MERFISH data analysis
Last updated: 2025-04-13
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Knit directory: KODAMA-Analysis/
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Introduction
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. Data was retrieved from the BASS GitHub repository The original data can be downloaded from Dryad.
Loading and Preprocessing Data
The R library are loaded and the number of parallel computation is set.
# Load the necessary libraries
library(rgl)
library(irlba)
library(KODAMAextra)
library(scater)
library(SPARK)
library(ggplot2)
library(plotly)
library(mclust)
library(harmony)
library(bluster)
library(igraph)
n.cores=16The spatial ttranscriptomic data and relative spatial coordinates are loaded in the R environment.
# Load the MERFISH data
load("../MERFISH_Animal1.RData")
cols <- c("#669bbc", "#81b29a", "#f2cc8f", "#adc178",
"#dde5b6", "#a8dadc", "#e5989b", "#e07a5f",
"#aae5b6", "#a8aadc", "#e59811", "#aa7900")
# 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
Genes are ranked using the SPARK-X algorithm independently for each slide. The top gene are the spatially expressed genes.
top=multi_SPARKX(RNA,xyz[,-3],as.factor(xyz[,3]),n.cores = n.cores)Passing message
The gene expression value are processed using the passing-message function.
RNA.PM=passing.message(RNA[,top[1:100]],xyz)Dimensionality reduction
The dimensionality of the dataset is reduced using the principal component analysis (PCA) and batch-effect is removed using the harmony function.
RNA.PM.scaled=scale(RNA.PM)
pca_results <- irlba(A = RNA.PM.scaled, nv = 50)
pca.PM <- pca_results$u %*% diag(pca_results$d)
pca.PM=RunHarmony(pca.PM,data.frame(z=xyz[,3]),"z")
RNA.scaled=scale(RNA[,top[1:100]])
pca_results <- irlba(A = RNA.scaled, nv = 50)
pca <- pca_results$u %*% diag(pca_results$d)
par(mfrow = c(1, 2))
plot(pca, col = cols[tissue_segments], main = "PCA",pch=20,xlab="PC1",ylab="PC2")
plot(pca.PM, col = cols[tissue_segments], main = "PCA.PM",pch=20,xlab="PC1",ylab="PC2")
Applying KODAMA
KODAMA is performed of the first 20 principal components of PCA.
# Apply KODAMA to the PCA results
jj=KODAMA.matrix.parallel(pca.PM[,1:20],
spatial = xyz,
landmarks = 100000,
n.cores=n.cores,
seed = 543210)Calculating Network
Calculating Network spatialsocket cluster with 16 nodes on host 'localhost'
================================================================================
Finished parallel computation
[1] "Calculation of dissimilarity matrix..."
================================================================================config=umap.defaults
config$n_neighbors=30
config$n_threads = n.cores
vis <- KODAMA.visualization(jj, method = "UMAP",config=config)Clustering and Refinement
A louvain clustering is performed.
g <- makeSNNGraph(as.matrix(vis), k = 100)
clu= louvain(g,ncluster = 8)$membership[1] 0 1
[1] 0.2 8.0ref=refine_SVM(xyz,clu,cost=1000)[1] "1"[1] 0.4927165 0.6436110 0.5047492 0.6667729 0.6808519
# 3D Visualization of Slice -0.14
library(rgl)
library(MASS)
library(misc3d)
volume_rendering(xyz[!is.na(ref),],ref[!is.na(ref)],selection = c("3","7","8","6"),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(2.4))
sessionInfo()R version 4.4.3 (2025-02-28)
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] BayesSpace_1.14.0 Seurat_5.2.1
[3] SeuratObject_5.0.2 sp_2.2-0
[5] PRECAST_1.6.6 ProFAST_1.6
[7] gtools_3.9.5 SpatialExperiment_1.14.0
[9] Banksy_1.0.0 BASS_1.1.0.017
[11] GIGrvg_0.8 misc3d_0.9-1
[13] MASS_7.3-65 igraph_2.1.4
[15] bluster_1.14.0 harmony_1.2.3
[17] Rcpp_1.0.14 mclust_6.1.1
[19] plotly_4.10.4 SPARK_1.1.1
[21] scater_1.32.1 ggplot2_3.5.1
[23] scuttle_1.14.0 SingleCellExperiment_1.26.0
[25] SummarizedExperiment_1.34.0 Biobase_2.64.0
[27] GenomicRanges_1.56.2 GenomeInfoDb_1.40.1
[29] IRanges_2.38.1 S4Vectors_0.42.1
[31] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[33] matrixStats_1.5.0 KODAMAextra_1.2
[35] e1071_1.7-16 doParallel_1.0.17
[37] iterators_1.0.14 foreach_1.5.2
[39] KODAMA_3.0 umap_0.2.10.0
[41] Rtsne_0.17 minerva_1.5.10
[43] irlba_2.3.5.1 Matrix_1.7-3
[45] rgl_1.3.18 workflowr_1.7.1
loaded via a namespace (and not attached):
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[3] vctrs_0.6.5 spatstat.random_3.3-3
[5] digest_0.6.37 png_0.1-8
[7] proxy_0.4-27 git2r_0.33.0
[9] ggrepel_0.9.6 deldir_2.0-4
[11] parallelly_1.43.0 combinat_0.0-8
[13] Rnanoflann_0.0.3 magick_2.8.6
[15] reshape2_1.4.4 GiRaF_1.0.1
[17] httpuv_1.6.15 withr_3.0.2
[19] xfun_0.51 ggpubr_0.6.0
[21] survival_3.8-3 memoise_2.0.1
[23] ggbeeswarm_0.7.2 zoo_1.8-13
[25] pbapply_1.7-2 Formula_1.2-5
[27] promises_1.3.2 httr_1.4.7
[29] rstatix_0.7.2 rhdf5filters_1.16.0
[31] globals_0.16.3 fitdistrplus_1.2-2
[33] rhdf5_2.48.0 ps_1.9.0
[35] rstudioapi_0.17.1 UCSC.utils_1.0.0
[37] miniUI_0.1.1.1 generics_0.1.3
[39] base64enc_0.1-3 processx_3.8.6
[41] curl_6.2.2 zlibbioc_1.50.0
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[59] magrittr_2.0.3 lmtest_0.9-40
[61] later_1.4.1 viridis_0.6.5
[63] lattice_0.22-7 spatstat.geom_3.3-6
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[143] ggridges_0.5.6 progressr_0.15.1
[145] limma_3.60.6 edgeR_4.2.2
[147] jsonlite_2.0.0 miscTools_0.6-28
[149] RcppHNSW_0.6.0 bitops_1.0-9
[151] xgboost_1.7.9.1 bit64_4.6.0-1
[153] assertthat_0.2.1 spatstat.utils_3.1-3
[155] BiocNeighbors_1.22.0 matlab_1.0.4.1
[157] metapod_1.12.0 jquerylib_0.1.4
[159] dqrng_0.4.1 spatstat.univar_3.1-2
[161] lazyeval_0.2.2 shiny_1.10.0
[163] htmltools_0.5.8.1 sctransform_0.4.1
[165] glue_1.8.0 tcltk_4.4.3
[167] spam_2.11-1 XVector_0.44.0
[169] RCurl_1.98-1.17 rprojroot_2.0.4
[171] scran_1.32.0 gridExtra_2.3
[173] sccore_1.0.5 R6_2.6.1
[175] tidyr_1.3.1 CompQuadForm_1.4.3
[177] labeling_0.4.3 cluster_2.1.8.1
[179] Rhdf5lib_1.26.0 DelayedArray_0.30.1
[181] tidyselect_1.2.1 vipor_0.4.7
[183] car_3.1-3 future_1.34.0
[185] leidenAlg_1.1.4 rsvd_1.0.5
[187] munsell_0.5.1 KernSmooth_2.23-26
[189] furrr_0.3.1 data.table_1.17.0
[191] htmlwidgets_1.6.4 RColorBrewer_1.1-3
[193] rlang_1.1.5 spatstat.sparse_3.1-0
[195] spatstat.explore_3.4-2 beeswarm_0.4.0