sample <- readRDS(path.to.SC.sample)
sample$Full_Annotation <- as.character(sample$Full_Annotation)
# S10A
p1 <- SCpubr::do_EnrichmentHeatmap(sample,
flavor = "UCell",
input_gene_list = ATRT_RNA_Xenium_AddOn_Panel,
group.by = "Full_Annotation",
max.cutoff = 0.5,
groups.order = list("Full_Annotation" = rev(c("IPC-like",
"CP-like",
"Cilia-like",
"RG-like",
"NPC-like",
"OPC-like",
"Mesenchymal-like",
"Hypoxic",
"Immune-like",
"NMF-MP2",
"NMF-MP3",
"NMF-MP5",
"NMF-MP8",
"SHH-1",
"SHH-2",
"SHH-3",
"MYC-1",
"Unannotated",
"Astrocytes",
"Neurons",
"OPC",
"Microglia & Immune",
"Endothelial",
"Pericytes"))),
features.order = c("IPC-like",
"CP-like",
"Cilia-like",
"RG-like",
"NPC-like",
"OPC-like",
"Mesenchymal-like",
"Hypoxic",
"Immune-like",
"NMF-MP2",
"NMF-MP3",
"NMF-MP5",
"NMF-MP8",
"SHH-1",
"SHH-2",
"SHH-3",
"MYC-1",
"Unannotated",
"Astrocytes",
"Neurons",
"OPC",
"Microglia & Immune",
"Endothelial",
"Pericytes"))
sample$Full_Annotation <- factor(sample$Full_Annotation, levels = c("IPC-like",
"CP-like",
"Cilia-like",
"RG-like",
"NPC-like",
"OPC-like",
"Mesenchymal-like",
"Hypoxic",
"Immune-like",
"NMF-MP2",
"NMF-MP3",
"NMF-MP5",
"NMF-MP8",
"SHH-1",
"SHH-2",
"SHH-3",
"MYC-1",
"Unannotated",
"Astrocytes",
"Neurons",
"OPC",
"Microglia & Immune",
"Endothelial",
"Pericytes"))
# S10B
p2 <- SCpubr::do_DotPlot(sample,
features = ATRT_RNA_Xenium_AddOn_Panel[c("IPC-like",
"CP-like",
"Cilia-like",
"RG-like",
"NPC-like",
"OPC-like",
"Mesenchymal-like",
"Hypoxic",
"Immune-like",
"NMF-MP2",
"NMF-MP3",
"NMF-MP5",
"NMF-MP8",
"SHH-1",
"SHH-2",
"SHH-3",
"MYC-1",
"Unannotated",
"Astrocytes",
"Neurons",
"OPC",
"Microglia & Immune",
"Endothelial",
"Pericytes")],
group.by = "Full_Annotation",
max.cutoff = 2)Figure S10
# Code commented due to compilation problems with Quarto.
# # S10C
# import numpy as np
# import pandas as pd
#
# import matplotlib.pyplot as plt
# import seaborn as sns
#
# import scanpy as sc
# import squidpy as sq
#
# import os
# from copy import deepcopy
#
# #### This script was run per tumor sample to generate the plots in Figures 3C and S7C, but the steps are identical
#
# # Read expression matrix
# adata = sc.read_10x_h5(
# filename = "cell_feature_matrix.h5"
# )
#
# # Read the cell info file
# df = pd.read_csv(
# "cells.csv.gz"
# )
#
# df.set_index(adata.obs_names, inplace = True)
# adata.obs = df.copy()
#
# adata.obsm["spatial"] = adata.obs[["x_centroid","y_centroid"]].copy().to_numpy()
#
# # Read RCTD annotations
# annotations = pd.read_csv(
# "ANNOTATIONS.csv"
# )
#
# adata_sub = adata[annotations["cell_id"]].copy()
# del(adata)
#
# annotations.set_index(adata_sub.obs_names, inplace = True)
# adata_sub.obs["type"]= annotations["group"].copy().astype('category')
#
# import matplotlib.colors
#
# colors = ["#BA531CFF","#be660e","#be0e0e","#5E4CCDFF","#be920e","#0ebe66","#0092AAFF","#a32978","#0466c8",
# "#BFBFBF","#787F00FF","#009257FF","#0435c8", "#0497c8"]
#
# cmap = matplotlib.colors.ListedColormap(colors)
#
#
# sq.pl.spatial_scatter(adata_sub,
# shape = None,
# color = "type",
# size = 1,
# library_id = "spatial",
# img = False,
# figsize = (15,15),
# palette = cmap,
# frameon = False,
# colorbar = False,
# title = "",
# legend_loc = None
# )
#
#
#
# ```
#
# ```{r}
# #| eval: false
#
# # S10D
#
# library(Seurat)
# library(tidyverse)
#
#
# srat <- readRDS("OBJECT.rds") # Read Seurat object containing annotated cells (post-normalization)
# data <- srat@meta.data %>% select(group, sample)
#
# # Build proportion table
# df <- as.data.frame(prop.table(table(data$group,data$sample), 2))
#
#
# df$subtype <- plyr::mapvalues(df$Var2,
# from = c("ATRT-05",
# "ATRT-15-RV4",
# "ATRT-173",
# "ATRT-207",
# "ATRT-243",
# "ATRT-256",
# "ATRT-340"),
# to = c("ATRT-SHH","ATRT-TYR","ATRT-SHH","ATRT-MYC","ATRT-MYC","ATRT-SHH","ATRT-TYR")
# )
# colnames(df) <- c("Annotation","Tumor","Freq","Subtype")
#
#
#
# cpal <- c("Astrocytes" = "#BA531CFF",
# "Cilia-like" = "#be0e0e",
# "CP-like" = "#be660e",
# "Endothelial" = "#5E4CCDFF",
# "IPC-like" = "#be920e",
# "Mesenchymal-like" = "#0ebe66",
# "Microglia & immune" = "#0092AAFF",
# "Pericytes" = "#a32978",
# "Neurons" = "#787F00FF",
# "NPC-like" = "#0466c8",
# "OPC" = "#009257FF",
# "OPC-like" = "#0435c8",
# "RG-like" = "#0497c8",
# "Necrotic" = "grey55")
#
# df$Annotation <- factor(df$Annotation, levels = c("Astrocytes",
# "Neurons",
# "OPC",
# "Microglia & immune",
# "Endothelial",
# "Pericytes",
# "IPC-like",
# "CP-like",
# "Cilia-like",
# "OPC-like",
# "NPC-like",
# "RG-like",
# "Mesenchymal-like",
# "Necrotic"))
#
#
#
# df$Tumor <- factor(df$Tumor, levels = c("ATRT-05",
# "ATRT-173",
# "ATRT-256",
# "ATRT-15-RV4",
# "ATRT-340",
# "ATRT-207",
# "ATRT-243"))
#
# ggplot(df, aes(x = Tumor, y = Freq, fill = Annotation)) +
# geom_bar(position = "fill", stat = "identity", color = "black", linewidth = 0.1) +
# xlab("") +
# ylab("Proportion of tumor cells") +
# scale_fill_manual(values = cpal) +
# theme_classic() +
# facet_grid(cols = vars(Subtype), scales = "free", space = "free") +
# theme(legend.title = element_blank(),
# legend.text = element_text(size = 6, color = "black"),
# legend.position = "none",
# axis.title = element_text(size = 6, colour = "black"),
# axis.text = element_text(size = 6, color = "black"),
# axis.text.x = element_text(angle = 90, hjust = 1),
# axis.ticks = element_line(size = 0.25),
# axis.line = element_line(size = 0.25),
# strip.text = element_text(size = 6, color = "black"),
# strip.background = element_blank(),
# legend.key.size = unit(3,"mm"),
# legend.key.spacing.y = unit(1,"mm"))
#
# # S10E
# library(Seurat)
# library(tidyverse)
#
# srat <- readRDS("OBJECT.rds") # Read Seurat object containing annotated cells (post-normalization)
#
# # Remove Non-malignant cells
# Idents(srat) <- "group"
# srat <- subset(srat, cells = WhichCells(srat, idents = c("Astrocytes",
# "Neurons",
# "OPC",
# "Microglia_Immune",
# "Endothelial",
# "Mural")), invert = T)
#
#
# # Use the Seurat built-in DotPlot function to pull the necessary data easily
# data <-DotPlot(srat, features = c("CCNB2","CDK1","CENPF",
# "MKI67","PCNA",
# "TOP2A"))$data
#
# # Re-order IDs for plotting
# data$id <- factor(data$id, levels = rev(c("IPC-like",
# "CP-like",
# "Cilia-like",
# "OPC-like",
# "NPC-like",
# "RG-like",
# "Mesenchymal-like")),
# labels = rev(c("IPC-like",
# "CP-like",
# "Cilia-like",
# "OPC-like",
# "NPC-like",
# "RG-like",
# "Mesenchymal-like")))
#
# ggplot(data, aes(y = id, x = features.plot, size = pct.exp, fill = avg.exp.scaled)) +
# geom_point(shape=21, stroke = 0.1) +
# theme_classic() +
# scale_size_area(max_size = 3.5) +
# scale_fill_distiller(palette = "RdBu", limits=c(-2.5,2.5)) +
# labs(fill="Average expression", size = "Percent expressed") +
# xlab("") +
# ylab("") +
# theme(axis.text.y = element_text(size=6, color = "black"),
# axis.text.x = element_text(size=5, color = "black", face = "italic"),
# legend.text = element_text(size=6, color = "black"),
# legend.title = element_text(size=6, color = "black"),
# legend.key.size = unit(0.75, 'lines'),
# legend.position = "none",
# axis.ticks = element_line(size = 0.25),
# axis.line = element_line(size = 0.25))