---
title: 'Xenium BC data analysis'
author: "Xiao Zhang"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Xenium BC data analysis}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

This vignette introduces the CAESAR.Suite workflow for the analysis of Xenium BC spatial transcriptomics dataset. In this vignette, the workflow of CAESAR.Suite consists of five steps

* Reference dataset and target dataset preprocessing
* Detect signature genes as cell type markers from scRNA-seq reference datasets
* Annotate Xenium BC data using CAESAR
* Enrichment analysis for Xenium BC data using CAESAR
* Downstream analysis (i.e. , signature gene analysis, visualization of cell types and coembeddings)

## Load and quality control both reference and target data
We demonstrate the application of CAESAR.Suite to Xenium data. In this vignette, the input data includes: `BC_scRNAList` — two scRNA-seq reference datasets, each with 3,000 cells; `BC_XeniumList` — two Xenium target datasets, each with 3,000 cells; and `BC_feature_imgList` — two feature matrices containing histology image information from the Xenium target datasets. For more details with respect to histology image feature extraction, see [vignette](https://xiaozhangryy.github.io/CAESAR.Suite/articles/XeniumBCEIF.html). The genes of scRNA-seq reference datasets and Xenium target datasets are aligned. The package can be loaded with the command:
```{r}
set.seed(1) # set a random seed for reproducibility.
library(CAESAR.Suite) # load the package of CAESAR.Suite method
library(Seurat)
library(ProFAST)
library(ggplot2)
library(msigdbr)
library(dplyr)
```

Those data can be downloaded and load to the current working path by the following command:
```{r}
githubURL <- "https://github.com/XiaoZhangryy/CAESAR.Suite/blob/master/vignettes_data/BC_scRNAList.rda?raw=true"
BC_scRNAList_file <- file.path(tempdir(), "BC_scRNAList.rda")
download.file(githubURL, BC_scRNAList_file, mode='wb')
load(BC_scRNAList_file)

print(BC_scRNAList)

githubURL <- "https://github.com/XiaoZhangryy/CAESAR.Suite/blob/master/vignettes_data/BC_XeniumList.rda?raw=true"
BC_XeniumList_file <- file.path(tempdir(), "BC_XeniumList.rda")
download.file(githubURL, BC_XeniumList_file, mode='wb')
load(BC_XeniumList_file)

print(BC_XeniumList)

githubURL <- "https://github.com/XiaoZhangryy/CAESAR.Suite/blob/master/vignettes_data/BC_feature_imgList.rda?raw=true"
BC_feature_imgList_file <- file.path(tempdir(), "BC_feature_imgList.rda")
download.file(githubURL, BC_feature_imgList_file, mode='wb')
load(BC_feature_imgList_file)

print(sapply(BC_feature_imgList, dim))
```

Users can perform appropriate quality control on the reference dataset and target datasets. Genes expressed in less than one cell are required to remove to avoid unknown errors. Other quality control steps can be set by the user according to the data quality. Here, since scRNA-seq reference datasets and Xenium target datasets had been aligned, we do not perform quality control.
```{r}
# BC_scRNAList <- lapply(BC_scRNAList,  function(seu) {
#     CreateSeuratObject(
#         counts = seu@assays$RNA@counts,
#         meta.data = seu@meta.data,
#         min.features = 5,
#         min.cells = 1
#     )
# })
# 
# print(BC_scRNAList)
# 
# 
# BC_XeniumList <- lapply(BC_XeniumList,  function(seu) {
#     CreateSeuratObject(
#         counts = seu@assays$RNA@counts,
#         meta.data = seu@meta.data,
#         min.features = 5,
#         min.cells = 1
#     )
# })
# 
# print(BC_XeniumList)
# 
# BC_feature_imgList <- lapply(1:2, function(i) {
#     BC_feature_imgList[[i]][colnames(BC_XeniumList[[i]]), ]
# })
```

## Preprocessing and align reference and target data
First, we normalize the data and select the variable genes. We align genes and variable genes of reference and target data.
```{r}
# align genes
common_genes <- Reduce(intersect, c(
    lapply(BC_scRNAList, rownames),
    lapply(BC_XeniumList, rownames)
))

print(length(common_genes))

# all common genes are used as variable genes, as only around 300 genes here
BC_scRNAList <- lapply(BC_scRNAList, function(seu) {
    seu <- seu[common_genes, ]
    seu <- NormalizeData(seu)
    VariableFeatures(seu) <- common_genes
    seu
})

BC_XeniumList <- lapply(BC_XeniumList, function(seu) {
    seu <- seu[common_genes, ]
    seu <- NormalizeData(seu)
    VariableFeatures(seu) <- common_genes
    seu
})

print(BC_scRNAList)
print(BC_XeniumList)
```

## Detect signature genes for each cell type using scRNA-seq reference data
We introduce how to use CAESAR to detect signature genes form scRNA-seq reference data. First, we calculate the co-embeddings.
```{r}
BC_scRNAList <- lapply(BC_scRNAList, ProFAST::NCFM, q = 50)
```

Then, we detect signature genes.
```{r}
# calculate cell-gene distance
BC_scRNAList <- lapply(BC_scRNAList, ProFAST::pdistance, reduction = "ncfm")

# identify signature genes
sg_sc_List <- lapply(BC_scRNAList, function(seu) {
    print(table(seu$CellType))

    Idents(seu) <- seu$CellType
    find.sig.genes(seu)
})

str(sg_sc_List)
```

Finally, select marker genes for each cell type from the signature gene list.
```{r}
markerList <- lapply(sg_sc_List, marker.select, overlap.max = 1)

print(markerList)
```

## Annotate the MOB ST data using CAESAR and marker genes from scRNA-seq reference data
Similarly, we first calculate co-embeddings for Xenium BC dataset. The difference is that spatial transcriptome data has spatial coordinates and image feature information, so we can obtain image-based spatial aware co-embeddings.
```{r}
BC_XeniumList <- lapply(1:2, function(i) {
    seu <- BC_XeniumList[[i]]

    # the spatial coordinates
    pos <- seu@meta.data[, c("x_centroid", "y_centroid")]
    print(head(pos))

    # the image feature
    feature_img <- BC_feature_imgList[[i]]

    seu <- CAESAR.coembedding.image(
        seu, feature_img, pos, reduction.name = "caesar", q = 50)
    seu
})
names(BC_XeniumList) <- paste0("BC", 1:2)

print(BC_XeniumList)
```

Subsequently, the CAESAR co-embeddings and marker gene lists from scRNA-seq reference datasets are used to annotate the Xenium BC data.
```{r}
# convert marker list to marker frequency matrix
marker.freq <- markerList2mat(markerList)

# perform annotation using CAESAR and save results to Seurat object
BC_XeniumList <- lapply(
    BC_XeniumList, CAESAR.annotation, marker.freq = marker.freq,
    reduction.name = "caesar", add.to.meta = TRUE
)
print(colnames(BC_XeniumList[[1]]@meta.data))
```

## Downstream analysis
In the following, we visualize the CAESAR annotation results.
```{r}
# set up colors
cols <- setNames(
    c(
        "#fdc086", "#386cb0", "#b30000", "#FBEA2E", "#731A73",
        "#FF8C00", "#F898CB", "#4DAF4A", "#a6cee3", "#737373"
    ),
    c(
        "B-cells", "CAFs", "Cancer Epithelial", "Endothelial", "Myeloid",
        "Normal Epithelial", "Plasmablasts", "PVL", "T-cells", "unassigned"
    )
)
celltypes <- c(
    "B-cells", "CAFs", "Cancer Epithelial", "Endothelial", "Myeloid",
    "Normal Epithelial", "Plasmablasts", "PVL", "T-cells", "unassigned"
)

BC_XeniumList <- lapply(BC_XeniumList, function(seu) {
    Idents(seu) <- factor(seu$CAESARunasg, levels = celltypes)

    pos <- seu@meta.data[, c("x_centroid", "y_centroid")]
    colnames(pos) <- paste0("pos", 1:2)
    seu@reductions[["pos"]] <- CreateDimReducObject(
        embeddings = as.matrix(pos),
        key = paste0("pos", "_"), assay = "RNA"
    )
    seu
})
```

First, we visualize the CAESAR annotation account for 'unassigned'.
```{r, fig.width=12, fig.height=15.75}
plots <- lapply(BC_XeniumList, function(seu) {
    DimPlot(seu, reduction = "pos", cols = cols, pt.size = 1)
})

cowplot::plot_grid(plotlist = plots, ncol = 1)
```

The confidence level of the CAESAR annotation can be visualized by
```{r, fig.width=12, fig.height=15.75}
plots <- lapply(BC_XeniumList, function(seu) {
    FeaturePlot(
        seu,
        reduction = "pos", features = "CAESARconf", pt.size = 1,
        cols = c("blue", "lightgrey"), min.cutoff = 0.0, max.cutoff = 1.0
    )
})

cowplot::plot_grid(plotlist = plots, ncol = 1)
```

Next, we detect and visualize the signature genes for each cell type.
```{r}
sg_List <- lapply(BC_XeniumList, find.sig.genes)

str(sg_List)
```

We remove unwanted variation by
```{r}
dist_names <- paste0("dist_", gsub("-|/| ", ".", setdiff(celltypes, "unassigned")))

distList <- lapply(BC_XeniumList, function(seu) {
    as.matrix(seu@meta.data[, dist_names])
})

seuInt <- CAESAR.RUV(BC_XeniumList, distList, verbose = FALSE, species = "human")

metaInt <- Reduce(rbind, lapply(BC_XeniumList, function(seu) {
    as.matrix(seu@meta.data[, "CAESARunasg", drop = FALSE])
})) %>% as.data.frame()
colnames(metaInt) <- "CAESARunasg"
row.names(metaInt) <- colnames(seuInt)
seuInt <- AddMetaData(seuInt, metaInt, col.name = colnames(metaInt))
Idents(seuInt) <- factor(seuInt$CAESARunasg, levels = names(cols))

print(seuInt)
```

Then, we can visualize the top three signature genes by a dot plot.
```{r, fig.width=12, fig.height=5}
# obtain the top three signature genes
celltypes_plot <- setdiff(celltypes, "unassigned")
top3sgs <- Intsg(sg_List, 3)[celltypes_plot]
print(top3sgs)

sg_features <- unname(unlist(top3sgs))

DotPlot(
    seuInt,
    idents = celltypes_plot, col.min = -1, col.max = 2, dot.scale = 7,
    features = sg_features, scale.min = 0, scale.max = 30
) + theme(axis.text.x = element_text(face = "italic", angle = 45, vjust = 1, hjust = 1))
```

Next, we calculate the UMAP projections of co-embeddings of cells and the selected signature genes.
```{r, fig.width=12, fig.height=15.75}
# calculate coumap
BC_XeniumList <- lapply(
    BC_XeniumList, CoUMAP, reduction = "caesar",
    reduction.name = "caesarUMAP", gene.set = sg_features
)

df_gene_label <- data.frame(
    gene = unlist(top3sgs),
    label = rep(names(top3sgs), each = 3)
)

plots <- lapply(BC_XeniumList, function(seu) {
    CoUMAP.plot(
        seu, reduction = "caesarUMAP", gene_txtdata = df_gene_label,
        cols = c("gene" = "#000000", cols)
    )
})

cowplot::plot_grid(plotlist = plots, ncol = 1)
```

## Enrichment analysis
Next, we show how to use CAESAR for enrichment analysis. Here we choose GOBP pathways as an example. Let's first get some pathways.
```{r}
pathway_list <- msigdbr(species = "Homo sapiens", category = "C5", subcategory = "GO:BP") %>%
    group_by(gs_name) %>%
    summarise(genes = list(intersect(gene_symbol, common_genes))) %>%
    tibble::deframe()
n.pathway_list <- sapply(pathway_list, length)
pathway_list <- pathway_list[n.pathway_list >= 5]

print(head(pathway_list))
```

Then, we can test whether those pathways are enriched in BC1 section.
```{r}
seuBC1 <- BC_XeniumList[[1]]

df_enrich <- CAESAR.enrich.pathway(
    seuBC1, pathway.list = pathway_list, reduction = "caesar"
)

# obtain significant enriched pathways
pathways <- pathway_list[df_enrich$asy.wei.pval.adj < 0.05]
```

Next, we can calculate the spot level enrichment scores and detect differentially enriched pathways.
```{r}
enrich.score.BC1 <- CAESAR.enrich.score(seuBC1, pathways)

dep.pvals <- CAESAR.CTDEP(seuBC1, enrich.score.BC1)
head(dep.pvals)
```

We can visualize the spatial heatmap of enrichment score.
```{r, fig.width=12, fig.height=7.5}
seuBC1 <- AddMetaData(seuBC1, as.data.frame(enrich.score.BC1))

pathway <- "GOBP_VASCULATURE_DEVELOPMENT"
FeaturePlot(seuBC1, features = pathway, reduction = "pos") +
    scale_color_gradientn(
        colors = c("#fff7f3", "#fcc5c0", "#f768a1", "#ae017e", "#49006a"),
        values = scales::rescale(seq(0, 1, 0.25)),
        limits = c(0, 1)
    ) +
    theme(
        legend.position = "right",
        legend.justification = "center",
        legend.box = "vertical"
    )
```

<details>
<summary>**Session Info**</summary>
```{r}
sessionInfo()
```
</details>
