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HU3 in MixPop.R
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HU3 in MixPop.R
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#loading tables:
library(readr)
library(readxl)
BGS_BPK475 <- read_delim("~/OneDrive - ITG/ITM/PhD/Results/WGS/2020-06-30 - WGS for somy estimation of 10X samples and barcoded BPK282/Pieter Analysis/BPK475 - 104123-001-021.mapq30.removedups.proper_paired.somy.csv",
"\t", escape_double = FALSE, trim_ws = TRUE)
BGS_BPK498 <- read_delim("~/OneDrive - ITG/ITM/PhD/Results/WGS/2020-06-30 - WGS for somy estimation of 10X samples and barcoded BPK282/Pieter Analysis/BPK498 - HG7FFDSXY_104123-001-022_GGCAAGTT-CTCAGAAG.mapq30.removedups.proper_paired.somy.csv",
"\t", escape_double = FALSE, trim_ws = TRUE)
BGS_HU3 <- read_delim("~/OneDrive - ITG/ITM/PhD/Results/WGS/2020-06-30 - WGS for somy estimation of 10X samples and barcoded BPK282/Pieter Analysis/HU3 - HG7FFDSXY_104123-001-019_TGGAAGCA-GAGAGTAC.mapq30.removedups.proper_paired.somy.csv",
"\t", escape_double = FALSE, trim_ws = TRUE)
BGS_BPK506 <- read_delim("~/OneDrive - ITG/ITM/PhD/Results/WGS/2020-06-30 - WGS for somy estimation of 10X samples and barcoded BPK282/Pieter Analysis/BPK506 - HG7FFDSXY_104123-001-020_CAATAGCC-TCTACGCA.mapq30.removedups.proper_paired.somy.csv",
"\t", escape_double = FALSE, trim_ws = TRUE)
BGS_BPK081 <- read_delim("~/OneDrive - ITG/ITM/PhD/Results/WGS/2020-06-30 - WGS for somy estimation of 10X samples and barcoded BPK282/Pieter Analysis/BPK081 - HG7FFDSXY_104123-001-018_CACAGACT-CTGTACCA.mapq30.removedups.proper_paired.somy.csv",
"\t", escape_double = FALSE, trim_ws = TRUE)
#getting the barcodes of the cells marked as HU3 based on SNPs
barcodes_HU3 <- barcodes_HU3 <- read_excel("inputs/barcodes_HU3.xlsx")
#gertting the barcodes of the doublets detected by the SNP method
doublets <- c("AATCCAGTCTGGTGGC-1", "ACATACGGTACCACTA-1", "ACTGCTCCAGGTCTGC-1", "AGCAGCCCACGTCGGT-1", "AGTGAGGGTGGCCACT-1", "ATCCGAAGTGCGAACA-1", "CACAAACTCATTCGCC-1", "CACCACTAGTCACGCC-1", "CAGAATCTCGTTATCT-1", "CAGATCACAGTAGAAT-1", "CCCTCCTCAGTTTGCA-1", "CGTGTCTCATCGCACG-1", "CTCGTACGTTCGGTGC-1", "CTCGTACCAACTCACA-1", "CTGGTCTCATCCCTTG-1", "GACGTTAAGATCTGAA-1", "GGCGACTAGCAGGTCA-1", "GGCTGGTAGTGATGAT-1", "TAGCCGGAGGCTCATT-1", "TGCACCTCAGGCCTTG-1", "TGGACGCGTGTGTATC-1")
#getting the BGS of all 204 strains from the eLife paper
df_204_strains <- read_excel("inputs/eLife_paper_donovani_strains.xlsx")
df_204_strains[,c(2:ncol(df_204_strains))] <- df_204_strains[,c(2:ncol(df_204_strains))]*2 #the values are normalized to 1. So here it multiplies everything by 2 to get the somy values.
BGS_BPK475 <- BGS_BPK475[-37,]
BGS_BPK498 <- BGS_BPK498[-37,]
BGS_HU3 <- BGS_HU3[-37,]
BGS_BPK506 <- BGS_BPK506[-37,]
BGS_BPK081 <- BGS_BPK081[-37,]
#inputs
HU3_barcodes <- barcodes_HU3$barcode
MixPop <- MixPop_integer_somies
MixPop_data <- MixPop_metrics
#adding barcodes to integer somies
integer_cells_barcodes <- MixPop_data$barcode[which(MixPop_data$cell_id %in% MixPop$cell)]
MixPop$barcode <- integer_cells_barcodes
#checking which cells are HU3 and doublets
MixPop <- MixPop %>%
mutate(is_HU3 = ifelse(barcode %in% HU3_barcodes, TRUE, FALSE)) %>%
mutate(is_doublet = ifelse(barcode %in% doublets, TRUE, FALSE))
MixPop_backup <- MixPop
MixPop <- filter(MixPop, baseline_ploidy == 2)
#clustering the cells and dividing in 4 groups
distances <- dist(MixPop[,2:37])
clusters <- hclust(distances, method = "ward.D2")
MixPop$cluster <- cutree(clusters, k = 4)
group_vec <- c("A", "D", "B", "C")
MixPop <- mutate(MixPop, group = group_vec[cluster])
MixPop_HU3 <- filter(MixPop, barcode %in% HU3_barcodes)
MixPop_A <- filter(MixPop, group == "A")
MixPop_B <- filter(MixPop, group == "B")
MixPop_C <- filter(MixPop, group == "C")
MixPop_D <- filter(MixPop, group == "D")
#ploting
ggplot(MixPop, aes(x = group, y = 1, color = is_HU3))+
geom_jitter(height = 0.5, size = 1.5, alpha = 0.75)+
scale_color_manual(name = "SNP Profile", labels = c("BPK", "HU3"), values = c("#383838", "orange")) +
scale_x_discrete(name = "Cluster")+
theme_bw()+
theme(axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.title.x = element_text(size = 14, face = 2))
BGSVsSCGS_HU3 <- data.frame(chromosome = c(1:36), BGS = BGS_HU3$somy_bin20000, SCGS = colMeans(MixPop_C[,2:37]), sample = "HU3")
BGSVsSCGS_BPK475 <- data.frame(chromosome = c(1:36), BGS = BGS_BPK475$somy_bin20000, SCGS = colMeans(MixPop_A[,2:37]), sample = "BPK475")
BGSVsSCGS_BPK498 <- data.frame(chromosome = c(1:36), BGS = BGS_BPK498$somy_bin20000, SCGS = colMeans(MixPop_B[,2:37]), sample = "BPK498")
BGSVsSCGS_BPK506 <- data.frame(chromosome = c(1:36), BGS = BGS_BPK506$somy_bin20000, SCGS = colMeans(MixPop_D[,2:37]), sample = "BPK506")
to_plot <- rbind(BGSVsSCGS_HU3, BGSVsSCGS_BPK475, BGSVsSCGS_BPK498, BGSVsSCGS_BPK506)
to_plot2 <- cbind(BGSVsSCGS_HU3, BGSVsSCGS_BPK475, BGSVsSCGS_BPK498, BGSVsSCGS_BPK506)
to_plot <- gather(to_plot, key = "method", value = "somy", -sample, - chromosome)
to_plot <- to_plot %>%
mutate(sample_label = ifelse(sample == "BPK475", "BPK475/Cluster A", ifelse(sample == "BPK498", "BPK498/Cluster B", ifelse(sample == "HU3", "HU3/Cluster C", "BPK506/Cluster D"))))
ggplot(to_plot, aes(x = factor(chromosome), y = somy, fill = method, facets = sample))+
geom_col(position = "dodge", color = "black")+
scale_x_discrete(name = "Chromosome") +
scale_fill_manual(name = "Data", values = c("black", "#68afc4"), labels = str_wrap(c("Strain Average Somies (BGS)", "Cluster Cumulative Somies (SCGS)"), 20))+
facet_grid(rows = vars(factor(sample_label, levels = c("BPK475/Cluster A", "BPK498/Cluster B", "HU3/Cluster C", "BPK506/Cluster D"))))+
theme(axis.title.y = element_text(size = 15),
axis.title.x = element_text(size = 15),
legend.title = element_text(size = 15, face = 2),
legend.text = element_text(size = 15, margin = margin(t = 10)),
legend.key = element_rect(size = 3, color = "white"),
legend.key.height = unit(20, "pt"),
legend.spacing.y = unit(15, "pt"),
strip.text = element_text(size = 13, face = 2))
df_compare <- data.frame(BPK081_old = df_204_strains$BPK081A1,
BPK081_new = BGS_BPK081$somy_bin20000,
BPK081_SCGS = colMeans(BPK081_integer_somies[,2:37]),
BPK475_old = df_204_strains$BPK475A1,
BPK475_new = BGS_BPK475$somy_bin20000,
cluster_A = BGSVsSCGS_BPK475$SCGS,
BPK498_old = df_204_strains$BPK498A1,
BPK498_new = BGS_BPK498$somy_bin20000,
cluster_B = BGSVsSCGS_BPK498$SCGS,
HU3_old = df_204_strains$LdonLV9,
HU3_new = BGS_HU3$somy_bin20000,
cluster_C = BGSVsSCGS_HU3$SCGS,
BPK506_old = df_204_strains$BPK506A1,
BPK506_new = BGS_BPK506$somy_bin20000,
cluster_D = BGSVsSCGS_BPK506$SCGS)
to_plot <- numeric.matrix(df_compare)
par(bg = "white")
heatmap.2(to_plot, key.xlab = "Somy",
Rowv = F,
Colv = F,
trace="none",
density = "density",
colsep = c(3,6,9,12),
linecol = 1,
col= brewer_palette(200),
#col = heat_palette(200),
breaks = seq(min_scale,max_scale, length = 201),
dendrogram = "both",
margins = c(7, 3),
cexCol=1,
key.title = "Color Key",
hclust=function(x) hclust(x,method = "ward.D2")
)
#HU3 SCGS Vs BGS
to_plot <- data.frame(chromosome = c(1:36), HU3_BGS = BGS_HU3$somy_bin20000, HU3_SCGS = colMeans(HU3_integer_somies[,2:37]))
to_plot <- gather(to_plot, key = "sample", value = "somy", -chromosome)
ggplot(to_plot, aes(x = factor(chromosome), y = somy, fill = sample))+
geom_col(position = "dodge")+
scale_fill_manual(values = c("black", "#68afc4"))
MixPop_A$sample <- "Cluster A"
MixPop_B$sample <- "Cluster B"
MixPop_C$sample <- "Cluster C"
MixPop_D$sample <- "Cluster D"
BPK081_integer_somies$sample <- "BPK081"
BPK282_integer_somies$sample <- "BPK282"
equalmix <- rbind(MixPop_A[sample(c(1:nrow(MixPop_A)), size = 250),which(colnames(MixPop_A) %in% colnames(BPK282_integer_somies))],
MixPop_B[sample(c(1:nrow(MixPop_B)), size = 250),which(colnames(MixPop_A) %in% colnames(BPK282_integer_somies))],
MixPop_C[sample(c(1:nrow(MixPop_C)), size = 250),which(colnames(MixPop_A) %in% colnames(BPK282_integer_somies))],
MixPop_D[sample(c(1:nrow(MixPop_D)), size = 250),which(colnames(MixPop_A) %in% colnames(BPK282_integer_somies))],
BPK081_integer_somies[sample(c(1:nrow(BPK081_integer_somies)), size = 250),],
BPK282_integer_somies[sample(c(1:nrow(BPK282_integer_somies)), size = 250),]
)
MixPop_backup <- MixPop_backup %>%
mutate(color = (ifelse(is_doublet == TRUE, "purple",
ifelse(is_HU3 == TRUE, "orange", "black"))))
to_cluster <- MixPop_backup
to_cluster <- hclust(dist(to_cluster[,2:37], method = "euclidean"), method = "ward.D2")
colors <- MixPop_backup$color[to_cluster$order]
to_plot <- MixPop_backup[to_cluster$order,]
to_plot <- to_plot[,-1]
to_plot <- to_plot[,1:36]
to_plot <- numeric.matrix(to_plot)
to_plot <- t(to_plot)
min_scale <- 0
max_scale <- round(max(to_plot))
max_scale <- 8
heatmap.2(to_plot,
main = paste(ncol(to_plot), "Cells from 4 strains"),
key.xlab = "Somy",
density = "density",
denscol = "black",
keysize = 1,
key.title = "Color Key",
Rowv = F,
#Colv = as.dendrogram(to_cluster),
trace="none",
#key = F,
linecol = 1,
col= color_palette(200),
ColSideColors = colors,
#col = heat_palette(200),
#adjCol = c(0.5,0.5),
breaks = seq(min_scale,max_scale, length = 201),
dendrogram = "both",
cexRow=1.8,
labCol = FALSE,
#labRow = FALSE,
hclust = function(x) hclust(x, method = "ward.D2")
)