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Knit directory: KODAMA-Analysis/
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MERFISH Data Analysis
Introduction
Spatial transcriptomics is a powerful technique for studying the spatial organization of gene expression within tissues. Here, we apply KODAMA to analyze the spatial transcriptomic data that measured the mouse preoptic region of the hypothalamus using the MERFISH technology from Moffitt et al., 2018. Link to study. We focus on the tissue sections Bregma -0.04, -0.09, -0.14, -0.19, and -0.24 mm from a consecutive brain hypothalamic region of animal 1. The original data can be downloaded from Dryad.
Loading and Preprocessing Data
# Load the necessary libraries
library(rgl)
library(irlba)
library(KODAMAextra)
library(scater)
library(SPARK)
library(ggplot2)
library(plotly)
library(mclust)# Load the MERFISH data
load("../MERFISH_Animal1.RData")
# Define the slides to be analyzed
slides <- c("-0.04", "-0.09", "-0.14", "-0.19", "-0.24")
# Initialize variables
xyz <- NULL
tissue_segments <- NULL
cell_type <- NULL
RNA <- NULL
# Extract spatial and expression data from each slide
for (i in slides) {
x <- info_mult[[i]]$x / 1000
y <- info_mult[[i]]$y / 1000
z <- as.numeric(i)
slide_xyz <- cbind(x - min(x), y - min(y), z)
xyz <- rbind(xyz, slide_xyz)
tissue_segments <- c(tissue_segments, info_mult[[i]]$z)
cell_type <- c(cell_type, info_mult[[i]]$Cell_class)
RNA <- rbind(RNA, t(cnts_mult[[i]]))
}
# Normalize RNA counts
RNA <- t(normalizeCounts(t(RNA), log = TRUE))
# Convert tissue segments to factor with defined levels
tissue_segments <- factor(tissue_segments, levels = c("V3", "BST", "fx", "MPA", "MPN", "PV", "PVH", "PVT"))
# Convert xyz to numeric matrix
xyz <- matrix(as.numeric(as.matrix(xyz)), ncol = ncol(xyz))Identifying Differentially Expressed Genes
# Initialize matrix to store p-values
pvalue_mat <- matrix(nrow = ncol(RNA), ncol = length(slides))
rownames(pvalue_mat) <- colnames(RNA)
# Calculate p-values using SPARK
for (i in 1:length(slides)) {
sel <- xyz[, 3] == slides[i]
RNA_sub <- RNA[sel,]
xyz_sup <- xyz[sel, -3]
sparkX <- sparkx(t(RNA_sub), xyz_sup, numCores = 1, option = "mixture")
pvalue_mat[, i] <- sparkX$res_mtest$combinedPval
print(slides[i])
}
# Order genes by median -log(p-value)
oo <- order(apply(pvalue_mat, 1, function(x) median(-log(x))), decreasing = TRUE)
top <- colnames(RNA)[oo]Passing message
RNA.sel.2=RNA[,top[1:100]]
number_knn=10
RNA.sel.3=RNA.sel.2
knn=knn_Armadillo(xyz,xyz,number_knn)
for(h in 1:nrow(xyz)){
temp=rep(0,ncol(RNA.sel.3))
RNA.temp=RNA.sel.2[knn$nn_index[h,],]
knn_gene=knn_Armadillo(RNA.temp,RNA.temp[1,,drop=FALSE],round(number_knn*0.5))$nn_index
for(i in 1:number_knn){
temp=temp+RNA.temp[i,]/(1+knn$distances[h,i]^2)
}
RNA.sel.3[h,]=temp
}
Applying KODAMA
# Apply KODAMA to the PCA results
jj <- KODAMA.matrix.parallel(pca[,1:30],spatial = xyz,f.par.pls = 50, FUN = "PLS", landmarks = 100000, n.cores = 8,splitting=100)socket cluster with 8 nodes on host 'localhost'
================================================================================[1] "Finished parallel computation"
[1] "Calculation of dissimilarity matrix..."
================================================================================config=umap.defaults
config$n_neighbors=30
vis <- KODAMA.visualization(jj, method = "UMAP",config=config)
# Cluster the results
#g <- makeSNNGraph(vis,k = 100)
#g_walk <- cluster_walktrap(g)
#clu = as.character(igraph::cut_at(g_walk, no=8))
clu=kmeans(vis,8,nstart = 100)$cluster
# Plot the clusters
par(mfrow = c(1, 2))
plot(vis, col = tissue_segments, main = "KODAMA tissue")
plot(vis, col = clu, main = "KODAMA Clusters")
ref=refine_SVM(xyz,clu,cost=10000)[1] "1"ARI=NULL
slides=unique(xyz[,3])
for(i in 1:length(slides)){
sel=slides[i]==xyz[,3]
ARI[i]=adjustedRandIndex(ref[sel], tissue_segments[sel])
}
print(ARI)[1] 0.5421992 0.6090266 0.4803425 0.6161870 0.6220334Refinement and Visualization
cols <- c("#669bbc", "#81b29a", "#f2cc8f", "#adc178",
"#dde5b6", "#a8dadc", "#e5989b", "#e07a5f")
change=sort(tapply(ref,tissue_segments,function(x) which.max(table(x))))
ref=names(change)[ref]
ref=factor(ref,levels = levels(tissue_segments))
df <- data.frame(xyz, tissue_segments)
df <- data.frame(xyz, ref)
colnames(df)=c("x","y","z","tissue_segments")
df$z=as.factor(df$z)
df$tissue_segments=as.factor(df$tissue_segments)
ggplot(df, aes(x, y, color = tissue_segments)) +
geom_point(size = 1) +
facet_grid(~z) +
theme_bw() +
theme(
legend.position = "bottom",
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank())+
scale_color_manual("Domain", values = cols) +
guides(color = guide_legend(nrow = 1,
override.aes = list(size = 2)))
# 3D Visualization for Slice -0.14
library(rgl)
library(MASS)
library(misc3d)
volume_rendering <- function(xyz, tissue_segments,selection=NULL, alpha=NULL, colors=NULL,cells=c(20, 20, 20), level=exp(-3)) {
if(!is.factor(tissue_segments)){
stop("tissue_segments is not a factor")
}
option_tissue=levels(tissue_segments)
if(is.null(colors)){
colors=rainbow(length(option_tissue))
}else{
if(length(option_tissue)!=length(alpha)){
stop("The number of color does not match")
}
}
if(is.null(alpha)){
alpha=rep(1,length(option_tissue))
}else{
if(length(option_tissue)!=length(alpha)){
stop("The number of alpha does not match")
}
}
if(!is.null(selection)){
option_tissue=selection
}
ww=which(levels(tissue_segments) %in% option_tissue)
colors=colors[ww]
alpha=alpha[ww]
cells[1]=min(cells[1],length(unique(xyz[,1])))
cells[2]=min(cells[2],length(unique(xyz[,2])))
cells[3]=min(cells[3],length(unique(xyz[,3])))
sel.alpha=alpha>0
option_tissue=option_tissue[sel.alpha]
alpha=alpha[sel.alpha]
colors=colors[sel.alpha]
for (i in 1:length(option_tissue) ){
segment <- option_tissue[i]
sel <- tissue_segments == segment
d <- kde3d(xyz[sel, 1], xyz[sel, 2], xyz[sel, 3], n = cells)
e=array(0,dim=cells+2)
e[2:(cells[1]+1),2:(cells[2]+1),2:(cells[3]+1)]=d$d
dx=c(d$x[1]-d$x[2],d$x,d$x[cells[1]]+d$x[2]-d$x[1])
dy=c(d$y[1]-d$y[2],d$y,d$y[cells[2]]+d$y[2]-d$y[1])
dz=c(d$z[1]-d$z[2],d$z,d$z[cells[3]]+d$z[2]-d$z[1])
contour3d(e, level=level, dx, dy, dz,
color = colors[i], scale = FALSE,
engine = "rgl", draw = TRUE, alpha = alpha[i], add = (i != 1))
}
rglwidget()
}
volume_rendering(xyz,ref,selection = c("V3","PVH","PV","fx"),alpha = c(0.8,0.8,0.8,0.8,0.8,0.8,0.8,0.8) ,colors = cols,cells=c(20, 20, 20),level=exp(2.8))
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 20.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] misc3d_0.9-1 MASS_7.3-61
[3] mclust_6.1.1 plotly_4.10.4
[5] SPARK_1.1.1 scater_1.32.1
[7] ggplot2_3.5.1 scuttle_1.14.0
[9] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[11] Biobase_2.64.0 GenomicRanges_1.56.1
[13] GenomeInfoDb_1.40.1 IRanges_2.38.1
[15] S4Vectors_0.42.1 BiocGenerics_0.50.0
[17] MatrixGenerics_1.16.0 matrixStats_1.3.0
[19] KODAMAextra_1.0 e1071_1.7-14
[21] doParallel_1.0.17 iterators_1.0.14
[23] foreach_1.5.2 KODAMA_3.1
[25] umap_0.2.10.0 Rtsne_0.17
[27] minerva_1.5.10 irlba_2.3.5.1
[29] Matrix_1.7-0 rgl_1.3.1
[31] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] rstudioapi_0.16.0 jsonlite_1.8.8
[3] magrittr_2.0.3 ggbeeswarm_0.7.2
[5] farver_2.1.2 rmarkdown_2.27
[7] fs_1.6.4 zlibbioc_1.50.0
[9] vctrs_0.6.5 DelayedMatrixStats_1.26.0
[11] CompQuadForm_1.4.3 askpass_1.2.0
[13] base64enc_0.1-3 htmltools_0.5.8.1
[15] S4Arrays_1.4.1 BiocNeighbors_1.22.0
[17] SparseArray_1.4.8 sass_0.4.9
[19] pracma_2.4.4 bslib_0.7.0
[21] htmlwidgets_1.6.4 cachem_1.1.0
[23] whisker_0.4.1 lifecycle_1.0.4
[25] pkgconfig_2.0.3 rsvd_1.0.5
[27] R6_2.5.1 fastmap_1.2.0
[29] GenomeInfoDbData_1.2.12 digest_0.6.36
[31] colorspace_2.1-0 ps_1.7.7
[33] rprojroot_2.0.4 RSpectra_0.16-1
[35] beachmat_2.20.0 labeling_0.4.3
[37] fansi_1.0.6 httr_1.4.7
[39] abind_1.4-5 compiler_4.4.1
[41] proxy_0.4-27 withr_3.0.0
[43] BiocParallel_1.38.0 viridis_0.6.5
[45] highr_0.11 openssl_2.2.0
[47] DelayedArray_0.30.1 tools_4.4.1
[49] vipor_0.4.7 beeswarm_0.4.0
[51] httpuv_1.6.15 glue_1.7.0
[53] callr_3.7.6 promises_1.3.0
[55] grid_4.4.1 getPass_0.2-4
[57] generics_0.1.3 snow_0.4-4
[59] gtable_0.3.5 class_7.3-22
[61] tidyr_1.3.1 data.table_1.15.4
[63] BiocSingular_1.20.0 ScaledMatrix_1.12.0
[65] utf8_1.2.4 XVector_0.44.0
[67] ggrepel_0.9.5 pillar_1.9.0
[69] stringr_1.5.1 later_1.3.2
[71] dplyr_1.1.4 lattice_0.22-6
[73] tidyselect_1.2.1 knitr_1.48
[75] git2r_0.33.0 gridExtra_2.3
[77] xfun_0.45 stringi_1.8.4
[79] UCSC.utils_1.0.0 lazyeval_0.2.2
[81] yaml_2.3.9 evaluate_0.24.0
[83] codetools_0.2-20 tcltk_4.4.1
[85] tibble_3.2.1 cli_3.6.3
[87] matlab_1.0.4.1 reticulate_1.38.0
[89] munsell_0.5.1 processx_3.8.4
[91] jquerylib_0.1.4 Rcpp_1.0.12
[93] doSNOW_1.0.20 png_0.1-8
[95] sparseMatrixStats_1.16.0 viridisLite_0.4.2
[97] scales_1.3.0 purrr_1.0.2
[99] crayon_1.5.3 rlang_1.1.4