Identifying Spatially Variable Genes using Big-Small Patch (BSP) algorithm and sparse matrix implementation as scBSP
Big-small patch (BSP) is a granularity-guided, data-driven, and parameter-free model for identifying spatial variable genes in 2D and 3D high-throughput spatial transcriptomics data.
Original implementation of BSP in python is available at https://github.com/juexinwang/BSP
scBSP - A Fast Tool for Single-Cell Spatially Variable Genes Identifications on Large-Scale Spatially Resolved Transcriptomics Data
This package utilizes a granularity-based dimension-agnostic tool, single-cell big-small patch (scBSP), implementing sparse matrix operation and KD-tree/balltree method for distance calculation, for the identification of spatially variable genes on large-scale data. A corresponding Python library is available at https://pypi.org/project/scbsp. The R source code is available at https://github.com/CastleLi/scBSP . The Python source code is available at https://github.com/YQ-Wang/scBSP
Tested on MacOS Sonoma version 14.4.1 with R version 4.3.1, and on Windows 11 12th Gen Intel(R) i7-1265U with R version 4.2.1.
This package can be installed on R CRAN
install.packages("scBSP")
For installation errors with 'sparseMatrixStats', install the bioconductor package from here before installing scBSP.
In your working directory, create a 'data' subdirectory to download the tutorial data files into. Load the scBSP package and Breast cancer data set, which can be downloaded here.
> library(scBSP)
> load("data/Layer2_BC_Count.rds")
View the Breat Cancer expression count matrix 'rawcount', where each row denotes a gene and each column represents a cell/spot.
> rawcount[1:5,1:5]
17.907x4.967 18.965x5.003 18.954x5.995 17.846x5.993 20.016x6.019
GAPDH 1 7 5 1 2
USP4 1 0 0 0 0
MAPKAPK2 1 1 0 0 1
CPEB1 0 0 0 0 0
LANCL2 0 0 0 0 0
Extract the coordinates from the raw data
> info <- cbind.data.frame(
x=as.numeric(sapply(strsplit(colnames(rawcount),split="x"),"[",1)),
y=as.numeric(sapply(strsplit(colnames(rawcount),split="x"),"[",2))
)
> rownames(info) <- colnames(rawcount)
> Coords <- info[,1:2]
View the coordinates of x and y on 2-D space
> head(Coords)
x y
17.907x4.967 17.907 4.967
18.965x5.003 18.965 5.003
18.954x5.995 18.954 5.995
17.846x5.993 17.846 5.993
20.016x6.019 20.016 6.019
20.889x6.956 20.889 6.956
Check the coordinates dimensions (Should be M x D, representing D-dimensional coordinates for M spots)
> dim(Coords)
[1] 251 2
Exclude low expressed genes from the expression matrix
> Filtered_ExpMat <- SpFilter(rawcount)
For the gene expression data, we need to explicitly convert the data format to a sparse Matrix
> Filtered_ExpMat_sparse <- Matrix::Matrix(Filtered_ExpMat, sparse = TRUE)
Check the dimensions of the filtered input data (Should be N x M, sparse matrix with N genes and M spots)
> dim(Filtered_ExpMat_sparse)
[1] 11769 251
The inputs are the coordinates and the expresson matrix, scBSP calculates the p-values
> P_values <- scBSP(Coords, Filtered_ExpMat_sparse)
> head(P_values)
GeneNames P_values
1 GAPDH 2.704573e-09
2 USP4 2.452562e-01
3 MAPKAPK2 4.177737e-03
4 CPEB1 7.656481e-01
5 LANCL2 7.272278e-01
6 MCL1 9.308317e-06
Display significant SVGs by sorting and filtering for P_values < 0.05
> results <- P_values[P_values$P_values<0.05,]
> results <- results[order(results$P_values),]
> head(results)
GeneNames P_values
326 SPINT2 0.000000e+00
261 POSTN 6.439294e-15
2013 COL1A1 6.550316e-15
1347 B2M 7.660539e-15
2370 FN1 1.521006e-14
1072 EEF1A1 1.356693e-13
> dim(results)
[1] 1765 2
We can see there is a significant difference in the SVGs found by scBSP which have a very high P_value (PCLO) & genes which were found to have very low P_values (POSTN)
| PCLO | POSTN |
|---|---|
 |
 |
HDST provides a subcellular resolution spatial trianscriptomics data, which contains 181,367 spots and 19,950 genes with a density of ~0.0003. The data can be downloaded at here
> library(scBSP)
> load("data/CN24_D1_unmodgtf_filtered_red_ut_HDST_final_clean.rds")
View the expression count matrix which is already loaded in as a sparse matrix, each row denotes a gene and each column represents a cell/spot.
> head(sp_count)
Loading required package: Matrix
6 x 181367 sparse Matrix of class "dgCMatrix"
[[ suppressing 34 column names ‘1000x100’, ‘1000x103’, ‘1000x113’ ... ]]
Rcn2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mycbp2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
mt-Rnr2 . . . . . . . . . . 1 . 1 2 . . 1 . 1 1 . . 2 2 2 6 4 . 6 2 . 1 1 4
Mprip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mroh1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Zfp560 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rcn2 ......
Mycbp2 ......
mt-Rnr2 ......
Mprip ......
Mroh1 ......
Zfp560 ......
.....suppressing 181333 columns in show(); maybe adjust options(max.print=, width=)
..............................
Extract the coordinates
> info <- cbind.data.frame(
x=as.numeric(sapply(strsplit(colnames(sp_count),split="x"),"[",1)),
y=as.numeric(sapply(strsplit(colnames(sp_count),split="x"),"[",2))
)
> rownames(info) <- colnames(sp_count)
> Coords <- as.matrix(info)
View the coordinates of x and y on 2-D space
> head(Coords)
x y
1000x100 1000 100
1000x103 1000 103
1000x113 1000 113
1000x114 1000 114
1000x116 1000 116
1000x142 1000 142
Check the coordinates dimensions (Should be M x D, representing D-dimensional coordinates for M spots)
> dim(Coords)
[1] 181367 2
Remove mitochondrial genes from the expression matrix
> mt_idx <- grep("mt-",rownames(sp_count))
> if(length(mt_idx)!=0){
sp_count <- sp_count[-mt_idx,]
}
Excluding low expressed genes from the expression matrix
> Filtered_ExpMat <- SpFilter(sp_count)
Check the dimensions of the filtered input data (Should be N x M, sparse matrix with N genes and M spots)
> dim(Filtered_ExpMat)
[1] 13209 181367
Calculate Spatially Variable Genes with the filtered sparse matrix and coordinates using scBSP
> P_values <- scBSP(Coords, Filtered_ExpMat)
Install peakRAM package to record computational resources
> install.packages("peakRAM")
Alternate installation method
> install.packages("remotes")
> remotes::install_github("tpq/peakRAM")
Record the computational resources used when running scBSP (It takes seconds on a laptop)
> library(peakRAM)
> peakRAM(P_values <- scBSP(Coords, Filtered_ExpMat))
Normalizing the expression matrix
Normalizing the coords matrix
Calculating p-values
Function_Call
1 P_values<-scBSP(Coords,Filtered_ExpMat)
Elapsed_Time_sec Total_RAM_Used_MiB Peak_RAM_Used_MiB
1 4.64 0.1 771.4
Calcuate the density of the data
> Matrix::nnzero(sp_count)
[1] 1217700
> dim(sp_count)
[1] 19913 181367
> dimNum<-dim(sp_count)
> dimNum[1]*dimNum[2]
[1] NA
Warning message:
In dimNum[1] * dimNum[2] : NAs produced by integer overflow
> Matrix::nnzero(sp_count)/dimNum[1]/dimNum[2]
[1] 0.0003371672
The density of the data is ~0.0003, and by using this sparse matrix format, can accelerate the computational processing by a lot.
Display significant SVGs by sorting and filtering for P_values < 0.05
> results <- P_values[P_values$P_values<0.05,]
> results <- results[order(results$P_values),]
> head(results)
GeneNames P_values
24 Gm42418 0.000000e+00
70 Cmss1 0.000000e+00
74 Camk1d 3.330669e-16
49 Gphn 7.771561e-16
64 CT010467.1 1.975087e-13
43 Cdk8 1.445843e-12
> dim(results)
[1] 1829 2
Use SlideSeq V2 data with higher density.
> install.packages("remotes")
> remotes::install_github("satijalab/seurat-data")
> library(Seurat)
> library(SeuratData)
> library(SeuratObject)
> library(peakRAM)
Check the available data
> AvailableData()
Install Slide-seq v2 dataset of mouse hippocampus
> InstallData("ssHippo")
Once installed, load the data into your enviorment and extract the filtered expression matrix
> slide.seq <- LoadData("ssHippo")
> data_extracted <- scBSP::LoadSpatial(slide.seq)
> ExpMatrix_Filtered <- scBSP::SpFilter(data_extracted$ExpMatrix, Threshold = 1)
Check the dimensions of the SlideSeq coordinates and filtered gene expression matrix
> dim(data_extracted$Coords)
[1] 53173 2
> dim(ExpMatrix_Filtered)
[1] 23243 53173
Record the computational resources used when running scBSP
> peakRAM({ P_values <- scBSP::scBSP(data_extracted$Coords,ExpMatrix_Filtered)})
Normalizing the expression matrix
Normalizing the coords matrix
Calculating p-values
Function_Call
1 {P_values<-scBSP::scBSP(data_extracted$Coords,ExpMatrix_Filtered)}
Elapsed_Time_sec Total_RAM_Used_MiB Peak_RAM_Used_MiB
1 22.4 0.1 3485.7
We can see that it takes more time to run compared to the HDST data since they have different densities.
Calcuate the density of the data
> Matrix::nnzero(data_extracted$ExpMatrix)
[1] 22361774
> dim(data_extracted$ExpMatrix)
[1] 23264 53173
> dimNum<-dim(data_extracted$ExpMatrix)
> dimNum[1]*dimNum[2]
[1] 1237016672
> Matrix::nnzero(data_extracted$ExpMatrix)/(dimNum[1]*dimNum[2])
[1] 0.01807718
The density of the SlideSeq V2 data is ~0.02. The sparse matrix format cannot accelerate the computational time much as the HDST data which has a density of ~0.0003.
Since scBSP is dimension agnostic, we are able to use data of any dimension. Load the scBSP package and 3D simulated data file, which can be downloaded here.
> library(scBSP)
> simdata <- read.csv("data/Pattern_1.csv")
Extract the coordinates from the simulated data file
> coords <- simdata[c(1,2,3)]
> colnames(coords) <- c("x", "y", "z")
View the coordinates of x, y, z in the 3-D space
> head(coords)
x y z
1 12.714909 13.352257 1
2 2.823547 4.759709 1
3 12.577456 3.674638 1
4 13.225454 2.316355 1
5 9.251169 4.510353 1
6 8.807597 7.141753 1
Check the coordinates dimensions (Should be M x D, representing D-dimensional coordinates for M spots)
> dim(coords)
[1] 2229 3
Extract that simulated gene counts and convert the format to a sparse matrix
> exp_matrix <- simdata[c(-1,- 2,-3)]
> exp_matrix <- Matrix::Matrix(t(exp_matrix), sparse = TRUE)
Check the dimensions of the filtered input data (Should be N x M, sparse matrix with N genes and M spots)
> dim(exp_matrix)
[1] 10 2229
Using the 3D coordatinates and expression matrix, scBSP calculates the p-values
> P_values <- scBSP::scBSP(coords,exp_matrix)
> head(P_values)
GeneNames P_values
1 SVG_1 0.06804437
2 SVG_2 0.71548891
3 SVG_3 0.87505115
4 SVG_4 0.25470892
5 SVG_5 0.40861395
6 SVG_6 0.91673636
Additionally, to check the gene expression across the sample spots/cells, you can plot the gene expression with the spatial coordinates to visualize the distribution. Create a subdirectory in your working directory names 'plots' to save the figures. (Note: Example 2 & 3's data is very large and may take up alot of computational resources to plot all the cells/spots. Please only use the below code for Example 1)
install.packages("ggplot2")
library(ggplot2)
saveGenePlotLog <- function(counts_matrix, Coords, geneName) {
counts <- data.frame(t(data.frame(counts_matrix)))
exp <- counts[,geneName]
expo <- log(exp + 1)
df1 <- data.frame(
x = Coords$x,
y = Coords$y,
z = expo
)
p <- ggplot(df1, aes(x = x, y = y, color = z)) +
geom_point(size = 4) +
scale_color_gradient(low = "blue", high = "red") +
labs(color = geneName) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
ggsave(paste("plots/", geneName, ".png", sep = ""), plot = p, width = 8, height = 6, dpi = 300)
}
Once the function is defined, pass in the sample expression matrix, coordinates, and the name of the gene you want to create a plot for. In the example below, we use the expression matrix and coordinates from Example 1, and plot the SVG which was found to have the lowest P_value.
saveGenePlotLog(Filtered_ExpMat, Coords, "SPINT2")
| SPINT2 |
|---|
 |
In this section, we will be analyzing 4 kidney CKD 10x Xenium samples. These files can be found here. Unzip the file in the same data folder as the previous scBSP data files.
The first step in the analysis is to find what are the SVGs in each sample. The following code runs scBSP in a loop over the four Xenium samples.
library(scBSP)
library(utils)
# Function to run scBSP
get_BSP <- function(folder= NULL, sample = NULL){
counts <- read.csv(paste0(folder, sample, "/", "count.csv"))
spa <- read.csv(paste0(folder, sample, "/", "spa.csv"))
# Recording sample metadata to output in a file
og_genes <- ncol(counts)-1
cells <- nrow(counts)
cat(paste0("\nCurrent Sample: ", sample))
cat(paste0("\nNumber of Genes (N): ", ncol(counts)-1))
cat(paste0("\nNumber of spots (M): ", nrow(counts)))
cat(paste0("\nDimensions (D): 2 (x,y)"))
# Removing 'ID' column from counts & transposing to get a N x M matrix for R's scBSP (N genes and M spots) and making matrix sparse
counts <- counts[,-1]
counts <- t(counts)
count <- Matrix::Matrix(as.matrix(counts), sparse = TRUE)
cat(paste0("\n\n------Running BSP--------\n"))
#start <- proc.time()
output_P <- scBSP(spa, count)
#stop <- proc.time()
#time_dif <- stop-start
cat(paste0("------Finish BSP--------\n"))
# Write out BSP p-value output
write.table(output_P,paste0(folder, sample, "/", sample,'_P_values_R.csv'), sep = ",", row.names = FALSE, quote = FALSE)
cat("\nP_values outputted in sample folder!")
}
setwd("________") # set your current working directory here
samples = c("5582",
"40440",
"40610",
"40775")
dir_folder <- "data/XeniumDKD/XeniumDKD/"
for (samp in samples){
cat("\n###############################################")
get_BSP(folder = dir_folder, sample = samp)
cat("\n")
cat("\n")
}
Rather than evaluating each CKD sample independently, we use the scBSP results to find one meta-p value for each gene. In this case, we are using 'sumlog' from the metap package for meta-p calculations.
# Install package for meta analysis
install.packages("metap")
library(metap)
library(tidyverse)
# Load in the scBSP results for all the samples in CKD
# The following section makes a dataframe using all samples in a condition
# Read in one scBSP output file to build the whole dataframe off of
svg_ckd <- read.csv(paste0(dir_folder, samples[1], "/", samples[1] ,"_P_values_R.csv"), col.names = c("GeneNames", samples[1]))
# Read in the remaining samples for that condition in a loop, appending to main dataframe
for(i in 2:length(samples)){
temp <- read.csv(paste0(dir_folder, samples[i], "/", samples[i] ,"_P_values_R.csv"), col.names = c("GeneNames", samples[i]))
#print(head(temp))
svg_ckd <- merge(svg_ckd,temp,by="GeneNames",all.x = TRUE, all.y=TRUE)
}
# Making GeneNames rownames
rownames_ckd <- svg_ckd$GeneNames
rownames(svg_ckd) <- rownames_ckd
svg_ckd$GeneNames = NULL
# Making a temporary colums 'meta-p' to fill
svg_ckd$meta_p <- 0
# Iterating through each row (gene), run sumlog to get the meta-p value for the gene
for (x in 1:length(rownames(svg_ckd))) {
temp <- sumlog(svg_ckd[x,1:length(samples)])
svg_ckd[x,(length(samples)+1)] <- temp$p
}
# How many statistically significant SVGs?
length(svg_ckd$meta_p[svg_ckd$meta_p < 0.05])
# Save meta-p values
write.table(svg_ckd,paste0(dir_folder, "CKD_P_values_meta.csv"), sep = ",", row.names = TRUE, quote = FALSE)
}
setwd("C:/Users/maumi/OneDrive/Documents/Fall2024/Juexin/Dec_Tutorial/")
samples = c("5582",
"40440",
"40610",
"40775")
dir_folder <- "data/XeniumDKD/XeniumDKD/"
for (samp in samples){
cat("\n###############################################")
get_BSP(folder = dir_folder, sample = samp)
cat("\n")
cat("\n")
}
NOTE: This section is run in python (jupyter notebook). For any subsequent python sections in this tutorial, please make sure you have the following packages installed in your python enviorment:
- numpy
- pandas
- matplotlib
- sklearn (scikit-learn)
- scipy
- h5py (required for scanpy)
- scanpy
For simplicity of running the python codes, please download the "Applications4_Clustering&PathwayScore.ipynb".
After retrieving the meta-p scores for the CKD samples, we use the statistically significant SVGs and cluster common gene groups in each sample to observe the pattern.
| Hierarchical Clustering example for 40440 |
|---|
 |
Two SVGs found in different clusters
| VIM | POSTN |
|---|---|
 |
 |
Using the significant SVGs for CKD, we run a GO enrichment analysis to find spatially variable ontologies. Leukocyte Migration is used for further analysis on the CKD samples.
library(clusterProfiler)
library(org.Hs.eg.db)
library(AnnotationDbi)
library(Seurat)
library(tibble)
ckd <- read.table(paste0(dir_folder, "CKD_allsig.txt"))
### Gene Ontology (GO) Enrichment analysis ###
# NOTE: you can write out the enrichment results table the same for Kegg and Reactome pathway analysis.
# Setting data to the genes you want to analyze
genes_to_run <- ckd$V1
# Run GO enrichment
GO_results <- enrichGO(gene = genes_to_run, OrgDb = "org.Hs.eg.db", keyType = "SYMBOL", ont = "BP")
View(GO_results@result)
# Save results
write.table(GO_results@result,paste0(dir_folder, "GO_CKD_allSVGs.csv"), sep = ",", row.names = TRUE, quote = FALSE)
# Print out isolated pathways if needed
pathway <-GO_results@geneSets$`GO:0050900` # GO:0050900 = Leukocyte mitigation
# subsetting only genes that are found in condition SVGs
pathway_ckdonly <- pathway[(pathway %in% genes_to_run)]
file_output = paste0(dir_folder, 'GO_0050900.txt')
write(paste(pathway_ckdonly, collapse = " "),
file=file_output)
NOTE: This section is run in python (jupyter notebook), found in "Applications4_Clustering&PathwayScore.ipynb".
After exporting the pathway genes from the gene ontology analysis, we use these genes to calculate the pathway expression using Scanpy's score_genes.
Finally, once we have the pathway expression, we can view its distribution over the sample. To view the expression over histology, a different tool such as FUSION should be used, but for this tutorial, we will show how to generate the expression plot for the sample 40440.
############################################################################
# Plotting Pathway Expression on Sample 40440 #
############################################################################
# The following code sets up the Xenium object using 40440 raw data.
sample="40440"
data <- ReadXenium(data.dir = paste0(dir_folder, sample, "/raw"),
type = c("centroids", "segmentations"))
data %>% names()
data %>% str()
assay <- "Xenium"
segmentations.data <- list(
"centroids" = CreateCentroids(data$centroids),
"segmentation" = CreateSegmentation(data$segmentations)
)
coords <- CreateFOV(
coords = segmentations.data,
type = c("segmentation", "centroids"),
molecules = data$microns,
assay = assay
)
xenium.obj2 <- CreateSeuratObject(counts = data$matrix[["Gene Expression"]], assay = assay)
xenium.obj2[["BlankCodeword"]] <- CreateAssayObject(counts = data$matrix[["Unassigned Codeword"]])
xenium.obj2[["ControlCodeword"]] <- CreateAssayObject(counts = data$matrix[["Negative Control Codeword"]])
xenium.obj2[["ControlProbe"]] <- CreateAssayObject(counts = data$matrix[["Negative Control Probe"]])
fov <- "test"
xenium.obj2[[fov]] <- coords
xenium.obj2
# An object of class Seurat
# 541 features across 92438 samples within 4 assays
# Active assay: Xenium (300 features, 0 variable features)
# 3 other assays present: BlankCodeword, ControlCodeword, ControlProbe
# 1 spatial field of view present: test
Cells(coords) %>% str
Cells(xenium.obj2) %>% str
# Read in the pathway expression for the sample
pathway_expression <- read.csv(paste0(dir_folder, sample, "/GO_0050900_pathwaycount.csv"),
row.names = 1)
# Add in pathway expression as metadata
xenium.obj2 <- AddMetaData(
object = xenium.obj2,
metadata = pathway_expression,
col.name = 'Leukocyte_migration'
)
# View the full feature plot for pathway expression
ImageFeaturePlot(xenium.obj2, features = c("Leukocyte_migration"),
min.cutoff = c(0),
size = 2, cols = c("white", "red"), axes = TRUE)
# Zoom in on cropped area to visualize areas of high pathway expression
cropped.coords <- Crop(xenium.obj2[["test"]], y = c(4500, 5500), x = c(550, 800), coords = "tissue")
xenium.obj2[["zoom"]] <- cropped.coords
DefaultBoundary(xenium.obj2[["zoom"]]) <- "segmentation"
ImageFeaturePlot(xenium.obj2, fov = "zoom", features = c("Leukocyte_migration"),
min.cutoff = c(0),
size = 0.75, cols = c("white", "red"), axes = TRUE)
| Full Sample Leukocyte Migration |
|---|
 |
| Area of High Expression Leukocyte Migration |
|---|
 |
# Cite
1. Wang, J., Li, J., Kramer, S.T. et al. Dimension-agnostic and granularity-based spatially variable gene identification using BSP. Nat Commun 14, 7367 (2023). https://doi.org/10.1038/s41467-023-43256-5
2. Li, J., Wang, Y., Raina, M.A. et al. scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data. bioRxiv 2024.05.06.592851; doi: https://doi.org/10.1101/2024.05.06.592851
# References:
1. https://github.com/juexinwang/BSP/
2. https://github.com/CastleLi/scBSP/
3. Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78-82, 2016
4. https://github.com/mssanjavickovic/3dst
5. https://xzhoulab.github.io/SPARK/02_SPARK_Example/