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Transcriptomic_analyses.Rmd
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---
title: "Unraveling the immunogenomic landscape of TNBC"
subtitle: "Transcriptomic analyses"
author: "Nuria Gendrau-Sanclemente"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
theme: cosmo
highlight: pygments
df_print: paged
code_folding: hide
fig_align: center
toc: true
toc_float: true
toc_depth: 3
number_sections: yes
---
```{r}
library(argparse)
library(SingleCellExperiment)
library(DropletUtils)
library(scuttle)
library(Seurat)
library(dplyr)
library(ggplot2)
library(tidyverse)
library(dsb)
library(tibble)
library(cowplot)
library(pheatmap)
library(stringr)
library(readxl)
library(fgsea)
library(tibble)
library(scran)
library(scater)
library(glue)
library(org.Hs.eg.db)
library(readr)
library(UCell)
library(reshape)
library(scclusteval)
library(miloR)
library(edgeR)
library(limma)
library(patchwork)
library(magrittr)
library(viridis)
library(scCustomize)
library(qs)
library(anndata)
library(LoomExperiment)
library(reticulate)
library(sceasy)
library(zellkonverter)
library(patchwork)
library(SeuratData)
library(harmony)
library(BiocManager)
library(Nebulosa)
library(crossmatch)
library(multicross)
library(SCPA)
library(msigdbr)
```
# All cells
The first thing we have to do is to load the `h5ad` object that we've saved after inferring CNV. We'll start using data from **all cells**:
```{r}
adata <- anndata::read_h5ad("adata_post_infercnv.h5ad")
```
Explore it. We get the same output that we see in Python.
```{r}
adata
```
```{r}
metadata <- adata$obs %>% as.data.frame()
seurat_obj <- CreateSeuratObject(counts = adata$X,
meta.data = metadata,
assay = "RNA",
min.cells = 3, min.features = 200)
```
Cell names are stored in `seurat_obj@assays$RNA@[email protected] %>% rownames`
```{r}
seurat_obj@assays$RNA@[email protected] %>% rownames %>% head
```
Our counts matrix is sparse and has 16258 genes (cols) x 40785 cells (rows)
```{r}
seurat_obj@assays$RNA@layers$counts %>% dim %>% table
```
Turn this matrix into a dense one and then add the cell names as rownames:
```{r}
counts_seurat_dense <- as.matrix(seurat_obj@assays$RNA@layers$counts)
```
```{r}
counts_seurat_dense %>% dim %>% table
```
```{r}
rownames(counts_seurat_dense) <- rownames(seurat_obj@assays$RNA@[email protected])
counts_seurat_dense %>% rownames %>% head
```
We can add now the gene names as colnames to our matrix. They are stored in:
```{r}
seurat_obj@assays$RNA@[email protected] %>% rownames %>% head(20)
```
```{r}
colnames(counts_seurat_dense) <- rownames(seurat_obj@assays$RNA@[email protected])
counts_seurat_dense %>% colnames %>% head
```
```{r}
counts_seurat_dense[1:5,1:5]
```
Now we will add all the information contained in `adata.obs` (cell_name, mouse, batch, strain, cell_type, cnv_leiden and cnv_score):
```{r}
adata$obs %>% head
```
Now we have a dataframe called `d` where wach row is a cell, and in the columns we have the metadata information (including CNV Leiden clusters from CNV inference by `invercnvpy`) plus gene expression.
```{r}
alldata <- counts_seurat_dense %>% as.data.frame
obs <- adata$obs
d <- cbind(obs, alldata)
d %>% head
```
Work with our Seurat object
```{r}
mutate(cell_name = metadata$cell_name) %>%
mutate(cell_type = metadata$cell_type) %>%
mutate(strain = metadata$strain) %>%
mutate(mouse = metadata$mouse) %>%
mutate(cnv_cluster = metadata$cnv_leiden)
```
```{r}
[email protected] %>% head
```
```{r}
so2 <- readRDS("updatedProjected.rds")
so2
```
```{r}
seurat_obj %>% dim
```
```{r}
[email protected] %>% head
```
```{r}
[email protected] %>% dim
```
```{r}
metadata %>% head
```
```{r}
mutate(cell_name = metadata$cell_name) %>%
mutate(cell_type = metadata$cell_type) %>%
mutate(strain = metadata$strain) %>%
mutate(mouse = metadata$mouse) %>%
mutate(cnv_cluster = metadata$cnv_leiden)
[email protected] %>% dim
```
```{r}
[email protected] %>% head
```
```{r}
[email protected] %>% colnames
```
Process the Seurat object
```{r}
so_proc <- so2 %>%
NormalizeData(normalization.method = "LogNormalize", scale.factor = 10000) %>%
FindVariableFeatures( selection.method = "vst", nfeatures = 2000) %>%
ScaleData() %>%
RunPCA() %>%
RunHarmony(group.by.vars = "strain", dims.use = 1:30) %>%
RunUMAP(reduction = "harmony", dims = 1:30) %>%
FindNeighbors(reduction = "harmony", dims = 1:30) %>%
FindClusters(resolution = 0.6)
```
Plot UMAP
```{r}
custcols <- cust_cols <- c("#f6b26b", "#ff746f", "#8c5e78", "#f6a6b2", "#90d2d8", "#c79dd7", "#c2f2d0","#01aeb5","#A88C8C", "#D9BF77","#668D3C", "#EAD3BF", "#A77B65", "#ff80ed","#565656", "#5281A2","#12d4a2","#f8ff99")
Idents(so_proc)<- [email protected]$cell_type
Idents(so_proc) <- factor(Idents(so_proc),
levels = c("B.cell","T.cell","DC","pDC","Endothelial",
"Fibroblast","Macrophage","Neutrophil","Osteoclast",
"pericyte","Cancer.cell"))
so_proc$cell_type <- factor(so_proc$cell_type, levels = c("B.cell","T.cell","DC","pDC","Endothelial",
"Fibroblast","Macrophage","Neutrophil","Osteoclast",
"pericyte","Cancer.cell"))
DimPlot(so_proc, reduction = "umap", group.by = c("strain", "mouse", "cell_type", "cnv_cluster"), ncol = 2, cols = custcols)
```
Plot a dotplot
```{r}
Idents(so_proc)<- [email protected]$cell_type
Idents(so_proc) <- factor(Idents(so_proc),
levels = c("B.cell","T.cell","DC","pDC","Endothelial",
"Fibroblast","Macrophage","Neutrophil","Osteoclast",
"pericyte","Cancer.cell"))
markers <- c("Cd79a","Ms4a1","Cd79b",
"Cd3e","Cd3g","Cd3d",
"Cd80","Itgae","Mmp12",
"Flt3","Cd8a","Cdh22",
"Cd34", "Cd36","Cdh13",
"Dpt","Mfap5","Pdgfra",
"Msr1","Mrc1", "Cd86",
"Nlrp3","G0s2","Cxcr2",
"Atp6v0d2","Acp5","Tnfrsf11a",
"Rgs5","Myh11","Des",
"Epcam","Krt8", "Krt18")
DotPlot(so_proc, features = markers, cols = c("#fcfdbf", "#51127c"),
dot.scale = 6) +
RotatedAxis() +
theme(axis.text.y = element_text(size = 7, colour = "black")) +
theme(axis.text.x = element_text(size = 7, colour = "black")) +
theme(axis.title.y = element_text(size = 8, colour = "black")) +
theme(axis.title.x = element_text(size = 8, colour = "black")) +
theme(axis.ticks = element_blank()) +
theme(legend.title = element_text(size = 6),
legend.title.align = 0.5,
legend.text = element_text(size = 5),
legend.key.size = unit(0.2, 'cm'),
legend.key.height = unit(0.2, 'cm'),
legend.key.width = unit(0.2, 'cm'))
```
We can do a more elaborated dotplot
```{r}
scCustomize::Clustered_DotPlot(so_proc, features = markers, colors_use_idents = custcols,
legend_label_size = 5, legend_title_size = 6, column_label_size = 5, x_lab_rotate = F, row_label_size = 5, cluster_feature = F, cluster_ident = F)
```
We can flip the plot
```{r}
scCustomize::Clustered_DotPlot(so_proc, features = markers, colors_use_idents = custcols,
legend_label_size = 5, legend_title_size = 6, column_label_size = 5, row_label_size = 5, cluster_feature = F, cluster_ident = F, flip = T, seed = 123)
```
We can do a heatmap with the top markers (`top_markers`)
```{r}
top_markers_heatmap <- Extract_Top_Markers(marker_dataframe = all_markers, num_genes = 3, named_vector = FALSE,
make_unique = TRUE)
DoHeatmap(subset(so_proc, downsample = 100), features = top_markers_heatmap, size = 2.5, angle = 0, hjust = 0.5, group.colors = custcols) +
theme(axis.text.y = element_text(size = 6, colour = "black")) +
scale_fill_viridis_c(option = "magma") +
theme(legend.title = element_text(size = 7),
legend.title.align = 0.5,
legend.text = element_text(size = 6),
legend.key.size = unit(0.2, 'cm'),
legend.key.height = unit(0.2, 'cm'),
legend.key.width = unit(0.2, 'cm'))
```
We can represent the cell markers in UMAPs
```{r}
FeaturePlot_scCustom(seurat_object = so_proc, features = c("Ms4a1","Cd3g","Itgae","Cdh22","Cdh13","Mfap5","Mrc1","Cxcr2","Atp6v0d2","Rgs5","Krt8"))
```
UMAPs with the top markers:
```{r}
FeaturePlot_scCustom(seurat_object = so_proc, features = c("Pax5","Trbc2","Htr7","Klk1","Cldn5","Bnc2","C1qa","G0s2","Cyp2s1","Higd1b","Gm49890"))
```
We can do UMAPs also representing cell types in individual mice
```{r}
so_proc$mouse <- factor(so_proc$mouse, levels = c("SITTA11","SITTC11","SITTD9","SITTE9","SITTF9","SITTA9","SITTB9","SITTB11","SITTC9","SITTH10"))
```
```{r}
so_proc_umap <- DimPlot_scCustom(seurat_object = so_proc, colors_use = cust_cols, split.by = "mouse", label = T, repel = T, split_seurat = T, num_columns = 5, label.size = 3, aspect_ratio = 1) +
theme(legend.key.size = unit(0.2, 'cm'),
legend.key.height = unit(0.3, 'cm'),
legend.key.width = unit(0.5, 'cm'),
legend.title = element_text(size=6),
legend.text = element_text(size=8))+
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text.x = element_text(size = 5))+
theme(axis.text.y = element_text(size = 5))+
theme(axis.title.y = element_text(size = 6))+
theme(axis.title.x = element_text(size = 6))+
theme(plot.title = element_text(hjust = 0.5, size = 5, face = "bold"))
so_proc_umap
```
## Frequency of cell types
```{r}
so_proc_df <- as.data.frame([email protected])
```
```{r}
so_proc_df_2 <- so_proc_df %>%
dplyr::group_by(strain, mouse, cell_type) %>%
dplyr::summarise(total_abund = n())
so_proc_df_2 <- so_proc_df_2 %>%
dplyr::group_by(strain, mouse) %>%
dplyr::mutate(prop_abund = total_abund / sum(total_abund))
```
Frequency of cell types
```{r}
so_proc_df_2$cell_type <- factor(so_proc_df_2$cell_type, levels = c("B.cell","T.cell","DC","pDC","Endothelial",
"Fibroblast","Macrophage","Neutrophil","Osteoclast",
"pericyte","Cancer.cell"))
so_proc_df_2_plot <- so_proc_df_2 %>%
ggplot(aes(x = mouse, y = prop_abund, fill = cell_type))+
geom_bar(stat='identity', width = 0.85)+
scale_fill_manual(values = cust_cols)+
theme_classic()+
ggtitle("Cell type frequency")+
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text.x = element_text(size = 5))+
theme(legend.position="bottom")+
theme(axis.ticks.x = element_blank())+
theme(axis.text.y = element_text(size = 5))+
theme(axis.title.y = element_text(size = 6))+
theme(axis.title.x = element_blank())+
theme(plot.title = element_text(hjust = 0.5, size = 8, face = "bold"))+
theme(legend.key.size = unit(0.2, 'cm'),
legend.key.height = unit(0.3, 'cm'),
legend.key.width = unit(0.5, 'cm'),
legend.title = element_text(size=6),
legend.text = element_text(size=6))+
coord_cartesian(xlim = c(1, NA), ylim = c(0, 1))+
xlab("Mouse")+
ylab("Cell type frequency")+
guides(fill = guide_legend(title = "Cell type"))+
facet_wrap(vars(strain), ncol = 15, scales = "free")+
theme(strip.background = element_blank())+
theme(strip.text.x = element_text(size = 6, color = "black"))
so_proc_df_2_plot
```
## Counts of cell types
```{r}
so_proc_df_2_plot_counts <- so_proc_df_2 %>%
ggplot(aes(x = factor(mouse), y = total_abund, fill = cell_type))+
geom_bar(stat='identity', width = 0.85)+
scale_fill_manual(values = cust_cols)+
theme_classic()+
ggtitle("Cell type counts")+
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text.x = element_text(size = 5))+
theme(legend.position="bottom")+
theme(axis.ticks.x = element_blank())+
theme(axis.text.y = element_text(size = 5))+
theme(axis.title.y = element_text(size = 6))+
theme(axis.title.x = element_blank())+
theme(plot.title = element_text(hjust = 0.5, size = 8, face = "bold"))+
theme(legend.key.size = unit(0.2, 'cm'),
legend.key.height = unit(0.3, 'cm'),
legend.key.width = unit(0.5, 'cm'),
legend.title = element_text(size=6),
legend.text = element_text(size=6))+
coord_cartesian(expand = F, ylim = c(0, 8000), xlim = c(0.4,5.5))+
scale_y_continuous(breaks = seq(0, 8000, by = 1000))+
xlab("Mouse")+
ylab("Counts")+
guides(fill = guide_legend(title = "Cell type"))+
facet_wrap(vars(strain), ncol = 15, scales = "free")+
theme(strip.background = element_blank())+
theme(strip.text.x = element_text(size = 6, color = "black"))
so_proc_df_2_plot_counts
```
```{r}
so_proc_df_2_plot_counts2 <- so_proc_df_2 %>%
ggplot(aes(x = factor(mouse), y = total_abund, fill = cell_type))+
geom_bar(stat='identity', width = 0.85)+
scale_fill_manual(values = cust_cols)+
theme_classic()+
# ggtitle("Cell type counts")+
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text.x = element_blank())+
theme(legend.position="bottom")+
theme(axis.ticks.x = element_blank())+
theme(axis.text.y = element_text(size = 5))+
theme(axis.title.y = element_text(size = 6))+
theme(axis.title.x = element_blank())+
theme(plot.title = element_text(hjust = 0.5, size = 8, face = "bold"))+
theme(legend.key.size = unit(0.2, 'cm'),
legend.key.height = unit(0.3, 'cm'),
legend.key.width = unit(0.5, 'cm'),
legend.title = element_text(size=6),
legend.text = element_text(size=6))+
coord_cartesian(expand = F, ylim = c(0, 8000), xlim = c(0.4,5.5))+
scale_y_continuous(breaks = seq(0, 8000, by = 1000))+
xlab("Mouse")+
ylab("Counts")+
guides(fill = guide_legend(title = "Cell type"))+
facet_wrap(vars(strain), ncol = 15, scales = "free")+
theme(strip.background = element_blank())+
theme(strip.text.x = element_text(size = 8, face = "bold", color = "black"))
so_proc_df_2_plot2 <- so_proc_df_2 %>%
ggplot(aes(x = mouse, y = prop_abund, fill = cell_type))+
geom_bar(stat='identity', width = 0.85)+
scale_fill_manual(values = cust_cols)+
theme_classic()+
# ggtitle("Cell type frequency")+
theme(plot.title = element_blank())+
theme(axis.text.x = element_text(size = 5))+
theme(legend.position="bottom")+
theme(axis.ticks.x = element_blank())+
theme(axis.text.y = element_text(size = 5))+
theme(axis.title.y = element_text(size = 6))+
theme(axis.title.x = element_blank())+
theme(plot.title = element_blank())+
theme(legend.key.size = unit(0.2, 'cm'),
legend.key.height = unit(0.3, 'cm'),
legend.key.width = unit(0.5, 'cm'),
legend.title = element_text(size=6),
legend.text = element_text(size=6))+
coord_cartesian(xlim = c(1, NA), ylim = c(0, 1))+
xlab("Mouse")+
ylab("Cell type frequency")+
guides(fill = guide_legend(title = "Cell type"))+
facet_wrap(vars(strain), ncol = 15, scales = "free")+
theme(strip.background = element_blank())+
theme(strip.text.x = element_blank())
ggarrange(so_proc_df_2_plot_counts2, so_proc_df_2_plot2, ncol = 1, nrow = 2, heights = c(0.5,0.5), align = "v", legend = "bottom", common.legend = T)
```
```{r}
DimPlot_scCustom(seurat_object = so_proc, colors_use = cust_cols, split.by = "cnv_cluster", label = T, repel = T, split_seurat = T, label.size = 3, num_columns = 6)
```
# Cancer cells
```{r}
adata_cancer <- anndata::read_h5ad("adata_cancer_post_infercnv.h5ad")
```
Explore it. We get the same output that we see in Python.
```{r}
adata_cancer
```
Create Seurat object from `anndata` object metadata (in `obs`)
```{r}
metadata_cancer <- adata_cancer$obs %>% as.data.frame()
seurat_obj_cancer <- CreateSeuratObject(counts = adata_cancer$X,
meta.data = metadata_cancer,
assay = "RNA",
min.cells = 3, min.features = 200)
```
Cell names are stored in `seurat_obj_cancer@assays$RNA@[email protected] %>% rownames`
```{r}
seurat_obj_cancer@assays$RNA@[email protected] %>% rownames %>% head
```
Our counts matrix is sparse and has 14217 genes (cols) x 19630 cells (rows)
```{r}
seurat_obj_cancer@assays$RNA@layers$counts %>% dim %>% table
```
Maybe I can turn this matrix into a dense one and then add the cell names as rownames:
```{r}
counts_seurat_dense_cancer <- as.matrix(seurat_obj_cancer@assays$RNA@layers$counts)
```
```{r}
counts_seurat_dense_cancer %>% dim %>% table
```
```{r}
rownames(counts_seurat_dense_cancer) <- rownames(seurat_obj_cancer@assays$RNA@[email protected])
counts_seurat_dense_cancer %>% rownames %>% head
```
We can add now the gene names as colnames to our matrix. They are stored in:
```{r}
seurat_obj_cancer@assays$RNA@[email protected] %>% rownames %>% head(20)
```
```{r}
colnames(counts_seurat_dense_cancer) <- rownames(seurat_obj_cancer@assays$RNA@[email protected])
counts_seurat_dense_cancer %>% colnames %>% head
```
```{r}
counts_seurat_dense_cancer[1:5,1:5]
```
Now we will add all the information contained in `adata.obs` (cell_name, mouse, batch, strain, cell_type, cnv_leiden and cnv_score):
```{r}
adata_cancer$obs %>% head
```
```{r}
alldata_cancer <- counts_seurat_dense_cancer %>% as.data.frame
obs_cancer <- adata_cancer$obs
d_cancer <- cbind(obs_cancer, alldata_cancer)
d_cancer %>% head
```
```{r}
d_cancer$cnv_leiden %>% table
```
Subset cancer cells from Seurat object
```{r}
so_orig <- readRDS("/updatedProjected.rds")
so_cancer <- subset(so_orig, idents = "Cancer.cell")
[email protected] %>% colnames
```
```{r}
[email protected] %>% dim
```
```{r}
metadata_cancer %>% dim
```
Add metadata
```{r}
mutate(cell_name = metadata_cancer$cell_name) %>%
mutate(strain = metadata_cancer$strain) %>%
mutate(mouse = metadata_cancer$mouse) %>%
mutate(cnv_cluster = metadata_cancer$cnv_leiden)
[email protected] %>% dim
```
Process data
```{r}
so_cancer_proc <- so_cancer %>%
NormalizeData(normalization.method = "LogNormalize", scale.factor = 10000) %>%
FindVariableFeatures( selection.method = "vst", nfeatures = 2000) %>%
ScaleData() %>%
RunPCA() %>%
RunHarmony(group.by.vars = "strain", dims.use = 1:30) %>%
RunUMAP(reduction = "harmony", dims = 1:30) %>%
FindNeighbors(reduction = "harmony", dims = 1:30) %>%
FindClusters(resolution = 0.6)
```
Plot top markers in cnv clusters of cancer cells
```{r}
Idents(so_cancer_proc)<- [email protected]$cnv_cluster
Idents(so_cancer_proc) <- factor(Idents(so_cancer_proc),
levels = c("0", "1", "2", "3", "4", "5", "6",
"7", "8", "9", "10", "11", "12", "13"))
all_markers_cancer <- FindAllMarkers(object = so_cancer_proc) %>%
Add_Pct_Diff() %>%
filter(pct_diff > 0.6)
top_markers_cancer <- Extract_Top_Markers(marker_dataframe = all_markers_cancer, num_genes = 50, named_vector = FALSE, make_unique = TRUE)
Clustered_DotPlot(seurat_object = so_cancer_proc, features = top_markers_cancer, colors_use_idents = custcols, legend_label_size = 5, legend_title_size = 6, column_label_size = 5, row_label_size = 5, cluster_feature = F, cluster_ident = F, flip = T, seed = 123)
```
```{r}
DimPlot(so_cancer_proc, reduction = "umap", group.by = c("strain", "mouse", "cnv_cluster"), ncol = 2, cols = cust_cols)
```
```{r}
so_cancer_proc$states <- factor(so_cancer_proc$states, levels = c("Cancer.cell.1", "Cancer.cell.2", "Cancer.cell3", "Cancer.cell.4", "Csf3.cancer", "Epcam.cancer", "Interferon.cancer", "Macro-like.Cancer", "Macro-like.cancer", "Ndrg1.cancer", "Pecam1.cancer", "proliferative.cancer", "Translation.cancer"))
DimPlot(so_cancer_proc, reduction = "umap", group.by = c("strain", "mouse", "cnv_cluster","states"), ncol = 2, cols = cust_cols)
```
Cluster by CNV clusters across individual mice:
```{r}
so_cancer_proc$mouse <- factor(so_cancer_proc$mouse, levels = c("SITTA11","SITTC11","SITTD9","SITTE9","SITTF9","SITTA9","SITTB9","SITTB11","SITTC9","SITTH10"))
DimPlot_scCustom(seurat_object = so_cancer_proc, colors_use = cust_cols, split.by = "mouse", label = T, repel = T, split_seurat = T, num_columns = 5, label.size = 3)
```
Cluster by CNV clusters between strains:
```{r}
so_cancer_proc_2 <- so_cancer_proc
Idents(so_cancer_proc_2)<- [email protected]$cnv_cluster
Idents(so_cancer_proc_2) <- factor(Idents(so_cancer_proc_2),
levels = c("0","1","2","3","4","5","6","7",
"8","9","10","11","12","13"))
DimPlot_scCustom(seurat_object = so_cancer_proc_2, colors_use = cust_cols, split.by = "strain", label = T, repel = T, split_seurat = T, num_columns = 5, label.size = 3)
```
Cluster by cell states between strains:
```{r}
so_cancer_proc_3 <- so_cancer_proc
Idents(so_cancer_proc_3)<- [email protected]$states
Idents(so_cancer_proc_3) <- factor(Idents(so_cancer_proc_3),
levels = c("Csf3.cancer","Epcam.cancer","Pecam1.cancer","Ndrg1.cancer","Interferon.cancer",
"Macro-like.cancer","Macro-like.Cancer","proliferative.cancer","Translation.cancer",
"Cancer.cell.1","Cancer.cell.2","Cancer.cell3", "Cancer.cell.4"))
DimPlot_scCustom(seurat_object = so_cancer_proc_3, colors_use = cust_cols, split.by = "strain", label = T, repel = T, split_seurat = T, num_columns = 5, label.size = 3)
```
We can split by mouse instead of strain:
```{r}
DimPlot_scCustom(seurat_object = so_cancer_proc_3, colors_use = cust_cols, split.by = "mouse", label = T, repel = T, split_seurat = T, num_columns = 5, label.size = 3)
```
## Clones per sample
```{r}
so_cancer_proc %>% head
```
```{r}
so_cancer_proc_df <- as.data.frame([email protected])
```
```{r}
so_cancer_proc_df_2 <- so_cancer_proc_df %>%
dplyr::group_by(mouse, strain, cnv_cluster) %>%
dplyr::summarise(total_abund = n())
so_cancer_proc_df_2 <- so_cancer_proc_df_2 %>%
dplyr::group_by(mouse, strain) %>%
dplyr::mutate(prop_abund = total_abund / sum(total_abund))
```
```{r}
so_cancer_proc_df_2 %>%
ggplot(aes(x = mouse, y = prop_abund, fill = cnv_cluster))+
geom_bar(stat='identity', width = 0.85)+
scale_fill_manual(values = cust_cols)+
theme_classic()+
ggtitle("Frequency of CNV Clusters in cancer cells from scRNAseq data")+
theme(plot.title = element_text(hjust = 0.5))+
theme(axis.text.x = element_text(size = 5))+
theme(legend.position="bottom")+
theme(axis.ticks.x = element_blank())+
theme(axis.text.y = element_text(size = 5))+
theme(axis.title.y = element_text(size = 6))+
theme(axis.title.x = element_blank())+
theme(plot.title = element_text(hjust = 0.5, size = 8, face = "bold"))+
theme(legend.key.size = unit(0.2, 'cm'),
legend.key.height = unit(0.3, 'cm'),
legend.key.width = unit(0.5, 'cm'),
legend.title = element_text(size=6),
legend.text = element_text(size=6))+
coord_cartesian(xlim = c(1, NA), ylim = c(0, 1))+
xlab("Mouse")+
ylab("CNV Cluster frequency")+
guides(fill = guide_legend(title = "CNV Cluster"))+
facet_wrap(vars(strain), ncol = 15, scales = "free")+
theme(strip.background = element_blank())+
theme(strip.text.x = element_text(size = 6, color = "black"))
```
## Single Cell Pathway Analysis
We will download Hallmark pathways from MolsigDB - category MH: orthology-mapped hallmark gene sets
```{r}
library(SCPA)
library(msigdbr)
pathways <- msigdbr(species = "Mus musculus", category = "H") %>%
format_pathways()
pathways %>% head
```
Now we will run the comparison
```{r}
scpa_out_ <- compare_seurat(so_cancer_proc,
group1 = "strain",
group1_population = c("balb", "nsg"),
pathways = pathways)
scpa_out_ %>% head(10)
```
```{r}
plot_rank(scpa_out_, c("HALLMARK_INTERFERON_GAMMA_RESPONSE","HALLMARK_INTERFERON_ALPHA_RESPONSE","HALLMARK_COMPLEMENT","HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION","HALLMARK_KRAS_SIGNALING_UP","HALLMARK_P53_PATHWAY", "HALLMARK_TNFA_SIGNALING_VIA_NFKB","HALLMARK_IL2_STAT5_SIGNALING","HALLMARK_INFLAMMATORY_RESPONSE","HALLMARK_MTORC1_SIGNALING","HALLMARK_IL6_JAK_STAT3_SIGNALING","HALLMARK_UNFOLDED_PROTEIN_RESPONSE","HALLMARK_TGF_BETA_SIGNALING","HALLMARK_PI3K_AKT_MTOR_SIGNALING","HALLMARK_NOTCH_SIGNALING","HALLMARK_WNT_BETA_CATENIN_SIGNALING"),
highlight_point_size = 3, highlight_point_color = "#60c5f7", label_size = 3)
```
Save csv file
```{r}
write.csv(scpa_out_, "SCPA_output.csv", row.names = FALSE, col.names = TRUE)
```
We can plot a heatmap
```{r}
plot_heatmap(scpa_out_,
highlight_pathways = c("HALLMARK_INTERFERON_GAMMA_RESPONSE","HALLMARK_INTERFERON_ALPHA_RESPONSE","HALLMARK_COMPLEMENT","HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION","HALLMARK_KRAS_SIGNALING_UP","HALLMARK_P53_PATHWAY", "HALLMARK_TNFA_SIGNALING_VIA_NFKB","HALLMARK_IL2_STAT5_SIGNALING","HALLMARK_INFLAMMATORY_RESPONSE","HALLMARK_MTORC1_SIGNALING","HALLMARK_IL6_JAK_STAT3_SIGNALING","HALLMARK_UNFOLDED_PROTEIN_RESPONSE","HALLMARK_TGF_BETA_SIGNALING","HALLMARK_PI3K_AKT_MTOR_SIGNALING","HALLMARK_NOTCH_SIGNALING","HALLMARK_WNT_BETA_CATENIN_SIGNALING"),
show_row_names = F)
```
# MERFISH genes in CNV clusters
We will study the MERFISH genes in the CNV clusters in the `so_cancer_proc` object. Ideally we should study separately Balb/C and NSG samples.
```{r}
merfish_genes <- read.csv("genes_mouse_merfish_balb.tsv", sep = "\t", header = F)
merfish_genes %>% head
```
```{r}
merfish_genes_list <- merfish_genes[,1]
merfish_genes_list %>% head
```
```{r}
merfish_genes_list %>% length()
```
Do the plots. Start by heatmap
```{r}
DoHeatmap(subset(so_cancer_proc, downsample = 100), features = merfish_genes_list, size = 2.5, angle = 0, hjust = 0.5, group.colors = custcols) +
theme(axis.text.y = element_text(size = 6, colour = "black")) +
scale_fill_viridis_c(option = "magma") +
theme(legend.title = element_text(size = 7),
legend.title.align = 0.5,
legend.text = element_text(size = 6),
legend.key.size = unit(0.2, 'cm'),
legend.key.height = unit(0.2, 'cm'),
legend.key.width = unit(0.2, 'cm'))
```
This is to order heatmap genes
```{r}
merfish_genes_toplot <- FindAllMarkers(object = so_cancer_proc, features = merfish_genes_list)
top20 <- merfish_genes_toplot %>% group_by(cluster) %>% top_n(20, avg_log2FC)
top10 <- merfish_genes_toplot %>% group_by(cluster) %>% top_n(10, avg_log2FC)
```
Save csv file of 10 top MERFISH genes per CNV cluster
```{r}
write.csv(top10, "top10_genes_merfish_cnv.csv", row.names = F)
```
```{r}
DoHeatmap(subset(so_cancer_proc, downsample = 100), features = top10$gene, size = 2.5, angle = 0, hjust = 0.5, group.colors = custcols) +
theme(axis.text.y = element_text(size = 6, colour = "black")) +
scale_fill_viridis_c(option = "magma") +
theme(legend.title = element_text(size = 7),
legend.title.align = 0.5,
legend.text = element_text(size = 6),
legend.key.size = unit(0.2, 'cm'),
legend.key.height = unit(0.2, 'cm'),
legend.key.width = unit(0.2, 'cm'))
```
# Cancer cells per strain
Load the `anndata` objects for Balb/C mice and NSG mice.
```{r}
adata_cancer_balb <- anndata::read_h5ad("adata_cancer_balb_post_infercnv.h5ad")
adata_cancer_nsg <- anndata::read_h5ad("adata_cancer_nsg_post_infercnv.h5ad")
```
Here is Balb/C
```{r}
adata_cancer_balb
```
Here is NSG
```{r}
adata_cancer_nsg
```
Create the Seurat objects
```{r}
metadata_cancer_balb <- adata_cancer_balb$obs %>% as.data.frame()
seurat_obj_cancer_balb <- CreateSeuratObject(counts = adata_cancer_balb$X,
meta.data = metadata_cancer_balb,
assay = "RNA",
min.cells = 3, min.features = 200)
metadata_cancer_nsg <- adata_cancer_nsg$obs %>% as.data.frame()
seurat_obj_cancer_nsg <- CreateSeuratObject(counts = adata_cancer_nsg$X,
meta.data = metadata_cancer_nsg,
assay = "RNA",
min.cells = 3, min.features = 200)
```
Cell names are stored in `seurat_obj_cancer_balb@assays$RNA@[email protected] %>% rownames`
```{r}
seurat_obj_cancer_balb@assays$RNA@[email protected] %>% rownames %>% head
```
Our counts matrix for Balb/C mice is sparse and has 8699 genes (cols) x 12498 cells (rows)
```{r}
seurat_obj_cancer_balb@assays$RNA@layers$counts %>% dim %>% table
```
Our counts matrix for NSG mice is sparse and has 10931 genes (cols) x 12967 cells (rows)
```{r}
seurat_obj_cancer_nsg@assays$RNA@layers$counts %>% dim %>% table
```
Now we transform our matrices to dense ones:
```{r}
counts_seurat_dense_cancer_balb <- as.matrix(seurat_obj_cancer_balb@assays$RNA@layers$counts)
counts_seurat_dense_cancer_nsg <- as.matrix(seurat_obj_cancer_nsg@assays$RNA@layers$counts)
```
```{r}
rownames(counts_seurat_dense_cancer_balb) <- rownames(seurat_obj_cancer_balb@assays$RNA@[email protected])
counts_seurat_dense_cancer_balb %>% rownames %>% head
```
```{r}
rownames(counts_seurat_dense_cancer_nsg) <- rownames(seurat_obj_cancer_nsg@assays$RNA@[email protected])
counts_seurat_dense_cancer_nsg %>% rownames %>% head
```
We can add now the gene names as colnames to our matrix. They are stored in:
```{r}
seurat_obj_cancer_balb@assays$RNA@[email protected] %>% rownames %>% head(20)
```
```{r}
seurat_obj_cancer_nsg@assays$RNA@[email protected] %>% rownames %>% head(20)
```
```{r}
colnames(counts_seurat_dense_cancer_balb) <- rownames(seurat_obj_cancer_balb@assays$RNA@[email protected])
counts_seurat_dense_cancer_balb %>% colnames %>% head
```
```{r}
colnames(counts_seurat_dense_cancer_nsg) <- rownames(seurat_obj_cancer_nsg@assays$RNA@[email protected])
counts_seurat_dense_cancer_nsg %>% colnames %>% head
```
```{r}
counts_seurat_dense_cancer_balb[1:5,1:5]
```
```{r}
counts_seurat_dense_cancer_nsg[1:5,1:5]
```
Now we will add all the information contained in `adata.obs` (cell_name, mouse, batch, strain, cell_type, cnv_leiden and cnv_score):
```{r}
adata_cancer_balb$obs %>% head
```
```{r}
adata_cancer_nsg$obs %>% head
```
```{r}
alldata_cancer_balb <- counts_seurat_dense_cancer_balb %>% as.data.frame
obs_cancer_balb <- adata_cancer_balb$obs
d_cancer_balb <- cbind(obs_cancer_balb, alldata_cancer_balb)
d_cancer_balb %>% head
```
```{r}
alldata_cancer_nsg <- counts_seurat_dense_cancer_nsg %>% as.data.frame
obs_cancer_nsg <- adata_cancer_nsg$obs
d_cancer_nsg <- cbind(obs_cancer_nsg, alldata_cancer_nsg)
d_cancer_nsg %>% head
```
## Balb/C cancer cells
Subset cancer cells & strain from `so2` object
```{r}
so_orig_balb <- readRDS("updatedProjected.rds")
so_cancer_balb <- subset(so_orig_balb, idents = "Cancer.cell")
```
```{r}
so_cancer_balb2 <- so_cancer_balb
Idents(so_cancer_balb2)<- [email protected]$exp
Idents(so_cancer_balb2) <- factor(Idents(so_cancer_balb2),
levels = c("balb","nsg"))
```
```{r}
so_cancer_balb_subset <- subset(so_cancer_balb2, idents = "balb")
[email protected] %>% colnames
```
```{r}
so_cancer_balb_subset$exp %>% unique
```
This is our seurat object for Balb/C mice, with 24516 genes as rownames and 8699 cells as columns
```{r}
so_cancer_balb_subset %>% dim
```
```{r}
so_cancer_balb_subset %>% colnames %>% head
```
```{r}
so_cancer_balb_subset %>% rownames %>% head
```
And this is the anndata object for Balb/C mice that contains 8699 columns that include some information + all genes studied. Each row is a cell:
```{r}
d_cancer_balb %>% dim
```
```{r}
d_cancer_balb %>% colnames %>% head(10)
```
```{r}
d_cancer_balb %>% rownames %>% head
```
Add the information to the `Seurat` object: