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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# scBSC (single cell Bivariate Spatial Correlation)
<!-- badges: start -->
<!-- badges: end -->
scBSC allows users calculate bivariate spatial correlation statistics as applied from [Lee S., J Geography Syst (2001)](https://link.springer.com/article/10.1007/s101090100064) to this wonderful spatial single cell RNA-Seq domain.
## Installation
You can install the released version of scBSC from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("scBSC")
```
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("soymintc/scBSC")
```
## Example
The following calculates spatial correlation between expression of two genes, Penk and Cck, from a Seurat-format mouse brain spatial scRNA-seq data from Visium 10x.
#### Using pre-made SeuratData
```R
# Import libraries
library(Seurat)
library(SeuratData)
library(scBSC)
# Import Seurat-format mouse brain data
InstallData("stxBrain")
brain <- LoadData("stxBrain", type = "anterior1")
brain <- SCTransform(brain, assay = "Spatial", verbose = FALSE)
# Calculate bivariate spatial correlation statistics
conn_mat <- make_conn_mat(brain) # creating connectivity matrix; only required once per dataset
bsc <- calc_bsc("Penk", "Cck", brain, conn_mat) # calculate statistics for any pair of genes in the data
print_ <- sprintf("%.3f %.3f %.3f %.3f %.3f",
bsc$L_XX, # How well is Penk expression spatially clustered
bsc$L_YY, # How well is Cck expression spatially clustered
bsc$r_sm, # Correlation between spatially smoothened expression of two genes
bsc$r, # Correlation between expression of two genes
bsc$L_XY) # Bivariate spatial correlation (clustering effect & expression correlation)
print(print_) # result: 0.869 0.865 -0.809 -0.729 -0.702
```
#### Using Visium 10x data, imported by Seurat Load10X_Spatial
Of course scBSC also supports your own spatial scRNA-seq, as long as the data is in Seurat spatial object format.
Link to raw data: [Visium 10x Mouse Brain Serial Section](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Mouse_Brain_Sagittal_Anterior)
```R
# Import libraries
library(Seurat)
library(scBSC)
# Import Visium 10x data using Seurat
setwd('/path/to/mouse_brain_data')
# Use Load10X_Spatial to load data from the following file structure.
# Data used was Visium 10x Mouse Brain Serial Section 1 (Anterior-Sagittal)
# anterior1 (directory under /path/to/mouse_brain_data; any name would suffice)
# ├── filtered_feature_bc_matrix
# │ ├── barcodes.tsv.gz # cell barcodes
# │ ├── features.tsv.gz # gene info
# │ └── matrix.mtx.gz # gene count per cell
# ├── filtered_feature_bc_matrix.h5 # [req] sparse matrix including the info above
# └── spatial
# ├── aligned_fiducials.jpg
# ├── detected_tissue_image.jpg
# ├── scalefactors_json.json # [req] high -> low-res image scaling info
# ├── tissue_hires_image.png
# ├── tissue_lowres_image.png # [req]
# └── tissue_positions_list.csv # [req] the coordinates for each spot
brain <- Load10X_Spatial(data.dir="./anterior1",
filename="filtered_feature_bc_matrix.h5",
assay='Spatial',
slice="anterior1",
filter.matrix = TRUE)
brain@meta.data$orig.ident <- "anterior1" # give cells identity; default: SeuratProject
Idents(object = brain) <- "anterior1" # label experiment
Project(object = brain) <- "brain" # label project
# Remove some unwanted genes for your downstream experiments
brain <- PercentageFeatureSet(brain, "^mt-", col.name = "percent_mito")
brain <- PercentageFeatureSet(brain, "^Hb.*-", col.name = "percent_hb")
brain = brain[, brain$nFeature_Spatial > 500 &
brain$percent_mito < 25 &
brain$percent_hb < 20]
brain <- brain[!grepl("Bc1", rownames(brain)), ] # filter Bc1
brain <- brain[!grepl("^mt-", rownames(brain)), ] # filter Mitocondrial
brain <- brain[!grepl("^Hb.*-", rownames(brain)), ] # filter Hemoglobin gene (optional if Hb genes needed)
# Normalize expression values by SCTransform
# Required for later calculations based on assay="SCT"; uses brain@assays[["SCT"]]@data as default
# Not required if the assay property in calc_bsc is to be changed to "Spatial" (then only requires brain@assays[["Spatial"]])
brain <- SCTransform(brain, assay = "Spatial", verbose = TRUE, method = "poisson")
# Calculate bivariate spatial correlation statistics
conn_mat <- make_conn_mat(brain) # creating connectivity matrix; only required once per dataset
bsc <- calc_bsc("Penk", "Cck", brain, conn_mat) # calculate statistics for any pair of genes in the data
print_ <- sprintf("%.3f %.3f %.3f %.3f %.3f",
bsc$L_XX, # How well is Penk expression spatially clustered
bsc$L_YY, # How well is Cck expression spatially clustered
bsc$r_sm, # Correlation between spatially smoothened expression of two genes
bsc$r, # Correlation between expression of two genes
bsc$L_XY) # Bivariate spatial correlation (clustering effect & expression correlation)
print(print_) # result: 0.869 0.865 -0.809 -0.729 -0.702
```
Plotting the relative gene expression between the two genes will show the following result.
Below includes a highly negative, highly positive, and close-to-zero Lxy cases.
```r
# Highly positive spatial correlation example with Lxx 0.869, Lyy 0.864, rsm 0.936, r 0.855, Lxy 0.813
SpatialFeaturePlot(brain, features = c("Gpr88", "Ppp1r1b"), ncol = 2, alpha = c(0.1, 1), max.cutoff = 5)
```
```r
# Highly negative spatial correlation with Lxx 0.869, Lyy 0.865, rsm -0.809, r -0.729, Lxy -0.702
SpatialFeaturePlot(brain, features = c("Penk", "Cck"), ncol = 2, alpha = c(0.1, 1), max.cutoff = 5)
```
```r
# Close-to-zero spatial correlation with Lxx 0.461, Lyy 0.569, rsm 0.075, r 0.059, Lxy 0.038
SpatialFeaturePlot(brain, features = c("Rpl34", "Ptn"), ncol = 2, alpha = c(0.1, 1), max.cutoff = 5)
```
