rm(list=ls())
library(ggplot2)
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load(file="./_processed_data/PDL1_imm_objects.rdata")
load(file="./_processed_data/LAG3_imm_objects.rdata")
df_meta <- read.csv(file="./_processed_data/metadata.csv")
LAG3_markers <- c('NK_cell', 'NK_cell_LAG3', 'B_cell', 'B_cell_LAG3', 'DC', 'DC_LAG3', 'SOX2_DAPI_pos')
PDL1_markers <- c('M1_TAM', 'M1_TAM_PDL1', 'M2_TAM', 'M2_TAM_PDL1', 'CD15', 'CD15_PDL1', 'CD45RO', 'CD45RO_PDL1')
load("./_processed_data/LAG3_corr_filenames.RData") # corr_LAG3_filenames
load("./_processed_data/PDL1_corr_filenames.RData") # corr_PDL1_filenames
corr_filenames = rbind(corr_PDL1_filenames, corr_LAG3_filenames)List of samples: unique(df_meta$sample_id)
unique(df_meta$sample_id)## [1] "C003018" "C006279" "C006312" "C006864" "C007312" "C007313" "C007646"
## [8] "C007319" "C006887" "C005253" "C000116" "C001285" "C006807" "C007153"
## [15] "C008785" "C008811" "C009860" "C001944" "C001368" "C003205" "C004462"
## [22] "C000209" "C000233" "C000252" "C000458" "C000490" "C000498" "C001039"
## [29] "C002922" "C002923" "C000184" "C000468" "C001199" "C002644" "C003354"
## [36] "C006609" "C006770" "C007253" "C007649" "C007675" "C008391" "C008977"
## [43] "C004752" "C003130" "C006578" "C008690" "C006033" "C007604" "C006693"
## [50] "C008277" "C008279" "C009812"sample_toUse <- "C006864"
print(paste0("Generating pseudo-image for sample: ", as.character(sample_toUse)))## [1] "Generating pseudo-image for sample: C006864"findfile = df_meta %>% select(filename_clean,
sample_id, multiplex, cycle, run, sample_type) %>% unique(.) %>%
plyr::join(., corr_filenames) %>%
filter(sample_id == sample_toUse)## Joining by: filename_cleanfindfile_PDL1_primary = findfile %>% filter(multiplex == "PDL1" & sample_type == "A") %>% .$filename_object
print(paste0("Using PDL1 file: ", as.character(findfile_PDL1_primary)))## [1] "Using PDL1 file: C006864_PDL1_2_R2_A.tif"findfile_LAG3_primary = findfile %>% filter(multiplex == "LAG3" & sample_type == "A") %>% .$filename_object
print(paste0("Using LAG3 file: ", as.character(findfile_LAG3_primary)))## [1] "Using LAG3 file: C006864_LAG3_2_R2_A.tif"# ggplot(df_PDL1_imm_only %>% filter(filename == "C004462_PDL1_R2_A.tif" &
# ggplot(df_PDL1_imm_only %>% filter(filename == "C000490_PDL1_R2_A.tif" &
# `Analysis Region` == "TC")) +
p_pdl1 <- ggplot(df_PDL1_imm_only %>% filter(filename == findfile_PDL1_primary)) +
# geom_point(aes(x=X.Coord, y=Y.Coord, col=factor(M2_TAM)), size=0.0001) +
geom_point(data = . %>% filter(Epithelial_cell == 1), aes(x=X.Coord, y=Y.Coord, color='Epithelial cell'),
alpha=1/10, size=0.0001) +
geom_point(data = . %>% filter(Epithelial_cell == 0), aes(x=X.Coord, y=Y.Coord, color='Stroma'),
alpha=1/10, size=0.0001) +
geom_point(data = . %>% filter(CD15 == 1), aes(x=X.Coord, y=Y.Coord, color='CD15'), size=0.1) +
geom_point(data = . %>% filter(CD15_PDL1 == 1), aes(x=X.Coord, y=Y.Coord, color='CD15_PDL1'), alpha=1/2, size=0.1) +
geom_point(data = . %>% filter(M2_TAM == 1), aes(x=X.Coord, y=Y.Coord, color='M2_TAM'), size=0.1) +
geom_point(data = . %>% filter(M2_TAM_PDL1 == 1), aes(x=X.Coord, y=Y.Coord, color='M2_TAM_PDL1'),alpha=1/2, size=0.1) +
geom_point(data = . %>% filter(M1_TAM == 1), aes(x=X.Coord, y=Y.Coord, color='M1_TAM'), size=0.1) +
geom_point(data = . %>% filter(M1_TAM_PDL1 == 1), aes(x=X.Coord, y=Y.Coord, color='M1_TAM_PDL1'),alpha=1/2, size=0.1) +
# scale_color_manual(values = c("grey", "red", "blue")) +
scale_colour_manual(name = "Cell type", drop=FALSE,
values = c('Epithelial cell' = 'black',
'Stroma' = 'lightyellow',
'CD15'='lightgreen',
'CD15_PDL1'='darkgreen',
'M1_TAM'='lightpink',
'M1_TAM_PDL1' = 'red',
'M2_TAM'='lightblue',
'M2_TAM_PDL1' = 'blue'),
breaks=c('Epithelial cell', 'Stroma', 'CD15', 'CD15_PDL1', 'M1_TAM', 'M1_TAM_PDL1','M2_TAM','M2_TAM_PDL1')) +
guides(colour = guide_legend(override.aes = list(size=3, shape=19))) +
theme_minimal() +
coord_equal(expand=FALSE) +
theme(panel.background = element_rect(linewidth=1, fill=NA), legend.position = "right",
strip.text = element_text(face="bold", size=12),
axis.title = element_blank(), axis.text = element_blank()) +
# facet_grid(`Analysis Region`~filename, switch="y")
facet_wrap(~filename)# ggplot(df_LAG3_imm_only %>% filter(filename == "C000490_LAG3_R2_A.tif" &
# ggplot(df_LAG3_imm_only %>% filter(filename == "C004462_LAG3_R2_A.tif" &
# ggplot(df_LAG3_imm_only %>% filter(filename == "C000252_LAG3_R2_A.tif" &
# ggplot(df_LAG3_imm_only %>% filter(filename == "C000233_LAG3_R2_A.tif" &
# `Analysis Region` == "TC")) +
# p_lag3 <- ggplot(df_LAG3_imm_only %>% filter(filename == "C003130_LAG3_2_R1_A.tif")) +
p_lag3 <- ggplot(df_LAG3_imm_only %>% filter(filename == findfile_LAG3_primary)) +
# geom_point(aes(x=X.Coord, y=Y.Coord, col=factor(M2_TAM)), size=0.0001) +
geom_point(data = . %>% filter(Epithelial == 1), aes(x=X.Coord, y=Y.Coord, color='Epithelial cell'),
alpha=1/10, size=0.0001) +
geom_point(data = . %>% filter(Epithelial == 0), aes(x=X.Coord, y=Y.Coord, color='Stroma'),
alpha=1/2, size=0.0001) +
geom_point(data = . %>% filter(NK_cell_noLAG3 == 1), aes(x=X.Coord, y=Y.Coord, color='NK_cell_noLAG3'), size=0.1) +
geom_point(data = . %>% filter(NK_cell_LAG3 == 1), aes(x=X.Coord, y=Y.Coord, color='NK_cell_LAG3'), size=0.1) +
geom_point(data = . %>% filter(B_cell_noLAG3 == 1), aes(x=X.Coord, y=Y.Coord, color='B_cell_noLAG3'), size=0.1) +
geom_point(data = . %>% filter(B_cell_LAG3 == 1), aes(x=X.Coord, y=Y.Coord, color='B_cell_LAG3'), size=0.1) +
geom_point(data = . %>% filter(DC_noLAG3 == 1), aes(x=X.Coord, y=Y.Coord, color='DC_noLAG3'), size=0.1) +
geom_point(data = . %>% filter(DC_LAG3 == 1), aes(x=X.Coord, y=Y.Coord, color='DC_LAG3'), size=0.1) +
# geom_point(data = . %>% filter(SOX2_DAPI_neg == 1), aes(x=X.Coord, y=Y.Coord, color='SOX2_DAPI_neg'), size=0.1) +
geom_point(data = . %>% filter(SOX2_DAPI_pos == 1), aes(x=X.Coord, y=Y.Coord, color='SOX2_DAPI_pos'), size=0.1) +
# scale_color_manual(values = c("grey", "red", "blue")) +
scale_colour_manual(name = "Cell type", drop=FALSE,
values = c('Epithelial cell' = 'grey30',
'Stroma' = 'lightyellow',
'NK_cell_noLAG3'='#b8e186',
'NK_cell_LAG3'='#4dac26',
'B_cell_noLAG3'='#f1b6da',
'B_cell_LAG3' = '#d01c8b',
'DC_noLAG3'='#92c5de',
'DC_LAG3' = '#0571b0',
# 'SOX2_DAPI_neg'='#c2a5cf',
'SOX2_DAPI_pos' = '#7b3294'),
breaks=c('Epithelial cell', 'Stroma', 'NK_cell_noLAG3', 'NK_cell_LAG3',
'B_cell_noLAG3', 'B_cell_LAG3','DC_noLAG3','DC_LAG3', #'SOX2_DAPI_neg',
'SOX2_DAPI_pos')) +
guides(colour = guide_legend(override.aes = list(size=3, shape=19))) +
theme_minimal() +
coord_equal(expand=FALSE) +
theme(panel.background = element_rect(linewidth=1, fill=NA), legend.position = "right",
strip.text = element_text(face="bold", size=12),
axis.title = element_blank(), axis.text = element_blank()) +
# facet_grid(`Analysis Region`~filename, switch="y")
facet_wrap(~filename) #+
# geom_polygon(aes(x = X.Coord, y = Y.Coord, colour=issue), fill=NA, data = hulls, alpha = 0.5)# p_region <- ggplot(df_LAG3_imm_only %>% filter(filename == "C003130_LAG3_2_R1_A.tif")) +
p_region <- ggplot(df_LAG3_imm_only %>% filter(filename == findfile_LAG3_primary)) +
geom_point(aes(x=X.Coord, y=Y.Coord, col=factor(`Analysis Region`)), size=0.0001, alpha=1/10) +
guides(colour = guide_legend(override.aes = list(size=3, shape=19, alpha=1))) +
theme_minimal() +
coord_equal(expand=FALSE) +
theme(panel.background = element_rect(linewidth=1, fill=NA), legend.position = "right",
strip.text = element_text(face="bold", size=12),
axis.title = element_blank(), axis.text = element_blank()) +
scale_colour_manual(name = "Analysis Region", drop=FALSE,
values = c('IMT3'='#fde0ef',
'IMT2' = '#e9a3c9',
'IMT1' = '#c51b7d',
'IMS3' = '#4d9221',
'IMS2'='#7fbc41',
'IMS1'='#b8e186',
'TC'='grey30',
'SC' = 'grey70'),
breaks = c("TC","IMT3", "IMT2", "IMT1", "IMS1", "IMS2", "IMS3", "SC")) +
facet_wrap(~filename) #+
# geom_point(data = hulls, aes(x = X.Coord, y = Y.Coord, colour=issue), shape=20) #+
# geom_polygon(data = hulls, aes(x = X.Coord, y = Y.Coord, colour=issue), fill=NA, alpha = 0.5)p_classifier <- ggplot(df_LAG3_imm_only %>% filter(filename == findfile_LAG3_primary)) +
geom_point(aes(x=X.Coord, y=Y.Coord, col=factor(`Classifier Label`)), size=0.0001, alpha=1/10) +
guides(colour = guide_legend(override.aes = list(size=3, shape=19, alpha=1))) +
theme_minimal() +
coord_equal(expand=FALSE) +
theme(panel.background = element_rect(linewidth=1, fill=NA), legend.position = "right",
strip.text = element_text(face="bold", size=12),
axis.title = element_blank(), axis.text = element_blank()) +
scale_colour_manual(name = "Classifier Label", drop=FALSE,
values = c('KP'='#1f78b4',
'EDP' = '#b2df8a',
'DP' = '#33a02c',
'UDP' = '#fb9a99',
'Tumour'='#e31a1c',
'Stroma' = 'grey'),
breaks = c("KP","EDP", "DP", "UDP", "Tumour", "Stroma")) +
facet_wrap(~filename) #+
# geom_point(data = hulls, aes(x = X.Coord, y = Y.Coord, colour=issue), shape=20) #+
# geom_polygon(data = hulls, aes(x = X.Coord, y = Y.Coord, colour=issue), fill=NA, alpha = 0.5)p<- plot_grid(p_classifier, p_region, p_pdl1, p_lag3)
ggsave(p, height=12, width=16, filename=paste0("./_results/pseudoimage_", unique(findfile$sample_id), ".pdf"))# tt <- df_LAG3_imm_only %>% filter(filename == "C003130_LAG3_2_R1_A.tif") %>% select(issue=`Analysis Region`, X.Coord, Y.Coord)
#
# #getting the convex hull of each unique point set
# find_hull <- function(df) df[chull(df$X.Coord, df$Y.Coord), ]
# hulls <- ddply(tt, "issue", find_hull)
#
# plot <- ggplot(data = tt, aes(x = X.Coord, y = Y.Coord, colour=issue, fill = issue)) +
# # geom_point() +
# geom_polygon(data = hulls, alpha = 0.5) +
# labs(x = "Efficiency", y = "Mandate")
# plot
#