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Last updated: 2024-08-12

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Knit directory: KODAMA-Analysis/

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File	Version	Author	Date	Message
Rmd	5ef8148	Stefano Cacciatore	2024-08-12	Start my new project
html	3374e66	Stefano Cacciatore	2024-08-06	Build site.
html	35ce733	Stefano Cacciatore	2024-08-03	Build site.
html	82fe167	Stefano Cacciatore	2024-07-24	Build site.
Rmd	b422e43	Stefano Cacciatore	2024-07-24	Start my new project
html	6f7daac	Stefano Cacciatore	2024-07-19	Build site.
Rmd	5b97082	tkcaccia	2024-07-15	updates
Rmd	7be8f59	tkcaccia	2024-07-15	updates
html	7be8f59	tkcaccia	2024-07-15	updates
Rmd	79f73a2	GitHub	2024-07-14	Update VisiumHD.Rmd
html	f8ca54a	tkcaccia	2024-07-14	update
html	d04c1e7	GitHub	2024-07-08	Update VisiumHD.html
html	754c8bf	GitHub	2024-07-04	Update VisiumHD.html
html	ee4ee17	GitHub	2024-06-19	Add files via upload
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Describe your project. The data can be downloaded using the following script: VisiumHD_CRC_download.sh. This script facilitates access to the raw data, which will then be preprocessed and analyzed in the subsequent steps of our pipeline.

The data can be downloaded using the following script: VisiumHD_CRC_download.sh. This script facilitates access to the raw data, which will then be preprocessed and analyzed in the subsequent steps of our pipeline.

library("ggplot2")
library("patchwork")
library("dplyr")
library("Seurat")
library("KODAMA")
library("KODAMAextra")

localdir="../Colorectal/outs/"
object <- Load10X_Spatial(data.dir = localdir, bin.size = c(8))


 vln.plot <- VlnPlot(object, features = "nCount_Spatial.008um", pt.size = 0) + NoLegend()
 count.plot <- SpatialFeaturePlot(object, features = "nCount_Spatial.008um", pt.size.factor = 1.2) +
   theme(legend.position = "right")

nCount_Spatial=colSums(object@assays$Spatial.008um$counts)
#w= which(nCount_Spatial >10)
#object@assays$Spatial.008um$counts= object@assays$Spatial.008um$counts[,w]
#object@meta.data=object@meta.data[w,]

sp_obj <- subset(
  object,
  subset = nCount_Spatial.008um > 100)



nCount_Spatial=colSums(sp_obj@assays$Spatial.008um$counts)


 counts=sp_obj@assays$Spatial.008um$counts
 is_mito <- grepl("(^MT-)|(^mt-)", rownames(counts))
 counts <- counts[!is_mito,]

 filter_genes_ncounts=1
 filter_genes_pcspots=0.5
 nspots <- ceiling(filter_genes_pcspots/100 *  ncol(counts))
 ix_remove <- rowSums(counts >= filter_genes_ncounts) <   nspots
 counts <- counts[!ix_remove,]

 QCgenes <- rownames(counts)

 VariableFeatures(sp_obj) = QCgenes

 rm(counts)


DefaultAssay(sp_obj) <- "Spatial.008um"
sp_obj <- NormalizeData(sp_obj)


sp_obj <- FindVariableFeatures(sp_obj)
sp_obj <- ScaleData(sp_obj)

xy=as.matrix(GetTissueCoordinates(sp_obj)[,1:2])


RNA.sel.2=(t(sp_obj@assays$Spatial.008um$scale.data))
number_knn=10
RNA.sel.3=RNA.sel.2
knn=knn_Armadillo(xy,xy,number_knn)
for(h in 1:nrow(xy)){
  temp=rep(0,ncol(RNA.sel.3))
  RNA.temp=as.matrix(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

  print(h)
}

sp_obj@assays$Spatial.008um$scale.data=t(RNA.sel.3)





xy=as.matrix(GetTissueCoordinates(sp_obj))

sp_obj <- RunPCA(sp_obj, reduction.name = "pca.008um")

dim(sp_obj)

[1]  18085 428381

plot(Seurat::Embeddings(sp_obj, reduction = "pca.008um"))

Version	Author	Date
6f7daac	Stefano Cacciatore	2024-07-19
7be8f59	tkcaccia	2024-07-15

sp_obj <- RunKODAMAmatrix(sp_obj, reduction = "pca.008um",
                          FUN= "PLS" ,
                          landmarks = 10000,
                          splitting = 100,
                          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"
     sp_obj <- RunKODAMAvisualization(sp_obj, method = "UMAP",config=config)

     

plot(Seurat::Embeddings(sp_obj, reduction = "KODAMA"))

Version	Author	Date
82fe167	Stefano Cacciatore	2024-07-24

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  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] KODAMAextra_1.0    e1071_1.7-14       doParallel_1.0.17  iterators_1.0.14  
 [5] foreach_1.5.2      KODAMA_3.1         umap_0.2.10.0      Rtsne_0.17        
 [9] minerva_1.5.10     Seurat_5.1.0       SeuratObject_5.0.2 sp_2.1-4          
[13] dplyr_1.1.4        patchwork_1.2.0    ggplot2_3.5.1      workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3     rstudioapi_0.16.0      jsonlite_1.8.8        
  [4] magrittr_2.0.3         ggbeeswarm_0.7.2       spatstat.utils_3.0-5  
  [7] farver_2.1.2           rmarkdown_2.27         fs_1.6.4              
 [10] vctrs_0.6.5            ROCR_1.0-11            spatstat.explore_3.3-1
 [13] askpass_1.2.0          htmltools_0.5.8.1      sass_0.4.9            
 [16] sctransform_0.4.1      parallelly_1.38.0      KernSmooth_2.23-24    
 [19] bslib_0.7.0            htmlwidgets_1.6.4      ica_1.0-3             
 [22] plyr_1.8.9             plotly_4.10.4          zoo_1.8-12            
 [25] cachem_1.1.0           whisker_0.4.1          igraph_2.0.3          
 [28] mime_0.12              lifecycle_1.0.4        pkgconfig_2.0.3       
 [31] Matrix_1.7-0           R6_2.5.1               fastmap_1.2.0         
 [34] fitdistrplus_1.2-1     future_1.34.0          shiny_1.9.1           
 [37] digest_0.6.36          colorspace_2.1-0       ps_1.7.7              
 [40] rprojroot_2.0.4        tensor_1.5             RSpectra_0.16-1       
 [43] irlba_2.3.5.1          progressr_0.14.0       fansi_1.0.6           
 [46] spatstat.sparse_3.1-0  httr_1.4.7             polyclip_1.10-7       
 [49] abind_1.4-5            compiler_4.4.1         proxy_0.4-27          
 [52] bit64_4.0.5            withr_3.0.0            fastDummies_1.7.3     
 [55] highr_0.11             MASS_7.3-61            openssl_2.2.0         
 [58] tools_4.4.1            vipor_0.4.7            lmtest_0.9-40         
 [61] beeswarm_0.4.0         httpuv_1.6.15          future.apply_1.11.2   
 [64] goftest_1.2-3          glue_1.7.0             callr_3.7.6           
 [67] nlme_3.1-165           promises_1.3.0         grid_4.4.1            
 [70] getPass_0.2-4          cluster_2.1.6          reshape2_1.4.4        
 [73] snow_0.4-4             generics_0.1.3         hdf5r_1.3.11          
 [76] gtable_0.3.5           spatstat.data_3.1-2    class_7.3-22          
 [79] tidyr_1.3.1            data.table_1.15.4      utf8_1.2.4            
 [82] spatstat.geom_3.3-2    RcppAnnoy_0.0.22       ggrepel_0.9.5         
 [85] RANN_2.6.1             pillar_1.9.0           stringr_1.5.1         
 [88] spam_2.10-0            RcppHNSW_0.6.0         later_1.3.2           
 [91] splines_4.4.1          lattice_0.22-6         bit_4.0.5             
 [94] survival_3.7-0         deldir_2.0-4           tidyselect_1.2.1      
 [97] miniUI_0.1.1.1         pbapply_1.7-2          knitr_1.48            
[100] git2r_0.33.0           gridExtra_2.3          scattermore_1.2       
[103] xfun_0.45              matrixStats_1.3.0      stringi_1.8.4         
[106] lazyeval_0.2.2         yaml_2.3.9             evaluate_0.24.0       
[109] codetools_0.2-20       tibble_3.2.1           cli_3.6.3             
[112] uwot_0.2.2             arrow_16.1.0           xtable_1.8-4          
[115] reticulate_1.38.0      munsell_0.5.1          processx_3.8.4        
[118] jquerylib_0.1.4        Rcpp_1.0.12            doSNOW_1.0.20         
[121] globals_0.16.3         spatstat.random_3.3-1  png_0.1-8             
[124] ggrastr_1.0.2          spatstat.univar_3.0-0  assertthat_0.2.1      
[127] dotCall64_1.1-1        listenv_0.9.1          viridisLite_0.4.2     
[130] scales_1.3.0           ggridges_0.5.6         leiden_0.4.3.1        
[133] purrr_1.0.2            rlang_1.1.4            cowplot_1.1.3