VisiumHD Colorectal Cancer Datasets

P1

# apply highSpaClone to infer spatial CNV and detect tumor subclones
library(highSpaClone)
library(ggplot2)
library(dplyr)

# load data
data <- readRDS('./visiumhd_colon_p1_data.rds')

# create highSpaClone object
obj <- createObject(counts=data$counts,
                    location=data$location,
                    annotations_file = data$annotation,
                    min_avg_expression=0,
                    min_gene_counts=0)

# smooth gene expression by chromosome
smooth_obj <- smooth_expr(obj = obj, parallel = T, use_chunk = T)

# tumor identification
tumor_obj <- FindClone(smooth_obj,
                      ref=c("B cells","T cells", "Smooth Muscle", "Endothelial"),
                      K=2,
                      lambda=0.001)

# tumor spatial visualization
colors <- c("#ebe5c2", "#D57358")
spatialplot(tumor_obj, colors=colors, point_size = 0.01, use_coord_flip=T, use_x_reverse=T)

tumor.df <- tumor_obj@cluster
tumor.id <- tumor.df %>% filter(cell.label=='Tumor') %>% select(cell.id)
subclone_obj <- FindClone(smooth_obj,
                          ref=c("B cells","T cells", "Smooth Muscle", "Endothelial"),
                          tumor.id=tumor.id$cell.id,
                          K=2,
                          lambda=1)

colors <- c("#D57358","#8a508f")
spatialplot(subclone_obj,  colors=colors, point_size = 0.01, use_coord_flip=T, use_x_reverse=T)

# CNV heatmap
log.cnv <- log2(subclone_obj@cnv.data)
log.cnv[log.cnv > -0.5 & log.cnv < 0.5] <- 0

visiumhd_colon_p1 <- list(
  cnv.data   = log.cnv,
  annotation = subclone_obj@cluster,
  chr_pos    = subclone_obj@chr_pos
)

cnv_heatmap(
  cnv.obj=visiumhd_colon_p1,
  groupby = "cell.label",
  min_value  = -3,  
  max_value  = 3,
  auto_size = F,
  show    = TRUE,       
  save    = TRUE,         
  outfile = "visiumhd_colon_p1_heatmap.pdf" 
)

P2

# load data
data <- readRDS('./visiumhd_colon_p2_data.rds')

# create highSpaClone object
obj <- createObject(counts=data$counts,
                    location=data$location,
                    annotations_file = data$annotation,
                    min_avg_expression=0,
                    min_gene_counts=0)

# smooth gene expression by chromosome
smooth_obj <- smooth_expr(obj = obj, parallel = T, use_chunk = T)

# tumor identification
tumor_obj <- FindClone(smooth_obj,
                      ref=c("B cells","T cells", "Smooth Muscle", "Endothelial"),
                      K=2,
                      lambda=0.001)

# tumor spatial visualization
colors <- c("#ebe5c2", "#D57358")
spatialplot(tumor_obj, colors=colors, point_size = 0.01, use_coord_flip=T, use_x_reverse=T)

tumor.df <- tumor_obj@cluster
tumor.id <- tumor.df %>% filter(cell.label=='Tumor') %>% select(cell.id)
subclone_obj <- FindClone(smooth_obj,
                          ref=c("B cells","T cells", "Smooth Muscle", "Endothelial"),
                          tumor.id=tumor.id$cell.id,
                          K=2,
                          lambda=1)

colors <- c("#D57358","#8a508f")
spatialplot(subclone_obj,  colors=colors, point_size = 0.01, use_coord_flip=T, use_x_reverse=T)

# CNV heatmap
log.cnv <- log2(subclone_obj@cnv.data)
log.cnv[log.cnv > -0.5 & log.cnv < 0.5] <- 0

visiumhd_colon_p2 <- list(
  cnv.data   = log.cnv,
  annotation = subclone_obj@cluster,
  chr_pos    = subclone_obj@chr_pos
)

cnv_heatmap(
  cnv.obj=visiumhd_colon_p2,
  groupby = "cell.label",
  min_value  = -3,  
  max_value  = 3,
  auto_size = F,
  show    = TRUE,       
  save    = TRUE,         
  outfile = "visiumhd_colon_p2_heatmap.pdf" 
)

P5

# load data
data <- readRDS('./visiumhd_colon_p5_data.rds')

# create highSpaClone object
obj <- createObject(counts=data$counts,
                    location=data$location,
                    annotations_file = data$annotation,
                    min_avg_expression=0,
                    min_gene_counts=0)

# smooth gene expression by chromosome
smooth_obj <- smooth_expr(obj = obj, parallel = T, use_chunk = T)

# tumor identification
tumor_obj <- FindClone(smooth_obj,
                      ref=c("B cells","T cells", "Smooth Muscle", "Endothelial"),
                      K=2,
                      lambda=0.001)

# tumor spatial visualization
colors <- c("#ebe5c2", "#D57358")
spatialplot(tumor_obj, colors=colors, point_size = 0.01, use_coord_flip=T, use_x_reverse=T)

tumor.df <- tumor_obj@cluster
tumor.id <- tumor.df %>% filter(cell.label=='Tumor') %>% select(cell.id)
subclone_obj <- FindClone(smooth_obj,
                          ref=c("B cells","T cells", "Smooth Muscle", "Endothelial"),
                          tumor.id=tumor.id$cell.id,
                          K=2,
                          lambda=1)

colors <- c("#D57358","#8a508f")
spatialplot(subclone_obj,  colors=colors, point_size = 0.01, use_coord_flip=T, use_x_reverse=T)

# CNV heatmap
log.cnv <- log2(subclone_obj@cnv.data)
log.cnv[log.cnv > -0.5 & log.cnv < 0.5] <- 0

visiumhd_colon_p5 <- list(
  cnv.data   = log.cnv,
  annotation = subclone_obj@cluster,
  chr_pos    = subclone_obj@chr_pos
)

cnv_heatmap(
  cnv.obj=visiumhd_colon_p5,
  groupby = "cell.label",
  min_value  = -3,  
  max_value  = 3,
  auto_size = F,
  show    = TRUE,       
  save    = TRUE,         
  outfile = "visiumhd_colon_p5_heatmap.pdf" 
)