list)){
if (colnames(df.cor.list)[i] == "genes") next
current.gene <- colnames(df.cor.list)[i]
which.match <- df.cor.list$genes %in% colnames(df.cor.list)[i]
df.cor.list[which.match,current.gene] <- NA
}
```
```{r prep genes for enrichment, warning = FALSE}
miko_message("Preparing gene associations for pathway enrichment...")
if (!is.null(df.cor.list)) {
df.cor.list.genes <- df.cor.list$genes
df.cor.list.cor <- df.cor.list %>% dplyr::select(-c("genes"))
mat.cor.list.cor <- as.matrix(df.cor.list.cor)
mat.cor.list.cor <- signif(mat.cor.list.cor, 4)
df.cor.list <- bind_cols(data.frame(genes = df.cor.list.genes), as.data.frame(mat.cor.list.cor))
df2enrich <- as.data.frame(df.cor.list)
} else {
df2enrich <- NULL
}
# ensure no duplicate exist
if (!is.null(df2enrich)){
df2enrich <- df2enrich[!duplicated(df2enrich$genes), ]
}
```
```{r cormat heatmap, warning = FALSE}
if (exists("df.cor.list")) {
miko_message("Similarity heatmap...")
df.cor.list.mat <- df.cor.list
df.cor.list.mat <- df.cor.list.mat[!duplicated(df.cor.list.mat$genes), ]
rownames(df.cor.list.mat) <- df.cor.list.mat$genes
df.cor.list.mat <- dplyr::select(df.cor.list.mat, -c("genes"))
cor.mat <- as.matrix(df.cor.list.mat)
top.n.cor <- parameter.list$cor.top.n
n.per.query <- ceiling(0.5*top.n.cor/ncol(cor.mat))
filter.cor <- T
if (filter.cor){
cor.mat.df <- as.data.frame(cor.mat)
top.cor <- unique(as.vector(unlist(apply(cor.mat.df, 2, function(x) {
c(rownames(cor.mat)[order(-x)][1:n.per.query], rownames(cor.mat)[order(x)][1:n.per.query])
}))))
cor.mat.sub <- cor.mat[rownames(cor.mat) %in% top.cor, ]
if (any(class(cor.mat.sub) == "numeric")){
names(cor.mat.sub) <- rownames(cor.mat)[rownames(cor.mat) %in% top.cor]
} else {
rownames(cor.mat.sub) <- rownames(cor.mat)[rownames(cor.mat) %in% top.cor]
}
} else {
cor.mat.sub <- cor.mat
}
}
```
```{r create cor heatmap, include= F}
# fitered correlation matrix
if ((class(cor.mat.sub) == "matrix") && (dim(cor.mat.sub)[2] > 0)) {
cor.mat.sub <- cor.mat.sub[ ,!(is.na(apply(cor.mat.sub, 2, function(x) var(x, na.rm = T))))]
library("heatmaply")
plt.heat <- heatmaply(cor.mat.sub, scale_fill_gradient_fun = scale_fill_miko(), main = "Feature Association Matrix",
xlab = "Query Feature(s)", ylab = "Associated Feature(s)",
key.title = parameter.list$cor.method)
if (parameter.list$general.print.inline) {
plt.heat
# plt.heat2
}
} else {
plt.heat <- NULL
}
```
```{r ranked correlations, fig.height=13, fig.width=10, include = FALSE}
if (tolower(parameter.list$cor.method) == "rho_p") {
fill.label <- "Proportionality Metric"