DLPFC
Last updated: 2024-07-24
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Knit directory: KODAMA-Analysis/
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Introduction
Here, we apply KODAMA to analyze the human dorsolateral prefrontal cortex (DLPFC) data by 10x Visium from Maynard et al., 2021. The links to download the raw data and H&E full resolution images can be found in the LieberInstitute/spatialLIBD github page.
Loading the required libraries
library("nnSVG")
library("scater")
library("scran")
library("scry")
library("SPARK")
library("harmony")
library("Seurat")
library("spatialLIBD")
library("KODAMA")
library("KODAMAextra")
library("mclust")Download the dataset
spe <- fetch_data(type = 'spe')Extract the metadata information
sample_names=c("151507",
"151508",
"151509",
"151510",
"151669",
"151670",
"151671",
"151672",
"151673",
"151674",
"151675",
"151676")
subject_names= c("Br5292","Br5595", "Br8100")
metaData = SingleCellExperiment::colData(spe)
expr = SingleCellExperiment::counts(spe)
sample_names <- paste0("sample_", unique(colData(spe)$sample_id))
sample_names <- unique(colData(spe)$sample_id)
dim(spe)[1] 33538 47681Preprocessing
Quality control
We identified mitochondrial genes, calculated per-spot QC metrix and select the QC threshoulds. Low quality spots are discarded.
is_mito <- grepl("(^MT-)|(^mt-)", rowData(spe)$gene_name)
table(is_mito)is_mito
FALSE TRUE
33525 13 spe <- addPerCellQC(spe, subsets = list(mito = is_mito))
# select QC thresholds
qc_lib_size <- colData(spe)$sum < 500
qc_detected <- colData(spe)$detected < 250
qc_mito <- colData(spe)$subsets_mito_percent > 30
qc_cell_count <- colData(spe)$cell_count > 12
discard <- qc_lib_size | qc_detected | qc_mito | qc_cell_count
table(discard)discard
FALSE TRUE
46653 1028 colData(spe)$discard <- discard
spe <- spe[, !colData(spe)$discard]
dim(spe)[1] 33538 46653Horizontalization
The position of spots is corrected to allow to set the slides in the same XY plane.
xy=spatialCoords(spe)
samples=unique(colData(spe)$sample_id)
for(j in 1:length(samples)){
sel=samples[j]==colData(spe)$sample_id
xy[sel,1]=spatialCoords(spe)[sel,1]+12000*(j-1)
}
spatialCoords(spe)=xyGene filtering
spe <- filter_genes(
spe,
filter_genes_ncounts = 2, #ncounts
filter_genes_pcspots = 0.5,
filter_mito = TRUE
)
dim(spe)[1] 6623 46653Spots that are not assigned to any tissue regions are removed from the analysis.
sel= !is.na(colData(spe)$layer_guess_reordered)
spe = spe[,sel]
dim(spe)[1] 6623 46318Normalization
spe <- computeLibraryFactors(spe)
spe <- logNormCounts(spe)Gene selection
The identification of genes that display spatial expression patterns is performed using the SPARKX method (Zhu et al. (2021)). The genes are ranked based on the median value of the logarithm value of the p-value obtained in each slide individually.
gene_i=NULL
pvalue_i=NULL
pvalue_mat=matrix(nrow=nrow(spe),ncol=length(sample_names))
for(i in 1:length(sample_names)){
sel=colData(spe)$sample_id==sample_names[i]
spe_sub= spe[,sel]
sparkX <- sparkx(logcounts(spe_sub),spatialCoords(spe_sub),numCores=1,option="mixture")
gene_i=c(gene_i,rowData(spe)$gene_id)
pvalue_i=c(pvalue_i,sparkX$res_mtest$combinedPval)
pvalue_mat[,i]=sparkX$res_mtest$combinedPval
print(sample_names[i])
}Warning in FUN(newX[, i], ...): There are p-values that are exactly 1!
Warning in FUN(newX[, i], ...): There are p-values that are exactly 1!
Warning in FUN(newX[, i], ...): There are p-values that are exactly 1!
Warning in FUN(newX[, i], ...): There are p-values that are exactly 1!
Warning in FUN(newX[, i], ...): There are p-values that are exactly 1!
Warning in FUN(newX[, i], ...): There are p-values that are exactly 1!
Warning in FUN(newX[, i], ...): There are p-values that are exactly 1!
Warning in FUN(newX[, i], ...): There are p-values that are exactly 1!
Warning in FUN(newX[, i], ...): There are p-values that are exactly 1!oo=order(pvalue_i)
top_genes=gene_i[oo]
n=ave(1:length(top_genes), top_genes, FUN = seq_along)
top_genes=top_genes[n==1]
oo=order(apply(pvalue_mat,1,function(x) median(-log(x))),decreasing = TRUE)
top=gene_i[oo]PCA and HARMONY
data=t(logcounts(spe)[top[1:2000],])
subjects=colData(spe)$subject
dim(spe_sub)[1] 6623 3326 spe <- runPCA(spe, 50,subset_row = top[1:2000], scale=TRUE)
#pca=irlba(scale(data),50)$u
labels=as.factor(colData(spe)$layer_guess_reordered)
xy=as.matrix(spatialCoords(spe))
samples=colData(spe)$sample_id
spe <- RunHarmony(spe, "subject",lambda=NULL)
# pca <- RunHarmony(pca, subjects,lambda=NULL)
pca=reducedDim(spe,type = "HARMONY")[,1:50]
plot(pca)
KODAMA
kk=KODAMA.matrix.parallel(pca,
spatial = xy,
FUN= "PLS" ,
landmarks = 100000,
splitting = 300,
f.par.pls = 50,
spatial.resolution = 0.4,
n.cores=4)socket cluster with 4 nodes on host 'localhost'
================================================================================[1] "Finished parallel computation"
[1] "Calculation of dissimilarity matrix..."
================================================================================ print("KODAMA finished")[1] "KODAMA finished" config=umap.defaults
config$n_threads = 4
config$n_sgd_threads = "auto"
kk_UMAP=KODAMA.visualization(kk,method="UMAP",config=config)CLUSTER
library("mclust")
clu=kmeans(kk_UMAP,7,nstart = 100)$cluster
plot(kk_UMAP,col=labels,pch=20)
plot(kk_UMAP,col=clu,pch=20)
plot(xy,col=clu,pch=20)
u=unique(samples)
for(i in 1:length(u)){
sel=samples==u[i]
print(adjustedRandIndex(labels[sel],clu[sel]))
}[1] 0.5424012
[1] 0.4869132
[1] 0.4918829
[1] 0.482701
[1] 0.3316558
[1] 0.3128665
[1] 0.3886265
[1] 0.4199793
[1] 0.5528813
[1] 0.566796
[1] 0.5556891
[1] 0.5284708ref=refine_SVM(xy,clu,samples,cost=100)[1] "151507"
[1] "151508"
[1] "151509"
[1] "151510"
[1] "151669"
[1] "151670"
[1] "151671"
[1] "151672"
[1] "151673"
[1] "151674"
[1] "151675"
[1] "151676"u=unique(samples)
for(i in 1:length(u)){
sel=samples==u[i]
print(adjustedRandIndex(labels[sel],ref[sel]))
}[1] 0.5808856
[1] 0.5194297
[1] 0.5068338
[1] 0.5109041
[1] 0.3762886
[1] 0.3615001
[1] 0.4520455
[1] 0.5476094
[1] 0.6065583
[1] 0.6119949
[1] 0.6139298
[1] 0.5945212plot(xy,col=ref,pch=20)
TRAJECTORY
library("slingshot")
d <- slingshot(kk_UMAP, clusterLabels = clu)
trajectory=d@metadata$curves$Lineage1$s
k=knn_Armadillo(trajectory,kk_UMAP,1)
map_color=rainbow(nrow(trajectory))[k$nn_index]
plot(kk_UMAP,col=map_color)
plot(xy,col=map_color)
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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] slingshot_2.12.0 TrajectoryUtils_1.12.0
[3] princurve_2.1.6 mclust_6.1.1
[5] KODAMAextra_1.0 e1071_1.7-14
[7] doParallel_1.0.17 iterators_1.0.14
[9] foreach_1.5.2 KODAMA_3.1
[11] umap_0.2.10.0 Rtsne_0.17
[13] minerva_1.5.10 spatialLIBD_1.16.2
[15] SpatialExperiment_1.14.0 Seurat_5.1.0
[17] SeuratObject_5.0.2 sp_2.1-4
[19] harmony_1.2.0 Rcpp_1.0.12
[21] SPARK_1.1.1 scry_1.16.0
[23] scran_1.32.0 scater_1.32.0
[25] ggplot2_3.5.1 scuttle_1.14.0
[27] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[29] Biobase_2.64.0 GenomicRanges_1.56.1
[31] GenomeInfoDb_1.40.1 IRanges_2.38.1
[33] S4Vectors_0.42.1 BiocGenerics_0.50.0
[35] MatrixGenerics_1.16.0 matrixStats_1.3.0
[37] nnSVG_1.8.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] goftest_1.2-3 DT_0.33
[3] Biostrings_2.72.1 vctrs_0.6.5
[5] spatstat.random_3.3-1 digest_0.6.36
[7] png_0.1-8 proxy_0.4-27
[9] git2r_0.33.0 ggrepel_0.9.5
[11] deldir_2.0-4 parallelly_1.37.1
[13] magick_2.8.4 MASS_7.3-61
[15] reshape2_1.4.4 httpuv_1.6.15
[17] withr_3.0.0 xfun_0.45
[19] survival_3.7-0 memoise_2.0.1
[21] benchmarkme_1.0.8 ggbeeswarm_0.7.2
[23] zoo_1.8-12 pbapply_1.7-2
[25] rematch2_2.1.2 KEGGREST_1.44.1
[27] promises_1.3.0 httr_1.4.7
[29] restfulr_0.0.15 globals_0.16.3
[31] fitdistrplus_1.2-1 ps_1.7.7
[33] rstudioapi_0.16.0 UCSC.utils_1.0.0
[35] miniUI_0.1.1.1 generics_0.1.3
[37] processx_3.8.4 curl_5.2.1
[39] fields_16.2 zlibbioc_1.50.0
[41] ScaledMatrix_1.12.0 polyclip_1.10-6
[43] doSNOW_1.0.20 GenomeInfoDbData_1.2.12
[45] ExperimentHub_2.12.0 SparseArray_1.4.8
[47] golem_0.4.1 xtable_1.8-4
[49] stringr_1.5.1 pracma_2.4.4
[51] evaluate_0.24.0 S4Arrays_1.4.1
[53] BiocFileCache_2.12.0 irlba_2.3.5.1
[55] colorspace_2.1-0 filelock_1.0.3
[57] ROCR_1.0-11 reticulate_1.38.0
[59] spatstat.data_3.1-2 shinyWidgets_0.8.6
[61] magrittr_2.0.3 lmtest_0.9-40
[63] later_1.3.2 viridis_0.6.5
[65] lattice_0.22-6 spatstat.geom_3.3-2
[67] future.apply_1.11.2 getPass_0.2-4
[69] scattermore_1.2 XML_3.99-0.17
[71] cowplot_1.1.3 RcppAnnoy_0.0.22
[73] class_7.3-22 pillar_1.9.0
[75] nlme_3.1-165 compiler_4.4.1
[77] beachmat_2.20.0 RSpectra_0.16-1
[79] stringi_1.8.4 tensor_1.5
[81] GenomicAlignments_1.40.0 plyr_1.8.9
[83] crayon_1.5.3 abind_1.4-5
[85] BiocIO_1.14.0 locfit_1.5-9.10
[87] bit_4.0.5 dplyr_1.1.4
[89] whisker_0.4.1 codetools_0.2-20
[91] BiocSingular_1.20.0 openssl_2.2.0
[93] bslib_0.7.0 paletteer_1.6.0
[95] plotly_4.10.4 mime_0.12
[97] splines_4.4.1 fastDummies_1.7.3
[99] dbplyr_2.5.0 sparseMatrixStats_1.16.0
[101] attempt_0.3.1 knitr_1.48
[103] blob_1.2.4 utf8_1.2.4
[105] BiocVersion_3.19.1 fs_1.6.4
[107] listenv_0.9.1 DelayedMatrixStats_1.26.0
[109] rdist_0.0.5 tibble_3.2.1
[111] Matrix_1.7-0 callr_3.7.6
[113] statmod_1.5.0 pkgconfig_2.0.3
[115] tools_4.4.1 BRISC_1.0.5
[117] cachem_1.1.0 RhpcBLASctl_0.23-42
[119] RSQLite_2.3.7 viridisLite_0.4.2
[121] DBI_1.2.3 fastmap_1.2.0
[123] rmarkdown_2.27 scales_1.3.0
[125] grid_4.4.1 ica_1.0-3
[127] Rsamtools_2.20.0 AnnotationHub_3.12.0
[129] sass_0.4.9 patchwork_1.2.0
[131] BiocManager_1.30.23 dotCall64_1.1-1
[133] RANN_2.6.1 snow_0.4-4
[135] yaml_2.3.9 rtracklayer_1.64.0
[137] cli_3.6.3 purrr_1.0.2
[139] leiden_0.4.3.1 lifecycle_1.0.4
[141] askpass_1.2.0 uwot_0.2.2
[143] bluster_1.14.0 sessioninfo_1.2.2
[145] BiocParallel_1.38.0 gtable_0.3.5
[147] rjson_0.2.21 ggridges_0.5.6
[149] progressr_0.14.0 limma_3.60.3
[151] jsonlite_1.8.8 edgeR_4.2.1
[153] RcppHNSW_0.6.0 bitops_1.0-7
[155] benchmarkmeData_1.0.4 bit64_4.0.5
[157] spatstat.utils_3.0-5 BiocNeighbors_1.22.0
[159] matlab_1.0.4.1 highr_0.11
[161] jquerylib_0.1.4 metapod_1.12.0
[163] config_0.3.2 dqrng_0.4.1
[165] spatstat.univar_3.0-0 lazyeval_0.2.2
[167] shiny_1.8.1.1 htmltools_0.5.8.1
[169] sctransform_0.4.1 rappdirs_0.3.3
[171] glue_1.7.0 spam_2.10-0
[173] XVector_0.44.0 RCurl_1.98-1.16
[175] rprojroot_2.0.4 gridExtra_2.3
[177] igraph_2.0.3 R6_2.5.1
[179] tidyr_1.3.1 CompQuadForm_1.4.3
[181] cluster_2.1.6 DelayedArray_0.30.1
[183] tidyselect_1.2.1 vipor_0.4.7
[185] maps_3.4.2 AnnotationDbi_1.66.0
[187] future_1.33.2 rsvd_1.0.5
[189] munsell_0.5.1 KernSmooth_2.23-24
[191] data.table_1.15.4 htmlwidgets_1.6.4
[193] RColorBrewer_1.1-3 rlang_1.1.4
[195] spatstat.sparse_3.1-0 spatstat.explore_3.3-1
[197] fansi_1.0.6 beeswarm_0.4.0