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041_ANALYSIS_04_xcell_clusters.R
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041_ANALYSIS_04_xcell_clusters.R
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pdata <- list()
pdata$figuresDir <- paste0(workspace$workspaceDir, "041_ANALYSIS_04_xcell_clusters_FIGURES/")
pdata$celltypes <- c("excitatory", "inhibitory", "opc", "oligodendrocyte", "astrocyte", "microglia")
names(pdata$celltypes) <- pdata$celltypes
pdata$ctprofiles <- read_rds(paste0(workspace$outputDir, "sc_stats_ctprofiles.rds"))
pdata$genes <- readRDS(paste0(workspace$outputDir, "genes_metadata.rds")) # genes
pdata$mkrs <- readRDS(paste0(workspace$outputDir, "umkrs.rds"))
pdata$goTerms <- readRDS(paste0(workspace$outputDir, "gene_ontology.rds"))
pdata$files$coexmats <- tibble(outfile = list.files(workspace$outputDir)) %>%
filter(grepl("coexmats", outfile)) %>%
mutate(outfile_name = gsub(".rds", "", outfile)) %>%
mutate(strs = strsplit(outfile_name, "_")) %>%
mutate(level = strs %>% sapply(function(currStr) { currStr[1] })) %>%
mutate(type = strs %>% sapply(function(currStr) { currStr[2] })) %>%
mutate(dataset = strs %>% sapply(function(currStr) { currStr[3] })) %>%
mutate(cell_type = strs %>% sapply(function(currStr) { currStr[4] })) %>%
dplyr::select(outfile, level, type, dataset, cell_type)
pdata$files$coefmats <- tibble(outfile = list.files(workspace$outputDir)) %>%
filter(grepl("coefmats", outfile)) %>%
mutate(outfile_name = gsub(".rds", "", outfile)) %>%
mutate(strs = strsplit(outfile_name, "_")) %>%
mutate(level = strs %>% sapply(function(currStr) { currStr[1] })) %>%
mutate(type = strs %>% sapply(function(currStr) { currStr[2] })) %>%
mutate(dataset = strs %>% sapply(function(currStr) { currStr[3] })) %>%
mutate(cell_type = strs %>% sapply(function(currStr) { currStr[4] })) %>%
dplyr::select(outfile, level, type, dataset, cell_type)
pdata$files$lnkmats <- tibble(outfile = list.files(workspace$outputDir)) %>%
filter(grepl("lnkmats", outfile)) %>%
mutate(outfile_name = gsub(".rds", "", outfile)) %>%
mutate(strs = strsplit(outfile_name, "_")) %>%
mutate(level = strs %>% sapply(function(currStr) { currStr[1] })) %>%
mutate(type = strs %>% sapply(function(currStr) { currStr[2] })) %>%
mutate(cell_type = strs %>% sapply(function(currStr) { currStr[3] })) %>%
dplyr::select(outfile, level, type, cell_type)
# +++++++++++++++++++++
# FIRST -- xcell level
# let's build consensus networks; one per cell type, by combining all the datasets
fdata <- list(); gc()
fdata$celltypes <- pdata$celltypes
fdata$genes <- fdata$celltypes %>% session$collectionUtils$lapply(function(currCelltype) {
pdata$ctprofiles %>%
filter(cell_type == currCelltype) %>%
dplyr::select(dataset, gene_id) %>%
mutate(value = 1) %>%
spread(dataset, value) %>%
na.omit() %>%
session$dataWrangler$extractColumn("gene_id") %>%
unique() %>%
sort()
})
fdata$coexmats <- fdata$genes %>% lapply(function(currGenes) {
coexmat <- matrix(nrow = length(currGenes), ncol = length(currGenes))
rownames(coexmat) <- currGenes
colnames(coexmat) <- currGenes
return(coexmat)
})
fdata$coexmats <- names(fdata$coexmats) %>% mclapply(function(currCelltype) {
coexmatClone <- fdata$coexmats[[currCelltype]]
lowerCoords <- which(lower.tri(coexmatClone), arr.ind = TRUE) # use the lower tri indices as primary
upperCoords <- lowerCoords[, c("col", "row")] # figure out the matching coordinates to "flip it back"
colnames(upperCoords) <- c("row", "col")
print("Computing individual co-expression networks.")
coexRanks <- rep(0, nrow(lowerCoords))
files <- pdata$files$coexmats %>% filter(cell_type == currCelltype, level == "sc")
files$outfile %>% session$collectionUtils$foreach(function(currFile) {
currDCoexmat <- read_rds(paste0(workspace$outputDir, currFile))
currDCoexmat <- currDCoexmat[rownames(coexmatClone), colnames(coexmatClone)]
currDCoexRanks <- currDCoexmat[lowerCoords]
currDCoexRanks[is.na(currDCoexRanks)] <- currDCoexRanks %>% median(na.rm = TRUE)
# normalize into ranks overall again; this would've been off because of data subsetting
currDCoexRanks <- currDCoexRanks %>% rank(ties.method = "average")
currDCoexRanks <- currDCoexRanks / max(currDCoexRanks)
coexRanks <<- coexRanks + currDCoexRanks
})
print("Normalizing co-expressions.")
coexRanks <- coexRanks %>% rank(ties.method = "average")
coexRanks <- coexRanks / max(coexRanks)
print("Converting to matrix form.")
coexmatClone[lowerCoords] <- coexRanks
coexmatClone[upperCoords] <- coexRanks
diag(coexmatClone) <- 1
return(coexmatClone)
}, mc.cores = 6)
names(fdata$coexmats) <- fdata$celltypes
fdata$coexmats %>% saveRDS(paste0(workspace$outputDir, paste0("sc_coexmats_consensus.rds")))
# fdata$coexmats <- read_rds(paste0(workspace$outputDir, paste0("sc_coexmats_consensus.rds")))
# +++++++++++++++++++++
# let's cluster the consensus stuff
fdata <- list(); gc()
fdata$coexmats <- read_rds(paste0(workspace$outputDir, paste0("sc_coexmats_consensus.rds")))
# OK, let's use this space to test out all the different methods
fdata$clusters <- fdata$coexmats %>% mclapply(function(currCoexmat) {
workspace$utils$computeClusters(currCoexmat, function(dendro, distMat) {
dynamicTreeCut::cutreeDynamic(dendro, distM = distMat, cutHeight = 0.2, minClusterSize = 30)
})
}, mc.cores = length(fdata$coexmats))
xdata <- list()
xdata$celltype <- "opc"
plotDendroAndColors(fdata$clusters$clusterObjs[[xdata$celltype]]$dendro,
colors = labels2colors(fdata$clusters$clusterObjs[[xdata$celltype]]$clusters),
rowText = fdata$clusters$clusterObjs[[xdata$celltype]]$clusters,
dendroLabels = FALSE)
# ++++++++++++==== COMMIT
fdata$clusters %>% saveRDS(paste0(workspace$outputDir, paste0("sc_clusters_objects.rds")))
# =================================
# +++++++++++++++++
# characterize the number of clusters and their n_gene distributions across cell types
fdata <- list(); gc()
fdata$clusters <- read_rds(paste0(workspace$outputDir, paste0("sc_clusters_objects.rds")))
# look at the number of genes per cluster, per cell type
fdata$nGenes <- fdata$clusters %>% session$collectionUtils$lapplyWithName(function(currCelltype, currCluster) {
currCluster$clusterTbl %>% group_by(cluster) %>% summarize(n_gene = n()) %>% mutate(cell_type = currCelltype)
}) %>% session$dataWrangler$rbind()
xdata <- list()
xdata$celltype <- "oligodendrocyte"
xdata$nGenes <- fdata$nGenes %>% filter(cell_type == xdata$celltype)
xdata$nGenes %>%
session$graphingUtils$ggplot(aes(x = n_gene, y = cluster)) +
geom_text(aes(label = n_gene), hjust = -0.3, size = 4.5) +
geom_bar(stat = "identity") +
xlim(0, max(xdata$nGenes$n_gene) + 10) +
ggtitle(paste0("Number of genes per cluster in ", xdata$celltype))
# flatten a big list of clusters, attach the cell type names to the clusters (cell type of origin)
fdata$clustersFlat <- fdata$clusters %>% session$collectionUtils$lapplyWithName(function(currCelltype, currCluster) {
currFlat <- currCluster$clusterFlat[names(currCluster$clusterFlat) != "id_0000"]
names(currFlat) <- paste0(currCelltype, ".", names(currFlat))
return(currFlat)
})
fdata$clustersMats <- fdata$clusters %>% session$collectionUtils$lapplyWithName(function(currCelltype, currCluster) {
currMat <- currCluster$clusterMat[, colnames(currCluster$clusterMat) != "id_0000", drop = FALSE]
colnames(currMat) <- paste0(currCelltype, ".", colnames(currMat))
return(currMat)
})
# ++++++++++++==== COMMIT
list(clusterObjs = fdata$clusters, clusterFlats = fdata$clustersFlat, clusterMats = fdata$clustersMats) %>%
saveRDS(paste0(workspace$outputDir, paste0("sc_clusters_objects.rds")))
# ++++++++++++++++++++++++++==
# compute the cluster quality in consensus SC to ensure quality and cell type specifciity
fdata <- list(); gc()
fdata$clusters <- read_rds(paste0(workspace$outputDir, paste0("sc_clusters_objects.rds")))
fdata$clustersGeneric <- read_rds(paste0(workspace$outputDir, paste0("sc_generic_clusters.rds")))
fdata$clusterMats <- fdata$clusters$clusterMats %>% session$collectionUtils$lapply(function(currClusterMat) {
genericMat <- fdata$clustersGeneric$clusterMat
genericMat <- genericMat[intersect(rownames(currClusterMat), rownames(genericMat)), ]
genesDiff <- rownames(currClusterMat) %>% setdiff(rownames(genericMat))
genericMat[genesDiff, ] <- 0
genericMat <- genericMat[rownames(currClusterMat), ]
return(currClusterMat %>% cbind(genericMat))
})
fdata$clusterFlats <- fdata$clusters$clusterFlats %>% session$collectionUtils$lapply(function(currClusterFlat) {
genesValid <- currClusterFlat %>% unlist() %>% unname() %>% unique()
genericFlat <- fdata$clustersGeneric$clusterFlat %>% lapply(function(genes) { genes %>% intersect(genesValid) })
return(c(currClusterFlat, genericFlat))
})
# fdata$coexmatFiles <- pdata$files$coexmats %>% filter(dataset != "consensus")
# fdata$coexmatFiles <- paste0(workspace$outputDir, fdata$coexmatFiles$outfile) %>% session$dataWrangler$attachNames(fdata$coexmatFiles$outfile)
fdata$coexmats <- read_rds(paste0(workspace$outputDir, paste0("sc_coexmats_consensus.rds")))
fdata$egad <- fdata$clusterMats %>% session$collectionUtils$lapply(function(currClusterMat) {
workspace$utils$computeEgadForEach(fdata$coexmats, currClusterMat) %>% dplyr::select(coex_ct = coexfile, everything())
}) %>% session$dataWrangler$rbind()
fdata$egad %>% session$graphingUtils$ggplot(aes(x = cluster, y = auc)) + geom_point(aes(color = coex_ct)) + session$graphingUtils$tiltX(angle = 90)
fdata$avgcoex <- fdata$clusterFlats %>% session$collectionUtils$lapplyWithName(function(cellType, currClusterFlat) {
workspace$utils$computeAvgCoexForEach(fdata$coexmats, currClusterFlat) %>% dplyr::select(coex_ct = coexfile, everything())
}) %>% session$dataWrangler$rbind()
fdata$avgcoex %>%
session$graphingUtils$ggplot(aes(x = cluster, y = coex)) + geom_point(aes(color = coex_ct)) + session$graphingUtils$tiltX(angle = 90)
# ========== COMMIT
list(egad = fdata$egad, avgcoex = fdata$avgcoex) %>%
saveRDS(paste0(workspace$outputDir, paste0("sc_clusters_integrity_across_celltype.rds")))
# =============
# ++++++++++++++++++++++++++==
# compute the quality of the clusters that were discovered at the sc level
# 1. egad, 2. average co-expression
# in sc, sbj, bk consensus and component networks in the same cell type
fdata <- list(); gc()
fdata$clusters <- read_rds(paste0(workspace$outputDir, paste0("sc_clusters_objects.rds")))
# fdata$clustersGeneric <- read_rds(paste0(workspace$outputDir, paste0("sc_generic_clusters.rds")))
fdata$clusterMats <- fdata$clusters$clusterMats %>% session$collectionUtils$lapply(function(currClusterMat) {
genericMat <- fdata$clustersGeneric$clusterMat
genericMat <- genericMat[intersect(rownames(currClusterMat), rownames(genericMat)), ]
genesDiff <- rownames(currClusterMat) %>% setdiff(rownames(genericMat))
genericMat[genesDiff, ] <- 0
genericMat <- genericMat[rownames(currClusterMat), ]
return(currClusterMat %>% cbind(genericMat))
})
fdata$clusterFlats <- fdata$clusters$clusterFlats %>% session$collectionUtils$lapply(function(currClusterFlat) {
genesValid <- currClusterFlat %>% unlist() %>% unname() %>% unique()
genericFlat <- fdata$clustersGeneric$clusterFlat %>% lapply(function(genes) { genes %>% intersect(genesValid) })
return(c(currClusterFlat, genericFlat))
})
fdata$coexmatFiles <- pdata$files$coexmats %>% filter(dataset != "consensus")
# fdata$coexmatFiles <- paste0(workspace$outputDir, fdata$coexmatFiles$outfile) %>% session$dataWrangler$attachNames(fdata$coexmatFiles$outfile)
fdata$coexmatsConsensus <- list(sc = read_rds(paste0(workspace$outputDir, paste0("sc_coexmats_consensus.rds"))),
sbj = read_rds(paste0(workspace$outputDir, paste0("sbj_coexmats_consensus.rds")))) %>%
unlist(recursive = FALSE)
# EGAD
fdata$egadComponents <- fdata$clusterMats %>% session$collectionUtils$lapplyWithName(function(cellType, currClusterMat) {
coexmatFiles <- fdata$coexmatFiles %>% filter(cell_type == cellType | level == "bk")
coexmats <- paste0(workspace$outputDir, coexmatFiles$outfile) %>% session$dataWrangler$attachNames(coexmatFiles$outfile)
workspace$utils$computeEgadForEach(coexmats, currClusterMat) %>%
left_join(coexmatFiles %>% dplyr::select(coexfile = outfile, level, dataset, coex_ct = cell_type), by = "coexfile") %>%
mutate(cluster_ct = cellType)
}) %>% session$dataWrangler$rbind()
fdata$egadConsensus <- fdata$clusterMats %>% session$collectionUtils$lapplyWithName(function(cellType, currClusterMat) {
coexmats <- fdata$coexmatsConsensus[grepl(cellType, names(fdata$coexmatsConsensus))]
workspace$utils$computeEgadForEach(coexmats, currClusterMat) %>%
mutate(str = strsplit(coexfile, "\\.")) %>%
mutate(level = str %>% sapply(function(currStr) { currStr[1] }),
cell_type = str %>% sapply(function(currStr) { currStr[2] })) %>%
dplyr::select(-coexfile, -str)
}) %>% session$dataWrangler$rbind()
fdata$egad <- fdata$egadComponents %>% dplyr::select(cluster, auc, avg_node_degree, degree_null_auc, n_gene, level, dataset, coex_ct, cluster_ct) %>%
rbind(fdata$egadConsensus %>%
mutate(dataset = "consensus") %>%
dplyr::select(cluster, auc, avg_node_degree, degree_null_auc, n_gene, level, dataset, coex_ct = cell_type) %>%
mutate(cluster_ct = coex_ct))
# AVG-COEX
fdata$avgcoexComponents <- fdata$clusterFlats %>% session$collectionUtils$lapplyWithName(function(cellType, currClusterFlat) {
coexmatFiles <- fdata$coexmatFiles %>% filter(cell_type == cellType | level == "bk")
coexmats <- paste0(workspace$outputDir, coexmatFiles$outfile) %>% session$dataWrangler$attachNames(coexmatFiles$outfile)
workspace$utils$computeAvgCoexForEach(coexmats, currClusterFlat) %>%
left_join(coexmatFiles %>% dplyr::select(coexfile = outfile, level, dataset, coex_ct = cell_type), by = "coexfile") %>%
mutate(cluster_ct = cellType)
}) %>% session$dataWrangler$rbind()
fdata$avgcoexConsensus <- fdata$clusterFlats %>% session$collectionUtils$lapplyWithName(function(cellType, currClusterFlat) {
coexmats <- fdata$coexmatsConsensus[grepl(cellType, names(fdata$coexmatsConsensus))]
workspace$utils$computeAvgCoexForEach(coexmats, currClusterFlat) %>%
mutate(str = strsplit(coexfile, "\\.")) %>%
mutate(level = str %>% sapply(function(currStr) { currStr[1] }),
cell_type = str %>% sapply(function(currStr) { currStr[2] })) %>%
dplyr::select(-coexfile, -str)
}) %>% session$dataWrangler$rbind()
fdata$avgcoex <- fdata$avgcoexComponents %>% dplyr::select(cluster, coex, n_gene, n_lnks, level, dataset, coex_ct, cluster_ct) %>%
rbind(fdata$avgcoexConsensus %>%
mutate(dataset = "consensus") %>%
dplyr::select(cluster, coex, n_gene, n_lnks, level, dataset, coex_ct = cell_type) %>%
mutate(cluster_ct = coex_ct))
# ========== COMMIT
list(egad = fdata$egad, avgCoex = fdata$avgcoex) %>%
saveRDS(paste0(workspace$outputDir, paste0("sc_clusters_integrity_within_celltype.rds")))
# =============
# for the bulk comparisons, before and after correction, etc, need to use the raw Pearson's correlations instead
# for all the bulk networks, compute cluster integrity within cell types
fdata <- list(); gc()
fdata$clusters <- read_rds(paste0(workspace$outputDir, paste0("sc_clusters_objects.rds")))
fdata$clustersGeneric <- read_rds(paste0(workspace$outputDir, paste0("sc_generic_clusters.rds")))
fdata$clusterFlats <- c(fdata$clusters$clusterFlats, list(generic = fdata$clustersGeneric$clusterFlat))
fdata$coexmatFiles <- pdata$files$coefmats %>% filter(level == "bk")
fdata$avgcoex <- fdata$clusterFlats %>% session$collectionUtils$lapplyWithName(function(cellType, currClusterFlat) {
coexmatFiles <- fdata$coexmatFiles
coexmats <- paste0(workspace$outputDir, coexmatFiles$outfile) %>% session$dataWrangler$attachNames(coexmatFiles$outfile)
workspace$utils$computeAvgCoexForEach(coexmats, currClusterFlat) %>%
left_join(coexmatFiles %>% dplyr::select(coexfile = outfile, level, dataset, coex_ct = cell_type), by = "coexfile") %>%
mutate(cluster_ct = cellType)
}) %>% session$dataWrangler$rbind()
# ========== COMMIT
fdata$avgcoex %>%
saveRDS(paste0(workspace$outputDir, paste0("sc_clusters_integrity_in_bk_coefmats.rds")))
# =============
# =============================
# ++++++++++++++++++++++++++==
# OK - finally can start plotting some figures & do some writing
# first, characterize the number of clusters / cluster sizes at the single cell level
fdata <- list(); gc()
fdata$clusters <- read_rds(paste0(workspace$outputDir, paste0("sc_clusters_objects.rds")))
fdata$objs <- fdata$clusters$clusterObjs
fdata$clusterFlat <- fdata$clusters$clusterFlats %>% unname() %>% unlist(recursive = FALSE)
fdata$clusterSize <- fdata$clusterFlat %>% session$collectionUtils$lapplyWithName(function(clusterName, genes) {
tibble(cluster = clusterName, n_gene = length(genes))
}) %>% session$dataWrangler$rbind()
fdata$clusterSize <- fdata$clusterSize %>%
mutate(cell_type = cluster %>% sapply(function(currStr) { currStr %>% str_extract("^[a-z]+") }))
# =================================================
# OUTPUT FIGURE
xdata <- list()
xdata$figName <- "sfigure_01.eps"
xdata$main <- fdata$clusterSize %>% mutate(cell_type = workspace$utils$fmtCelltypes(cell_type))
xdata$means <- fdata$clusterSize$n_gene %>% mean()
xdata$plot <- xdata$main %>%
session$graphingUtils$ggplot(aes(x = n_gene)) +
geom_histogram(aes(fill = cell_type)) +
geom_vline(xintercept = xdata$means, linetype = "dashed") +
scale_x_continuous(trans = "log10") +
scale_fill_brewer(palette = "Set1") +
ggtitle("Distribution of cluster size") +
xlab("Number of genes") +
ylab("Number of clusters") +
labs(fill = "Cell type")
xdata$plot
ggsave(filename = paste0(pdata$figuresDir, xdata$figName),
plot = xdata$plot, device = "eps", units = "cm", width = 25, height = 18, dpi = 1000)
# =================================================
# =================================================
# OUTPUT FIGURE ------- number of clusters per cell type
xdata <- list()
xdata$figName <- "sfigure_02.eps"
xdata$main <- fdata$clusterSize %>% group_by(cell_type) %>% summarize(n_cluster = n()) %>%
mutate(cell_type = workspace$utils$fmtCelltypes(cell_type))
xdata$means <- fdata$clusterSize$n_gene %>% mean()
xdata$plot <- xdata$main %>%
session$graphingUtils$ggplot(aes(x = cell_type, y = n_cluster)) +
geom_bar(stat = "identity", aes(fill = cell_type)) +
scale_fill_brewer(palette = "Set1") +
session$graphingUtils$tiltX(angle = 90) +
geom_text(aes(label = n_cluster), vjust = -1, size = 7) +
ylim(0, max(xdata$main$n_cluster) + 5) +
ggtitle("Number of clusters per cell type") +
theme(legend.position = "none") +
xlab("Cell type") +
ylab("Number of clusters")
xdata$plot
ggsave(filename = paste0(pdata$figuresDir, xdata$figName),
plot = xdata$plot, device = "eps", units = "cm", width = 20, height = 18, dpi = 1000)
# =================================================
# gene, across all networks
xdata <- list()
xdata$main <- fdata$clusters$clusterObjs %>% session$collectionUtils$lapplyWithName(function(currCelltype, currCluster) {
currCluster$clusterTbl %>% mutate(in_cluster = cluster_int > 0) %>% mutate(cell_type = currCelltype)
}) %>% session$dataWrangler$rbind()
xdata$inGenes <- xdata$main %>% filter(in_cluster) %>% session$dataWrangler$extractColumn("gene_id") %>% unique()
xdata$inGenes %>% length() # number of genes in cluster
xdata$allGenes <- xdata$main$gene_id %>% unique()
xdata$allGenes %>% length()
#=============
# gene coverage
xdata <- list()
xdata$genesOut <- fdata$clusters$clusterObjs %>% sapply(function(currCluster) {
currCluster$clusterTbl %>% filter(cluster_int == 0) %>% nrow()
})
xdata$genesIn <- fdata$clusterSize %>% group_by(cell_type) %>% summarize(n_gene_in = sum(n_gene))
xdata$genesIn$n_gene_out <- xdata$genesOut[xdata$genesIn$cell_type]
xdata$genesIn <- xdata$genesIn %>% mutate(total = n_gene_in + n_gene_out) %>% mutate(frac_in = n_gene_in / total)
xdata$main <- xdata$genesIn %>% dplyr::select(cell_type, n_gene_in, n_gene_out) %>%
gather(status, n_gene, -cell_type) %>% mutate(status = factor(status, levels = c("n_gene_out", "n_gene_in")))
# =================================================
# OUTPUT FIGURE ------- # gene coverage --- computed above
# xdata <- list()
xdata$figName <- "sfigure_03.eps"
xdata$plotMain <- xdata$main %>% mutate(cell_type = workspace$utils$fmtCelltypes(cell_type))
xdata$plotMain <- xdata$plotMain %>% mutate(Status = status %>% sapply(function(str) {
if (str == "n_gene_in") { "In" } else { "Out" }
}))
xdata$plot <- xdata$plotMain %>%
session$graphingUtils$ggplot(aes(x = cell_type, y = n_gene)) +
geom_bar(aes(fill = Status), stat = "identity") +
session$graphingUtils$tiltX(angle = 90) +
ggtitle("Number of genes assigned to a cluster") +
xlab("Cell type") +
ylab("Number of genes") +
scale_fill_manual(values = c("grey60", "black"))
xdata$plot
ggsave(filename = paste0(pdata$figuresDir, xdata$figName),
plot = xdata$plot, device = "eps", units = "cm", width = 25, height = 20, dpi = 1000)
# =================================================
# =================================================
# OUTPUT FIGURE
xdata <- list()
xdata$figName <- "sfigure_04.eps"
xdata$main <- fdata$clusterSize %>% mutate(cell_type = workspace$utils$fmtCelltypes(cell_type))
xdata$plot <- xdata$main %>%
session$graphingUtils$ggplot(aes(y = cluster, x = n_gene), size = "SMALL") +
geom_bar(aes(fill = cell_type), stat = "identity") +
scale_fill_brewer(palette = "Set1") +
labs(fill = "Cell type", x = "Number of genes", y = "Cluster")
xdata$plot
ggsave(filename = paste0(pdata$figuresDir, xdata$figName),
plot = xdata$plot, device = "eps", units = "cm", width = 20, height = 30, dpi = 1000)
# =================================================
# =================================================
# OUTPUT FIGURE
# gene intersections across the clusters
xdata <- list()
xdata$main <- fdata$clusters$clusterFlats %>% unname() %>% unlist(recursive = FALSE)
xdata$main <- xdata$main %>% sapply(function(cluster1) {
xdata$main %>% sapply(function(cluster2) {
(cluster1 %>% intersect(cluster2) %>% length()) / (cluster1 %>% union(cluster2) %>% length())
})
})
xdata$figNames <- c("sfigure_05.png", "sfigure_06.png", "sfigure_07.png", "sfigure_08.png", "sfigure_09.png", "sfigure_10.png")
xdata$i <- 6
(xdata$celltype <- sort(names(fdata$clusters$clusterFlats))[xdata$i])
xdata$clusterNames <- fdata$clusters$clusterFlats[[xdata$celltype]] %>% names()
xdata$clusterNamesOthrs <- (colnames(xdata$main))[!(colnames(xdata$main) %in% xdata$clusterNames)]
png(paste0(pdata$figuresDir, xdata$figNames[xdata$i]), units = "cm",
width = 55, height = 20, res = 400)
xdata$main[xdata$clusterNames, xdata$clusterNamesOthrs] %>%
session$graphingUtils$heatmap(size = session$graphingUtils$MEDIUM, cluster_rows = FALSE, cluster_cols = FALSE,
main = workspace$utils$fmtCelltypes(xdata$celltype))
dev.off()
# =================================================
# =================================================
# =================================================
# OUTPUT FIGURE
# dendrograms
xdata <- list()
xdata$i <- 6
xdata$figNames <- paste0("sfigure_", 11:16)
(xdata$celltype <- sort(pdata$celltypes)[xdata$i])
png(paste0(pdata$figuresDir, paste0(xdata$figNames[xdata$i], "_", xdata$celltype, ".png")), units = "cm", width = 30, height = 20, res = 400)
plotDendroAndColors(fdata$objs[[xdata$celltyp]]$dendro,
colors = labels2colors(fdata$objs[[xdata$celltype]]$clusters),
rowText = fdata$objs[[xdata$celltype]]$clusters,
dendroLabels = FALSE, abHeight = 0.2) %>% print()
dev.off()
# =================================================
# ++++++++++++++++++++++++++==
# here - look at how well single cell networks capture single cell clusters
fdata <- list(); gc()
fdata$main <- readRDS(paste0(workspace$outputDir, paste0("sc_clusters_integrity_within_celltype.rds")))
fdata$main <- fdata$main %>% lapply(function(currTbl) { currTbl %>% filter(level == "sc") })
# avg co-expression
xdata <- list()
xdata$main <- fdata$main$avgCoex
xdata$means <- xdata$main %>% group_by(coex_ct, dataset) %>% summarize(coex = mean(coex))
xdata$main %>%
session$graphingUtils$ggplot(aes(y = coex_ct, x = coex, color = coex_ct)) +
geom_point(shape = 1) +
geom_point(data = xdata$means, size = 5) +
session$graphingUtils$tiltX(angle = 90) +
ggtitle("Average consensus co-expression per cluster by dataset and cell type") +
facet_wrap(~dataset, ncol = 1) +
xlim(0, 1)
# egad
xdata <- list()
xdata$main <- fdata$main$egad
xdata$means <- xdata$main %>% group_by(coex_ct, dataset) %>% summarize(auc = mean(auc))
xdata$main %>%
session$graphingUtils$ggplot(aes(y = coex_ct, x = auc, color = coex_ct)) +
geom_point(shape = 1) +
geom_point(data = xdata$means, size = 5) +
session$graphingUtils$tiltX(angle = 90) +
ggtitle("EGAD performance per cluster by dataset and cell type") +
facet_wrap(~dataset, ncol = 1) +
xlim(0, 1)
# OK starting with average co-expressions
xdata <- list()
(xdata$celltype <- pdata$celltypes[5])
xdata$main <- fdata$main$avgCoex %>% filter(coex_ct == xdata$celltype)
xdata$consensus <- xdata$main %>% filter(dataset == "consensus")
xdata$component <- xdata$main %>% filter(dataset != "consensus")
xdata$componentMeans <- xdata$component %>% group_by(cluster, coex_ct) %>% summarize(coex = mean(coex))
xdata$component %>%
session$graphingUtils$ggplot(aes(x = coex)) +
geom_density(aes(color = dataset)) +
geom_density(data = xdata$consensus, size = 1) +
xlim(0, 1) +
ggtitle(paste0("Average co-expression in component networks"))
xdata$component %>%
session$graphingUtils$ggplot(aes(x = cluster, y = coex, color = dataset)) +
geom_point(color = "grey70") +
geom_line(aes(group = dataset), color = "grey70") +
geom_point(data = xdata$componentMeans, color = "black", size = 3) +
geom_line(data = xdata$componentMeans, group = 1, color = "black", size = 1) +
geom_point(data = xdata$consensus, color = "red", size = 3) +
geom_line(data = xdata$consensus, group = 1, color = "red", size = 1) +
session$graphingUtils$tiltX(angle = 90) +
ggtitle(paste0("Average co-expression in component networks: ", xdata$celltype)) +
ylim(0, 1)
xdata$component %>%
dplyr::select(cluster, dataset, coex) %>%
spread(dataset, coex) %>%
session$dataWrangler$setColAsRownames("cluster") %>%
session$graphingUtils$heatmap(cluster_row = FALSE, cluster_col = FALSE)
# OK, moving on to using EGAD
xdata <- list()
(xdata$celltype <- pdata$celltypes[6])
xdata$main <- fdata$main$egad #%>% filter(coex_ct == xdata$celltype)
xdata$consensus <- xdata$main %>% filter(dataset == "consensus")
xdata$component <- xdata$main %>% filter(dataset != "consensus")
xdata$componentMeans <- xdata$component %>% group_by(cluster) %>% summarize(auc = mean(auc))
xdata$component %>%
session$graphingUtils$ggplot(aes(x = auc)) +
geom_density(aes(color = dataset)) +
geom_density(data = xdata$consensus, size = 1) +
xlim(0, 1) +
ggtitle(paste0("Cluster integrity (EGAD-AUROC) in component networks"))
xdata$component %>%
session$graphingUtils$ggplot(aes(x = cluster, y = auc, color = dataset)) +
geom_point(color = "grey70") +
geom_point(data = xdata$componentMeans, color = "black", size = 2) +
geom_line(data = xdata$componentMeans, group = 1, color = "black") +
session$graphingUtils$tiltX(angle = 90) +
ggtitle(paste0("EGAD performance in component networks: ", xdata$celltype)) +
geom_hline(yintercept = 0.5, linetype = "dashed")
xdata$component %>%
dplyr::select(cluster, dataset, auc) %>%
spread(dataset, auc) %>%
session$dataWrangler$setColAsRownames("cluster") %>%
session$graphingUtils$heatmap(cluster_row = FALSE, cluster_col = FALSE)
# ++++++++++++++++++++++++++++++===========
# HERE - plot the EGAD-AUROC performance at the three levels, for all datasets;
# maybe split by cell types - rank by highest min xBulk performance between ROSMAP and Velmeshev
# later add for each cluster
# -- correlations in cell type profile (rank?)
# -- inter-cell type synchrony
# -- number of genes / fraction of which are ribosomal protein genes
fdata <- list(); gc()
fdata$main <- readRDS(paste0(workspace$outputDir, paste0("sc_clusters_integrity_within_celltype.rds")))$egad
fdata$main <- fdata$main %>% filter(dataset != "consensus", !grepl("generic", cluster))
# lets use the bulk level to sort
fdata$xbulk <- fdata$main %>% filter(level == "bk", dataset %in% c("rosmap", "velmeshev")) %>%
dplyr::select(cluster, dataset, auc) %>%
spread(dataset, auc)
fdata$xbulk <- fdata$xbulk %>% mutate(max_xbulk = pmax(rosmap, velmeshev))
fdata$xbulk <- fdata$xbulk %>% arrange(desc(max_xbulk)) # these are the clusters to focus on
# SOFT COMMIT #===============
fdata$xbulk %>% saveRDS(paste0(workspace$outputDir, "sc_clusters_xbulk_ranking.rds"))
pdata$clusters <- readRDS(paste0(workspace$outputDir, "sc_clusters_xbulk_ranking.rds"))
# ============================
# =================================================
# OUTPUT FIGURE
xdata <- list()
xdata$figName <- "figure_07.eps" # xcell performance
xdata$main <- fdata$main %>% filter(level == "sc")
xdata$main <- xdata$main %>% mutate(dataset = dataset %>% workspace$utils$fmtDataset())
xdata$main <- xdata$main %>% # map na values
dplyr::select(cluster, dataset, auc) %>% spread(dataset, auc) %>%
gather(dataset, auroc, -cluster)
xdata$plot <- xdata$main %>%
session$graphingUtils$ggplot(aes(x = cluster, y = dataset)) +
geom_tile(aes(fill = auroc)) +
scale_fill_gradient(low = "white", high = "red", na.value = "grey60", limits = c(0.5, 1)) +
session$graphingUtils$tiltX(angle = 90) +
scale_x_discrete(limits = c(pdata$clusters$cluster[1:20], pdata$clusters$cluster[96:115])) +
ggtitle("xCell")
xdata$plot
ggsave(filename = paste0(pdata$figuresDir, xdata$figName),
plot = xdata$plot, device = "eps", units = "cm", width = 35, height = 13)
# =================================================
# =================================================
# OUTPUT FIGURE
xdata <- list()
xdata$figName <- "figure_08.eps" # xsbj performance
xdata$main <- fdata$main %>% filter(level == "sbj")
xdata$main <- xdata$main %>% mutate(dataset = dataset %>% workspace$utils$fmtDataset())
xdata$main <- xdata$main %>% # map na values
dplyr::select(cluster, dataset, auc) %>% spread(dataset, auc) %>%
gather(dataset, auroc, -cluster)
xdata$plot <- xdata$main %>%
session$graphingUtils$ggplot(aes(x = cluster, y = dataset)) +
geom_tile(aes(fill = auroc)) +
scale_fill_gradient(low = "white", high = "red", na.value = "grey60", limits = c(0.5, 1)) +
session$graphingUtils$tiltX(angle = 90) +
xlim(c(pdata$clusters$cluster[1:20], pdata$clusters$cluster[96:115])) +
ggtitle("xSubject")
xdata$plot
ggsave(filename = paste0(pdata$figuresDir, xdata$figName),
plot = xdata$plot, device = "eps", units = "cm", width = 35, height = 13)
# =================================================
# =================================================
# OUTPUT FIGURE
xdata <- list()
xdata$figName <- "figure_09.eps" # xbulk performance
xdata$main <- fdata$main %>% filter(level == "bk", dataset %in% c("rosmap", "velmeshev"))
xdata$main <- xdata$main %>% mutate(dataset = dataset %>% workspace$utils$fmtDataset())
xdata$main <- xdata$main %>% # map na values
dplyr::select(cluster, dataset, auc) %>% spread(dataset, auc) %>%
gather(dataset, auroc, -cluster)
xdata$plot <- xdata$main %>%
session$graphingUtils$ggplot(aes(x = cluster, y = dataset)) +
geom_tile(aes(fill = auroc)) +
scale_fill_gradient(low = "white", high = "red", na.value = "grey60", limits = c(0.4, 1)) +
session$graphingUtils$tiltX(angle = 90) +
xlim(c(pdata$clusters$cluster[1:20], pdata$clusters$cluster[96:115])) +
ggtitle("xBulk")
xdata$plot
ggsave(filename = paste0(pdata$figuresDir, xdata$figName),
plot = xdata$plot, device = "eps", units = "cm", width = 35, height = 10)
# =================================================
# +++++++++++++++++++++++++++++++++============================
# get the gene sizes + ribosomal protein gene counts & plot it out
fdata <- list(); gc()
fdata$clusters <- read_rds(paste0(workspace$outputDir, paste0("sc_clusters_objects.rds")))
fdata$clusters <- fdata$clusters$clusterFlats %>% unname() %>% unlist(recursive = FALSE)
pdata$rpgs <- pdata$genes %>% filter(grepl("^RPL|^RPS.*$", gene)) %>% filter(!grepl("pseudogene", gene_name))
fdata$nGenes <- fdata$clusters %>% session$collectionUtils$lapplyWithName(function(id, clusterGenes) {
nRpg <- clusterGenes %>% intersect(pdata$rpgs$gene_id) %>% length()
nGene <- clusterGenes %>% length()
tibble(cluster = id, n_gene = nGene, n_rpg = nRpg)
}) %>% session$dataWrangler$rbind()
fdata$nGenes %>% arrange(desc(n_rpg))
fdata$nGenes %>%
session$graphingUtils$ggplot(aes(x = cluster, y = n_gene)) +
geom_bar(stat = "identity") +
session$graphingUtils$tiltX(angle = 90) +
xlim(c(pdata$clusters$cluster[1:20], pdata$clusters$cluster[96:115]))
fdata$nGenes %>%
session$graphingUtils$ggplot(aes(x = cluster, y = n_rpg)) +
geom_bar(stat = "identity") +
session$graphingUtils$tiltX(angle = 90) +
xlim(c(pdata$clusters$cluster[1:20], pdata$clusters$cluster[96:115]))
# =================================================
# OUTPUT FIGURE
xdata <- list()
xdata$figName <- "figure_10.eps"
xdata$plot <- fdata$nGenes %>%
session$graphingUtils$ggplot(aes(x = cluster)) +
geom_bar(aes(y = n_gene), stat = "identity", fill = "grey50") +
geom_bar(aes(y = n_rpg), stat = "identity", fill = "blue") +
session$graphingUtils$tiltX(angle = 90) +
scale_x_discrete(limits = c(pdata$clusters$cluster[1:20], pdata$clusters$cluster[96:115]), position = "top") +
ylab("Number of genes") +
ggtitle("Number of genes by cluster")
xdata$plot
ggsave(filename = paste0(pdata$figuresDir, xdata$figName),
plot = xdata$plot, device = "eps", units = "cm", width = 35, height = 15)
# =================================================
# =================================================
# ++++++++++++++++++++==============================
# let's see how the clusters change after CCV correction
fdata <- list(); gc()
fdata$main <- read_rds(paste0(workspace$outputDir, "sc_clusters_integrity_in_bk_coefmats.rds"))
fdata$main %>% filter(dataset %in% c("velmeshev", "velmeshev-mgpres")) %>%
session$graphingUtils$ggplot(aes(y = cluster, x = coex, color = dataset)) +
geom_point(size = 4) +
ylim(c(pdata$clusters$cluster[1:20], pdata$clusters$cluster[96:115]))
# =================================================
# OUTPUT FIGURE
xdata <- list()
xdata$figName <- "figure_11.eps"
xdata$plot <- fdata$main %>%
filter(dataset %in% c("rosmap-ihc", "rosmap-ihcres")) %>%
dplyr::select(cluster, dataset, coex) %>%
spread(dataset, coex) %>%
mutate(diff = `rosmap-ihcres` - `rosmap-ihc`) %>%
session$graphingUtils$ggplot(aes(x = cluster, y = diff)) +
geom_bar(stat = "identity") +
xlim(c(pdata$clusters$cluster[1:20], pdata$clusters$cluster[96:115])) +
session$graphingUtils$tiltX(angle = 90) +
ggtitle("ROSMAP IHC")
xdata$plot
ggsave(filename = paste0(pdata$figuresDir, xdata$figName),
plot = xdata$plot, device = "eps", units = "cm", width = 35, height = 11)
# =================================================
# =================================================
# OUTPUT FIGURE
xdata <- list()
xdata$figName <- "figure_12.eps"
xdata$plot <- fdata$main %>%
filter(dataset %in% c("rosmap", "rosmap-mgpres")) %>%
dplyr::select(cluster, dataset, coex) %>%
spread(dataset, coex) %>%
mutate(diff = `rosmap-mgpres` - `rosmap`) %>%
session$graphingUtils$ggplot(aes(x = cluster, y = diff)) +
geom_bar(stat = "identity") +
xlim(c(pdata$clusters$cluster[1:20], pdata$clusters$cluster[96:115])) +
session$graphingUtils$tiltX(angle = 90) +
ggtitle("ROSMAP MGP")
xdata$plot
ggsave(filename = paste0(pdata$figuresDir, xdata$figName),
plot = xdata$plot, device = "eps", units = "cm", width = 35, height = 11)
# =================================================
# =================================================
# OUTPUT FIGURE
xdata <- list()
xdata$figName <- "figure_13.eps"
xdata$plot <- fdata$main %>%
filter(dataset %in% c("velmeshev", "velmeshev-mgpres")) %>%
dplyr::select(cluster, dataset, coex) %>%
spread(dataset, coex) %>%
mutate(diff = `velmeshev-mgpres` - `velmeshev`) %>%
session$graphingUtils$ggplot(aes(x = cluster, y = diff)) +
geom_bar(stat = "identity") +
xlim(c(pdata$clusters$cluster[1:20], pdata$clusters$cluster[96:115])) +
session$graphingUtils$tiltX(angle = 90) +
ggtitle("Velmeshev MGP")
xdata$plot
ggsave(filename = paste0(pdata$figuresDir, xdata$figName),
plot = xdata$plot, device = "eps", units = "cm", width = 35, height = 11)
# =================================================
# ++++++++++++++++=============================
# display cell type profiles for the clusters
# first Velmeshev (show in the main figures)
fdata <- list()
fdata$ctprofiles <- pdata$mkrs$minfc %>% dplyr::select(gene_id, cell_type, expr, dataset) %>%
filter(dataset == "velmeshev") %>%
mutate(expr = log2(expr + 1))
fdata$clusters <- read_rds(paste0(workspace$outputDir, paste0("sc_clusters_objects.rds")))
fdata$clusters <- fdata$clusters$clusterFlats %>% unname() %>% unlist(recursive = FALSE)
fdata$clusters <- fdata$clusters %>% session$collectionUtils$lapplyWithName(function(id, genes) {
tibble(cluster = id, gene_id = genes)
}) %>% session$dataWrangler$rbind()
fdata$main <- fdata$clusters %>% inner_join(fdata$ctprofiles, by = "gene_id")
fdata$main <- fdata$main %>% group_by(cell_type, cluster) %>% summarize(expr = mean(expr)) %>% ungroup()
fdata$main <- fdata$main %>% group_by(cluster) %>% mutate(expr = ((expr - mean(expr)))) %>% ungroup()
# =================================================
# OUTPUT FIGURE
xdata <- list()
xdata$figName <- "figure_14.eps"
xdata$main <- fdata$main %>% mutate(cell_type = workspace$utils$fmtCelltypes(cell_type))
xdata$plot <- xdata$main %>%
session$graphingUtils$ggplot(aes(x = cluster, y = cell_type)) +
geom_tile(aes(fill = expr)) +