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M04_CellAnnotation_public.Rmd
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M04_CellAnnotation_public.Rmd
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---
title: "Cell Annotation"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
source_code: embed
self_contained: true
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("Seurat", "GSVA", "scMiko", "presto", "plyr", "dplyr", "tidyr", "reshape2", "preprocessCore", "stringr", "RColorBrewer", "ggplot2", "gridExtra", "GGally", "splines","DT", "flexdashboard", "ggpmisc", "ggExtra", "grid", "ggrepel", "future", "gridGraphics", "ggwordcloud", "scales","org.Mm.eg.db", "org.Hs.eg.db", "ddpcr", "tm", "homologene", "parallel", "doParallel", "foreach", "quantreg")
# load packages
invisible(lapply(packages2load, library, character.only = TRUE, quietly = TRUE))
```
```{r analysis specifications}
parameter.list <- list(
input.file ="output_34245PM_demo_zeisel_10000cell.Rdata",
external.markers = NULL,
internal.markers = "Cell_Catalog",
marker.filter = "Manno2021|Cao2019|Cao2020|TabulaMuris|TabulaSapiens|Tyser2021|PijuanSala2019",
coherence.threshold = 0.8,
p.threshold = 0.05,
do.center = F, # center miko score using null model (not recommended)
frequent.filter.threshold = 0.75,
filter.frequent.fliers = T, # T if diverse population, F if homogenous
min.representation = 0.5,
cluster.resolution = 0.5,
fdr.correction = F,
clean.clusters = F,
max.cells = 20000,
subsample_factor = 1,
min.geneset.size = 2,
max.geneset.size = 200,
pathway.db = "Bader", # options: Bader, GO
n.workers = parallel::detectCores(),
save.pdf = F,
show.top.n.gene.panels = 5,
max.gene.panels.allowed = 70,
print.inline = F ,
update.log = F,
rprofile.dir = F,
developer = F
)
```
```{r load data, warning = FALSE}
# Specify data directories
if (parameter.list$rprofile.dir){
dir.preprocessed <- "Preprocessed_Datasets/"
dir.reference <- "Reference_Datasets/"
if (!exists("data.path") & !exists("user")) {
stop("data.path and user variables do not exist in global enviroment. Ensure that these are specified in .Rprofile. If specified, try restarting Rstudio.")
}
}
if (!("input.file" %in% names(parameter.list))) stop("input.file is not specified")
# load query dataset
miko_message("Importing data...")
if ((!grepl(".Rdata|.RData", parameter.list$input.file)) & !(grepl(".rds", parameter.list$input.file))){
parameter.list$input.file <- paste0(parameter.list$input.file, ".Rdata")
}
if (parameter.list$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 = NULL, resolution= parameter.list$cluster.resolution, subset.data = NULL,
subsample = parameter.list$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$n.cell
rm(prep.list);
invisible({gc()})
# clean clusters
if (parameter.list$clean.clusters) so.query <- cleanCluster(so.query, return.plots = F)
parameter.list$species <- detectSpecies(so.query)
df.tally.orig <- as.data.frame(table(so.query@meta.data[ ,"seurat_clusters"]))
if (parameter.list$max.cells < ncol(so.query)){
so.query <- downsampleSeurat(object = so.query,
subsample.n = parameter.list$max.cells, sample.group = "seurat_clusters", min.group.size = 300)
}
df.tally.sampled <- as.data.frame(table(so.query@meta.data[ ,"seurat_clusters"]))
```
```{r analysis log}
if (parameter.list$developer){
parameter.list$update.log = T
}
if ("p.threshold" %in% names(parameter.list)){
if ((is.null(parameter.list$p.threshold)) | (!is.numeric(parameter.list$p.threshold))){
parameter.list$p.threshold <- 0.05
}
} else {
parameter.list$p.threshold <- 0.05
}
if ("coherence.threshold" %in% names(parameter.list)){
if ((is.null(parameter.list$coherence.threshold)) | (!is.numeric(parameter.list$coherence.threshold))){
parameter.list$coherence.threshold <- 0.8
}
} else {
parameter.list$coherence.threshold <- 0.8
}
miko_message("Updating analysis log...")
# Initiate and fill analysis Log
df.log <- initiateLog("Cell Annotation")
df.log <- addLogEntry("Query File (.Rdata)", parameter.list$input.file, df.log, "input.file")
df.log <- addLogEntry("Default Assay", current.assay, df.log, "current.assay")
df.log <- addLogEntry("Cluster Resolution", parameter.list$cluster.resolution, df.log, "cluster.resolution")
df.log <- addLogEntry("Subsample Factor", parameter.list$subsample_factor, df.log, "subsample_factor")
df.log <- addLogEntry("Max Cells", parameter.list$max.cells, df.log, "max.cells")
df.log <- addLogEntry("FDR correction", parameter.list$fdr.correction, df.log, "fdr.correction")
df.log <- addLogEntry("Species", parameter.list$species, df.log, "species")
df.log <- addLogEntry("Clusters cleaned", parameter.list$clean.clusters, df.log, "clean.clusters")
df.log <- addLogEntry("Minimum gene set size", parameter.list$min.geneset.size, df.log, "min.geneset.size")
df.log <- addLogEntry("Maximum gene set size", parameter.list$max.geneset.size, df.log, "max.geneset.size")
# df.log <- addLogEntry("Reference gene sets", paste(parameter.list$which.references, collapse = ", "), df.log, "which.references")
df.log <- addLogEntry("Gene set enrichment database", parameter.list$pathway.db, df.log, "pathway.db")
df.log <- addLogEntry("PDF saved", parameter.list$save.pdf, df.log, "save.pdf")
if (parameter.list$developer) df.log <- addLogEntry("Update log", parameter.list$update.log, df.log, "update.log")
if ("n.workers" %in% names(parameter.list)){
df.log <- addLogEntry("n workers", parameter.list$n.workers, df.log, "n.workers")
} else {
parameter.list$n.workers <- 1
df.log <- addLogEntry("n workers", parameter.list$n.workers, df.log, "n.workers")
}
```
```{r get prior history log}
# determine prior log history
cur.env <- objects()
module.logs <- cur.env[grep("^df.log_Module",cur.env)]
```
```{r cluster results, warning = FALSE}
plt.umap_by_cluster <-cluster.UMAP(so.query) +
labs(title = "UMAP", subtitle = "Stratified by Clusters") +
xlab("UMAP 1") + ylab("UMAP 2") + theme_miko(legend = T)
# plot umap by barcodes
if ("Barcode" %in% colnames(so.query@meta.data)){
plt.umap_by_barcode <- DimPlot(so.query, reduction = "umap", group.by = "Barcode") +
labs(title = "UMAP", subtitle = "Stratified by Barcodes") +
xlab("UMAP 1") + ylab("UMAP 2") + theme_miko(legend = T)
} else {
plt.umap_by_barcode <- NULL
}
if (parameter.list$print.inline){
print(plt.umap_by_cluster)
print(plt.umap_by_barcode)
}
```
```{r cluster composition by barcode, warning = FALSE}
# get barcode labels and clusters
cluster_membership <- as.vector(so.query@meta.data[["seurat_clusters"]])
if ("Barcode" %in% colnames(so.query@meta.data)) {
barcode_labels <- as.vector(so.query@meta.data[["Barcode"]])
} else {
barcode_labels <- NULL
}
df.bar_codes <- data.frame(cluster_membership, barcode_labels)
# tally up cluster compositions
df.all_barcodes <- df.bar_codes %>%
dplyr::group_by(cluster_membership, barcode_labels) %>%
tally() %>%
dplyr::mutate(freq = n / sum(n))
u_barcodes <- unique(df.all_barcodes$barcode_labels)
df_for_wide_barcodes <- data.frame()
df.all_barcodes_wide <- data.frame()
df.cluster_annotations_barcodes <- data.frame()
plt.cluster_composition_barcodes <- c()
# }
```
```{r prep all signatures}
miko_message("Preparing gene sets...")
# get cell-type markers
signatures.symbol <- list()
if (!is.null(parameter.list$external.markers)){
if (class(parameter.list$external.markers) == "list"){
signatures.symbol <- c(signatures.symbol, parameter.list$external.markers)
}
}
if (!is.null(parameter.list$internal.markers)){
data(geneSets)
which.match <- parameter.list$internal.markers[parameter.list$internal.markers %in% names(geneSets)]
for (i in 1:length(which.match)){
signatures.symbol <- c(signatures.symbol, wideDF2namedList(geneSets[[which.match[i]]]))
}
}
# filter markers
if (!is.null(parameter.list$marker.filter)){
if (length(parameter.list$marker.filter) > 1){
parameter.list$marker.filter <- paste(parameter.list$marker.filter, collapse = "|")
}
signatures.symbol <- signatures.symbol[grepl( parameter.list$marker.filter, names(signatures.symbol))]
}
if (length(signatures.symbol) == 0) stop("No cell-type markers have been specified.")
# ensure proper species representation
if (parameter.list$species == "Hs"){
signatures.symbol <- lapply(signatures.symbol, toupper)
} else if (parameter.list$species == "Mm"){
signatures.symbol <- lapply(signatures.symbol, firstup)
}
gene.list <- signatures.symbol
df.gs.statistics <- bind_rows(pbapply::pblapply(names(gene.list), function(x){
gene_set_name <- x[[1]]
gene_set_genes <- gene.list[[gene_set_name]]
n_genes <- length(gene_set_genes)
n_available <- sum(gene_set_genes %in% rownames(so.query))
return(data.frame(
set = gene_set_name,
n.genes = n_genes,
n.available = n_available,
fraction.available = signif(n_available/ n_genes, 3),
included.in.analysis = signif(n_available/ n_genes, 3) > parameter.list$min.representation
))
}))
# filter out gene sets that are not well represented in current data
gene.list <- gene.list[names(gene.list) %in% df.gs.statistics$set[df.gs.statistics$included.in.analysis]]
```
```{r cluster differentials}
miko_message("Getting expression matrices...")
# calculate differentials
u.clust <- getOrderedGroups(so.query, which.group = "seurat_clusters", is.number = T)
df.prest <- presto::wilcoxauc(X = so.query,
group_by = "seurat_clusters", assay = "data", seurat_assay = DefaultAssay(so.query))
e.mat <- pivot_wider(df.prest %>% dplyr::select(feature, group, avgExpr), names_from = group, values_from = avgExpr)
e.mat <- col2rowname(e.mat, "feature"); e.mat <- as.matrix(e.mat[ ,order(as.numeric(colnames(e.mat)))])
df.e.wide <- data.frame(gene = rownames(e.mat), as.data.frame(e.mat)); colnames(df.e.wide) <- c("gene", u.clust)
```
```{r expression heatmap, include = FALSE, fig.width = 12, fig.height = 5}
plt.heat.scaled <- NULL
try({
# get var features
var.features <- so.query@assays[[DefaultAssay(so.query)]]@var.features
# scaled expression plots
df.s4plt <- as.matrix(df.e.wide %>% dplyr::select(-c("gene")))
df.s4plt <- df.s4plt[rownames(df.s4plt) %in% var.features, ]
df.s4plt <- t(apply(df.s4plt, 1, function(x) (x-mean(x))/sd(x)))
scale.lim <- 3
df.s4plt[df.s4plt > scale.lim] <- scale.lim
df.s4plt[df.s4plt < (-1*scale.lim)] <- (-1*scale.lim)
my.breaks <- seq((-1*scale.lim), scale.lim, by = 0.01)
df.s4plt <- df.s4plt[!(apply(df.s4plt, 1, function(x) all(is.na(x)))), ]
plt.heat.scaled <- heatmaply::heatmaply(x = df.s4plt,
main = "Variable Feature Expression",
key.title = "Average\nScaled\nExpression",
xlab = "Clusters",
ylab = "Variable Features",
scale_fill_gradient_fun = scale_fill_miko())
if (parameter.list$print.inline){
plt.heat.scaled
}
rm(df.s4plt);
}, silent = T)
```
```{r score calculation}
maxgs <- max(unlist(lapply(gene.list, length)))
if (maxgs > 200) maxgs <- 200
stepsize <- round(maxgs / 15)
if (stepsize < 1) stepsize <- 1
if (stepsize > 20) stepsize <- 10
# Step 1: Null gene set-size adjusted model
miko_message("Step 1: Null model estimation...")
ns.res <- nullScore(object = so.query, assay = DefaultAssay(so.query), n.replicate = 10, nbin = 24,
min.gs.size = 2, max.gs.size = maxgs, step.size = stepsize,
nworkers = parameter.list$n.workers, verbose = T, subsample.n = 5000)
# Step 2: Miko scores
miko_message("Step 2: Miko score calculation...")
so.query <- mikoScore(object = so.query, geneset = gene.list, nbin = 24, do.center = parameter.list$do.center,
nullscore = ns.res, assay = DefaultAssay(so.query), nworkers = parameter.list$n.workers)
# Step 3: Significance assignment
miko_message("Step 3: Identfying significant enrichments...")
score.result <- sigScore(object = so.query, geneset = gene.list, reduction = "umap")
variance.raw.plot <- ns.res$variance.raw.plot
variance.mean.plot <- ns.res$variance.mean.plot
plt.null.model <- ns.res$mean.plot
plt.corrected.plot <- ns.res$corrected.plot
plt.score.comp <- score.result$score_plot
if (parameter.list$print.inline){
variance.raw.plot
variance.mean.plot
plt.null.model
plt.corrected.plot
plt.score.comp
}
if (!parameter.list$fdr.correction){
plt.score.comp[["data"]][["fdr"]] <- plt.score.comp[["data"]][["p"]]
plt.score.comp <- plt.score.comp + labs(fill = "p<0.05")
plt.score.comp[["labels"]][["subtitle"]] <- paste0(100*signif(sum(plt.score.comp[["data"]][["p"]] < 0.05) / length(plt.score.comp[["data"]][["p"]]), 3), "% Significance Rate (p < 0.05)")
}
plot.null.enrichment <- cowplot::plot_grid(
variance.mean.plot + labs(y = "Variance (Raw Enrichment Score)", title = "Null Variance Model", subtitle = ""),
plt.null.model + labs(title = "Null Model with 95% CI", y = "Raw Enrichment Score"),
plt.score.comp + theme(legend.position = "bottom"),
nrow = 1,
align = "h",
axis = "tb",
labels = "AUTO"
)
if (parameter.list$print.inline){
plot.null.enrichment
}
```
```{r coherence scoring}
raw.mat <- so.query@misc[["raw_score"]]
colnames(raw.mat) <- gsub("raw_", "", colnames(raw.mat))
# cohererence scoring
df.cscore <- coherentFraction(object = so.query, score.matrix =raw.mat, nworkers = parameter.list$n.workers,method = "pearson",
genelist = gene.list, assay = DefaultAssay(so.query), slot = "data", subsample.cluster.n = 500)
# consolidate results
df.score <- score.result$cluster_stats
df.score$gs <- df.score$name
df.score$cluster <- df.score$cluster
df.merge <- merge(df.cscore, df.score, by = c("gs", "cluster"))
# fdr coorection
if (!parameter.list$fdr.correction){
df.merge$fdr <- df.merge$p
sig.label <- paste0("Enriched & Coherent\n(p<", parameter.list$p.threshold ,")")
} else {
sig.label <- paste0("Enriched & Coherent\n(FDR<", parameter.list$p.threshold ,")")
}
# flag significant hits
df.merge$sig <- df.merge$coherence_fraction >= parameter.list$coherence.threshold &
df.merge$miko_score > 0 &
df.merge$fdr < parameter.list$p.threshold
# visualize
plt.score.vs.coh <- df.merge %>%
ggplot(aes(x = (miko_score), y = coherence_fraction, label = gs, fill = sig)) +
geom_point(pch = 21, color = "white", size = 2) +
theme_miko(legend = T) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
scale_fill_manual(values = c("TRUE" = "tomato", "FALSE" = "grey")) +
labs(x = "Signature Score", y = "Coherence Fraction", fill = sig.label,
title = "Coherent Enrichments",
subtitle = paste0(100*signif(sum( df.merge$sig, na.rm = T) / nrow(df.merge), 3), "% Significance Rate"))
if (parameter.list$print.inline){
plt.score.vs.coh
}
```
```{r consolidate scores}
# df.merge
vst.merge.final <- data.frame(cluster = df.merge$cluster,
cell.type = df.merge$gs,
# ref.set = NA,
n.genes = NA,
miko_score = df.merge$miko_score,
p = df.merge$p,
fdr = df.merge$fdr,
coherence_fraction = df.merge$coherence_fraction,
r_mean = df.merge$r_mean
)
if (!parameter.list$fdr.correction){
vst.merge.final$fdr <- vst.merge.final$p
}
vst.merge.final$cluster <- paste0("c", vst.merge.final$cluster)
```
```{r flag frequent fliers}
ugrp <- unique(vst.merge.final$cluster)
vst.merge.final.sig <- vst.merge.final %>% dplyr::filter(fdr < parameter.list$p.threshold)
df.tally.ff <- as.data.frame(table(vst.merge.final.sig$cell.type))
df.tally.ff$Freq2 <- df.tally.ff$Freq/length(ugrp)
ff.thresh <- parameter.list$frequent.filter.threshold
plt.ff <- df.tally.ff %>%
ggplot(aes(x = Freq2)) +
geom_histogram() +
geom_vline(xintercept = ff.thresh, linetype = "dashed", color = "tomato") +
labs(title = "Frequent Flier Distribution", subtitle = paste0(signif(100*sum(df.tally.ff$Freq2 > ff.thresh)/nrow(df.tally.ff), 3), "% enriched genesets are frequent fliers (significant > ", 100*ff.thresh, "% of clusters)"),
x = "Proportion of significant clusters", y = "Number of gene sets") +
theme_miko()
if (parameter.list$print.inline){
plt.ff
}
which.ff <- as.character(df.tally.ff$Var1[df.tally.ff$Freq2 >= ff.thresh])
vst.merge.final$frequent_flier = (vst.merge.final$cell.type %in% which.ff)
```
```{r wordclouds, fig.width = 10, fig.height =5, include = FALSE, warning = FALSE, message = FALSE}
if (parameter.list$filter.frequent.fliers){
ff <- vst.merge.final$frequent_flier
} else {
ff <- rep(F, length(vst.merge.final$frequent_flier))
}
plt.ww.list <- annotationCloud(object = so.query,
object.group = "seurat_clusters",
score = vst.merge.final$miko_score,
score.group = gsub("c", "",vst.merge.final$cluster),
score.cell.type = vst.merge.final$cell.type,
score.p = vst.merge.final$p,
score.fdr = vst.merge.final$fdr,
score.coherence.fraction = vst.merge.final$coherence_fraction,
score.frequent.flier = ff, #
fdr.correction = parameter.list$fdr.correction,
p.threshold = parameter.list$p.threshold,
coherence.threshold = parameter.list$coherence.threshold,
show.n.terms = 15,
verbose = T)
if (parameter.list$print.inline){
plt.ww.list
}
```
```{r plot signature gene expressions, message=FALSE, warning=FALSE, fig.width = 20, fig.height = 10}
miko_message("Generating dotplots...")
suppressMessages({
suppressWarnings({
# wrangle genesets into workable format
vst.merge.lenient <- vst.merge.final %>% dplyr::filter(fdr < parameter.list$p.threshold)
all.signatures.list <- list()
cur.signature.list <- signatures.symbol
signature.df <- NULL
for (j in 1:length(cur.signature.list)){
if (length(cur.signature.list[[j]])>0){
cur.signature.name <- names(cur.signature.list)[j]
cur.signature.df <- as.data.frame(unlist(cur.signature.list[[cur.signature.name]]))
cur.signature.df$signature <- cur.signature.name
cur.signature.df <- cur.signature.df[ ,c(2,1)]
colnames(cur.signature.df) <- c("signature", "genes")
signature.df <- bind_rows(signature.df,cur.signature.df )
vst.merge.lenient$n.genes[vst.merge.lenient$cell.type %in% cur.signature.name] <- nrow(cur.signature.df)
vst.merge.final$n.genes[vst.merge.final$cell.type %in% cur.signature.name] <- nrow(cur.signature.df)
}
}
all.signatures.list <- signature.df
# recover reference set annotations #
plt.signature.subset <- TRUE # (flag specifying whether to plot all or just subset)
# select top genesets to visualize
if (parameter.list$filter.frequent.fliers){
df.sig2plt.cluster <- unique((vst.merge.final %>%
dplyr::filter(!frequent_flier) %>%
dplyr::filter(fdr < parameter.list$p.threshold,
miko_score > 0,
coherence_fraction > parameter.list$coherence.threshold,
!frequent_flier) %>%
dplyr::group_by(cluster) %>%
dplyr::top_n(parameter.list$show.top.n.gene.panels, miko_score)))
df.sig2plt <- unique((vst.merge.final %>% dplyr::filter(fdr < parameter.list$p.threshold, coherence_fraction > parameter.list$coherence.threshold, !frequent_flier) %>%
dplyr::arrange(-miko_score)))
} else {
df.sig2plt.cluster <- unique((vst.merge.final %>%
dplyr::filter(fdr < parameter.list$p.threshold,
miko_score > 0,
coherence_fraction > parameter.list$coherence.threshold) %>%
dplyr::group_by(cluster) %>%
dplyr::top_n(parameter.list$show.top.n.gene.panels, miko_score)))
df.sig2plt <- unique((vst.merge.final %>% dplyr::filter(fdr < parameter.list$p.threshold, coherence_fraction > parameter.list$coherence.threshold) %>%
dplyr::arrange(-miko_score)))
}
if (nrow(df.sig2plt) > parameter.list$max.gene.panels.allowed) df.sig2plt <- df.sig2plt[1:parameter.list$max.gene.panels.allowed, ]
df.sig2plt <- unique(bind_rows(df.sig2plt, df.sig2plt.cluster))
signatures.symbol2 <- signatures.symbol
names(signatures.symbol2) <- make.names(names(signatures.symbol2))
gene.dot.list <- signatures.symbol2[unique(df.sig2plt$cell.type)]
gene.dot.list <- gene.dot.list[order(names(gene.dot.list))]
dot.hg <- runHG(gene.list = gene.dot.list,
gene.universe = rownames(so.query),
species = parameter.list$species,
pathway.db = parameter.list$pathway.db)
dot.hg.sum <- summarizeHG(dot.hg, show.n = 15)
#logical indicating whether to generate gene expression dot plots (computationally intensive)
show.gene.exp <- T
# generate plots
plt.expression.all.dotplots <- list()
# signature.dot.list.all <- gene.dot.list
signature.dot.list.all <- all.signatures.list
signature.dot.list.all$signature <- make.names(signature.dot.list.all$signature)
u.sig <- unique(signature.dot.list.all$signature)
u.sig <- u.sig[u.sig %in% names(gene.dot.list)]
if (show.gene.exp){
for (i in 1:length(u.sig)){
cur.signature.list.name <- u.sig[i]
plt.expression.dotplot <- list()
signature.df <- signature.dot.list.all %>% dplyr::filter(signature %in% u.sig[i])
u.signatures <- as.vector(unique(signature.df$signature))
cur.signature.df <- signature.df
cur.signature.genes <- unique(as.vector(cur.signature.df$genes))
n.genes <- length(cur.signature.genes)
if (n.genes < 11) xlab.size <- 15
if (n.genes > 10) xlab.size <- 12
if (n.genes > 40) xlab.size <- 10
if (n.genes > 50) xlab.size <- 9
if (n.genes > 60) xlab.size <- 8
if (n.genes > 70) xlab.size <- 7
if (n.genes > 80) xlab.size <- 6
dat <- score.result$cell_stats
mad.threshold <- 5
pumap <- score.result$cell_stats %>%
dplyr::filter(name == u.sig[i]) %>%
ggplot(aes(x= reduction.x, y = reduction.y,
color = snip(value, verbose = F,
lower.cutoff = median(dat$value, na.rm = T) - mad.threshold*mad(dat$value, na.rm = T),
upper.cutoff = median(dat$value, na.rm = T) + mad.threshold*mad(dat$value, na.rm = T))))+
scale_color_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
theme_void()+
labs(x= "UMAP1", y = "UMAP2", color = "score", title = u.sig[i])+
geom_point()
n.av <- sum(cur.signature.genes %in% rownames(so.query))
p1 <- DotPlot(so.query, features = cur.signature.genes,
dot.scale = 8, cols="RdBu", scale = T) +
theme_miko(legend =T) +
theme(legend.position = "bottom") +
RotatedAxis() +
ylab("Cell Cluster") +
scale_color_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
labs(title = u.sig[i], subtitle = paste0("Dotplot (", n.av , "/", length(cur.signature.genes), " Genes)")) +
theme(axis.text.x=element_text(size=xlab.size, angle = 75))
vst.merge.cloud.cur <- vst.merge.final %>% dplyr::filter(cell.type == u.sig[i])
vst.merge.cloud.cur$cluster2 <- (gsub("c", "", vst.merge.cloud.cur$cluster))
vst.merge.cloud.cur$cluster <- factor(vst.merge.cloud.cur$cluster,
levels = paste0("c", unique(vst.merge.cloud.cur$cluster2)[order(as.numeric(unique(vst.merge.cloud.cur$cluster2)))]))
if (parameter.list$fdr.correction){
sig.label <- paste0("Enriched (FDR < ", parameter.list$p.threshold,")")
} else {
sig.label <- paste0("Enriched (p < ", parameter.list$p.threshold,")")
}
p2 <- vst.merge.cloud.cur %>% ggplot(aes(x = cluster, y = miko_score,
fill = (fdr < parameter.list$p.threshold) & (miko_score > 0))) +
scale_fill_manual(values = c("TRUE" = "tomato", "FALSE" = "grey")) +
geom_bar(stat = "identity") + coord_flip() +
labs(title = "", subtitle = "Miko Score", y = "Miko Score",
x = "Cell Cluster", fill = sig.label) + theme_miko() + theme(legend.position = "bottom")
p2_v2 <- vst.merge.cloud.cur %>%
ggplot(aes(x = cluster, y = coherence_fraction)) +
geom_bar(stat = "identity") + coord_flip() +
geom_hline(yintercept = parameter.list$coherence.threshold, linetype = "dashed") +
labs(title = "", subtitle = "Coherence Score", y = "Coherence Score", x = "Cell Cluster",
fill = "Coherence (FDR < 5%)") + theme_miko() + theme(legend.position = "bottom")
p3 <- dot.hg.sum$plots[[u.sig[i]]] +
labs(title = "", subtitle = "Gene Set Enrichment") +
theme(legend.position = "bottom")
# dot.hg.sum$plots$
if (is.null(p3)){
plt.expression.dotplot[[ u.sig[i]]] <- cowplot::plot_grid(pumap, p1, p2,
nrow =1,
align = "h",
axis = "tb",
rel_widths = c(2, 2,1),
labels = "AUTO")
} else {
plt.expression.dotplot[[ u.sig[i]]] <- cowplot::plot_grid(pumap, p1, p2,p2_v2, p3,
nrow =1,
align = "h",
axis = "tb",
rel_widths = c(2, 2,0.75,0.75, 1.25),
labels = "AUTO")
}
if ((parameter.list$print.inline) & (i < 21))
print( plt.expression.dotplot[[ u.sig[i]]])
# }
plt.expression.all.dotplots[[cur.signature.list.name]] <- plt.expression.dotplot
}
}
plt.expression.dotplot <- plt.expression.all.dotplots
plt.expression.all.dotplots <- list(plt.expression.dotplot)
})
})
```
```{r central log}
# update central log
run.id <- NULL
if (parameter.list$developer){
if (!exists("user")) user <- "guest"
clog.update.success <- F
if (parameter.list$update.log){
try({
run.id <- updateCentralLog(Module = "M04", input.data = parameter.list$input.file, input.subset = NA, pdf.flag = parameter.list$save.pdf)
clog.update.success <- T
}, silent = F)
}
} else {
run.id <- paste("output_", gsub(":| ", "", paste0(format(Sys.time(), '%X'))), sep = "", collapse = "")
}
if (is.null(run.id)) {
warning("Central log update was unsuccessful :(\n")
run.id <- paste("M04_", user, "_r", paste0(format(Sys.time(), '%s')), sep = "", collapse = "")
}
```
```{r setup output directories}
# output path
if (!exists("data.path")) data.path = ""
output.path <- paste0(data.path, "Module_Outputs/", paste0(run.id,"_",format(Sys.time(), '%d%m%y'), "/"))
# create output directories
if (parameter.list$save.pdf){
dir.create(output.path)
dir.create(paste0(output.path, "Tables/"))
dir.create(paste0(output.path, "PDF/"))
}
```
Overview
=====================================
Sidebar {.sidebar}
-------------------------------------
For details on the **Cell-Type Annotation** scPipeline module, see [documentation](https://nmikolajewicz.github.io/scMiko/articles/scPipeline_module_overview.html).\
---
**Description**: Sample overview
---
**Cluster UMAP**: UMAP representation of scRNA-seq sample.
---
**Statistics**\
Cells, n: `r ncol(so.query)`\
Genes, n: `r nrow(so.query)`\
Variable genes, n: `r try({length(VariableFeatures(so.query))}, silent = T)`\
UMI/cell, median: `r try({round(median(so.query@meta.data[["nCount_RNA"]]))}, silent = T)`\
genes/cell, median: `r try({round(median(so.query@meta.data[["nFeature_RNA"]]))}, silent = T)`\
Clusters, n: `r try({ulength(so.query$seurat_clusters)}, silent = T)`\
Resolution: `r try({parameter.list$cluster.resolution}, silent = T)`\
Species: `r try({parameter.list$species}, silent = T)`
---
Row {.tabset}
-------------------------------------
### Cluster UMAP
```{r plt.umap_by_c, fig.width = 7, fig.height = 6}
plt.umap_by_cluster <- plt.umap_by_cluster +
labs(title = "UMAP", subtitle = paste0(ulength(so.query@meta.data$seurat_clusters), " clusters | ", ncol(so.query), " cells")) +
xlab("UMAP 1") + ylab("UMAP 2") +
theme_miko(legend = T)
print(plt.umap_by_cluster)
savePDF(file.name = paste0(output.path, "PDF/", "M04_umap_cluster.pdf"), plot.handle = plt.umap_by_cluster,
fig.width = 7, fig.height = 5, save.flag = parameter.list$save.pdf)
```
```{r plt.umap_by_b}
try({
savePDF(file.name = paste0(output.path, "PDF/", "M09_umap_barcode.pdf"), plot.handle = plt.umap_by_barcode,
fig.width = 7, fig.height = 5, save.flag = parameter.list$general.save.pdf)
}, silent =T)
if (("Barcode") %in% colnames(so.query@meta.data)){
if (ulength(so.query@meta.data$Barcode) > 1){
do.bc <- T
} else {
do.bc <- F
}
}
a1 <- knitr::knit_expand(text = sprintf("### %s\n", "Barcodes")) # tab header
a2 <- knitr::knit_expand(text = sprintf("\n```{r %s}",
paste("plt.umap_by_b", 1, sep = "")))
a3 <- knitr::knit_expand(text = sprintf("\n %s", "print(plt.umap_by_barcode)"))
a4 <- knitr::knit_expand(text = "\n```\n") # end r chunk
a1.chunk <- paste(a1, a2, a3, a4, collapse = '\n')
```
`r if (do.bc){paste(knitr::knit(text = paste(a1.chunk, collapse = '\n')))}`
```{r plt.cluster_composition_barcodes, fig.width=13, fig.height=4}
do.bc2 <- !is.null(plt.cluster_composition_barcodes)
if (do.bc2){
try({
savePDF(file.name = paste0(output.path, "PDF/", "M04_cluster_composition_barplot.pdf"), plot.handle = plt.cluster_composition_barcodes,
fig.width = 13, fig.height = 4, save.flag = parameter.list$save.pdf)
}, silent = T)
}
a1 <- knitr::knit_expand(text = sprintf("### %s\n", "Sample Representation")) # tab header
a2 <- knitr::knit_expand(text = sprintf("\n```{r %s, fig.width=13, fig.height=4}",
paste("plt.bar_by_sample", 1, sep = "")))
a3 <- knitr::knit_expand(text = sprintf("\n %s", "print(plt.cluster_composition_barcodes)"))
a4 <- knitr::knit_expand(text = "\n```\n") # end r chunk
a2.chunk <- paste(a1, a2, a3, a4, collapse = '\n')
```
`r if (do.bc2){paste(knitr::knit(text = paste(a2.chunk, collapse = '\n')))}`
```{r df.all_barcodes_wide}
## Sample Representation (table)
do.bc3 <- nrow(df.all_barcodes_wide) > 0
a1 <- knitr::knit_expand(text = sprintf("### %s\n", "Sample Representation (table)")) # tab header
a3.chunk <- paste(a1, collapse = '\n')
```
`r if (do.bc3){paste(knitr::knit(text = paste(a3.chunk, collapse = '\n')))}`
```{r}
if (do.bc3){
datatable(df.all_barcodes_wide, filter="top", extensions = 'Buttons',
options = list(pageLength = 50,
autoWidth = TRUE,
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'pdf')))
}
```
### Expression Heatmap
```{r exp mat}
plt.heat.scaled
```
```{r pdf heat}
try({
savePDF(file.name = paste0(output.path, "PDF/", "M04_expression_heatmap.pdf"),
plot.handle = (plt.heat.scaled),
fig.width = 8, fig.height = 5, save.flag = parameter.list$save.pdf)
}, silent = T)
```
Annotation Scores
=====================================
Sidebar {.sidebar data-width=400}
-------------------------------------
**Cell Annotation**: Marker-based cell type annotation using Miko scoring pipeline.\
**Description**:The Miko scoring pipeline is a marker-based cell-type annotation method that accounts for variations in gene set sizes, while simultaneously offering a hypothesis-testing framework capable of rejecting non-significantly enriched gene sets. For a given single cell dataset, query and size-matched random gene sets are scored, and the difference between query and random module scores is scaled using the size-adjusted standard deviation estimate obtained from a gene set-size dependent null model to yield the Miko score. Scaling by the size-adjusted standard deviation estimate corrects for size-dependencies and results in a test statistic from which a p-value can be derived.
We use two additional post-scoring filters to fine tune which gene sets are enriched. The first is a **coherence filter** in which a positive correlation between component gene expression and the Miko score is enforced for a minimum fraction of component genes. The second is a **frequent flier filter**, which flags gene sets that exceed a minimum significance rate and represent gene sets that enrich across most cell clusters.
On the *left*, the UMAP highlights which cluster of interest.
On the *right*, the word cloud illustrates the top enriched cell-type annotations, using the specified filters.
Row {.tabset data-height=550}
-------------------------------------
```{r Clouds}
out <- NULL
try({
out <- lapply(seq_along(plt.ww.list), function(i) {
a1 <- knitr::knit_expand(text = sprintf("### %s\n", names(plt.ww.list)[i])) # tab header
a2 <- knitr::knit_expand(text = sprintf("\n```{r %s, fig.width=10, fig.height=5}", paste(i, "detailed_tab")))
a3 <- knitr::knit_expand(text = sprintf("\nprint(plt.ww.list[[%d]])", i))
a4 <- knitr::knit_expand(text = "\n```\n") # end r chunk
paste(a1, a2, a3, a4, collapse = '\n') # collapse together all lines with newline separator