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Last updated: 2024-07-03

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Knit directory: KODAMA-Analysis/

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Rmd	8e5f41c	GitHub	2024-06-20	Update simulation.Rmd
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Introduction

In the context of working with biological spatial data, such as spatial sequencing data, which provide information on the location of biological molecules in a sample, the goal is to identify clusters of molecules grouped in specific areas, thus indicating regions of particular biological activity. For example, in immunology, analyzing the spatial distribution of immune cells within tissues can reveal insights into immune responses and disease mechanisms. Techniques like KODAMA and UMAP are valuable tools for this type of analysis. In the domain of immunology, we could simulate spatial data to study the distribution of immune cells within a tissue. For instance, simulating the localization of T cells and B cells within a lymph node can help us understand how these cells are organized spatially, which is critical for understanding their interactions and functions in immune responses.

Main simulation study

Scenario 1

The simulated data in this example are generated to demonstrate how to use the KODAMA and UMAP techniques for cluster and spatial domain analysis. We create two distinct regions representing areas rich in T cells and B cells within the lymph node. We generate x and y coordinates for the two regions, assigning labels to simulate different cell types (T cells and B cells).

library("KODAMA")

Loading required package: minerva

Loading required package: Rtsne

Loading required package: umap

Attaching package: 'KODAMA'

The following object is masked from 'package:umap':

    umap.defaults

library("KODAMAextra")

Loading required package: parallel

Loading required package: doParallel

Loading required package: foreach

Loading required package: iterators

Loading required package: e1071

x1=runif(100,min=-1,max=0)
x2=runif(100,min=0,max=1)
y1=runif(100)
y2=runif(100)
x=c(x1,x2)
y=c(y1,y2)
xy=cbind(x,y)

labels=rep(c(TRUE,FALSE),each=100)
ss=sample(100,5)
labels[ss]=!labels[ss]
labels=labels
region=rep(1:0,each=100)

data=cbind(rnorm(200,mean=labels,sd=0.1),
           rnorm(200,mean=labels,sd=0.1))

color.code=c("#1d79d0aa","#53ca3eaa")



kk <- KODAMA.matrix.parallel(data,spatial=xy,spatial.resolution=0.1,M=100,
                      FUN="PLS",
                      landmarks = 100000,
                      splitting = 100,
                      f.par.pls =  2,
                      n.cores=4)

socket cluster with 4 nodes on host 'localhost'
================================================================================[1] "Finished parallel computation"

[1] "Calculation of dissimilarity matrix..."
================================================================================

vis=RunKODAMAvisualization(kk,method="UMAP")

u=umap(data)$layout

par(mfrow=c(1,3))

plot(x,y,bg=color.code[region+1],pch=21+2*!labels,cex=1+1.5*region,axes=FALSE,main="Position")
axis(1)
axis(2,las=2)
box()

plot(vis,type="n",axes=FALSE,main="KODAMA")
axis(1)
axis(2,las=2)
box()
points(vis[!labels,],bg=color.code[region+1][!labels],pch=21+2,cex=1+1.5*region[!labels])
points(vis[labels,],bg=color.code[region+1][labels],pch=21+2*0,cex=1+1.5*region[labels])

plot(u,type="n",axes=FALSE,xlab="Dimension 1",ylab="Dimension 2",main="UMAP")
axis(1)
axis(2,las=2)
box()
points(u[!labels,],bg=color.code[region+1][!labels],pch=21+2,cex=1+1.5*region[!labels])
points(u[labels,],bg=color.code[region+1][labels],pch=21+2*0,cex=1+1.5*region[labels])

Past versions of unnamed-chunk-1-1.png

Version	Author	Date
77ad90d	Stefano Cacciatore	2024-07-02
ee4ee17	GitHub	2024-06-19

Scenario 2

Bla bla.

x1=runif(100,min=-1,max=0)
x2=runif(100,min=0,max=1)
y1=runif(100)
y2=runif(100)
x=c(x1,x2)
y=c(y1,y2)
xy=cbind(x,y)


labels=rep(c(TRUE,FALSE),each=100)

region=rep(1:0,each=100)

data=cbind(rnorm(200,mean=1+labels,sd=0.1),
           rnorm(200,mean=1+labels,sd=0.1),
           rnorm(200,mean=1+labels,sd=0.1),
           rnorm(200,mean=1+labels,sd=0.1))

ll=length(data)
ss=sample(ll,ll*0.5)
data[ss]=0
color.code=c("#1d79d0aa","#53ca3eaa")

sel=apply(data,1,function(x) sum(x>0))>2
data=data[sel,]
region=region[sel]
labels=labels[sel]
xy=xy[sel,]


labels=data>0
labels=labels[,1]+
  labels[,2]*2+
  labels[,3]*4+
  labels[,4]*8
labels=as.numeric(as.factor(labels))+1

pca=prcomp(scale(data))$x

kk <- KODAMA.matrix.parallel(pca,spatial=xy,spatial.resolution=0.1,M=100,
                             FUN="PLS",
                             landmarks = 100000,
                             splitting = 100,
                             f.par.pls =  10,
                             n.cores=4)

The number of components selected for PLS-DA is too high and it will be automatically reduced to 4

socket cluster with 4 nodes on host 'localhost'
================================================================================[1] "Finished parallel computation"

[1] "Calculation of dissimilarity matrix..."
================================================================================

config=umap.defaults
config$n_neighbors=15
vis=RunKODAMAvisualization(kk,method="UMAP",config=config)

u=umap(pca)$layout
old.par = par(mfrow=c(1,3))



plot(xy,bg=labels,pch=21+2*(region),cex=2+1*region,axes=FALSE,main="Position")
axis(1)
axis(2,las=2)
box()

plot(vis,bg=labels,pch=21+2*(region),cex=1+1.5*region,axes=FALSE,main="KODAMA")
axis(1)
axis(2,las=2)
box()

plot(u,bg=labels,pch=21+2*(region),cex=1+1.5*region,axes=FALSE,main="UMAP")
axis(1)
axis(2,las=2)
box()

Past versions of unnamed-chunk-2-1.png

Version	Author	Date
77ad90d	Stefano Cacciatore	2024-07-02
ee4ee17	GitHub	2024-06-19

par(old.par)

Session information

sessionInfo()

R version 4.3.3 (2024-02-29 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: Africa/Johannesburg
tzcode source: internal

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    workflowr_1.7.1  

loaded via a namespace (and not attached):
 [1] sass_0.4.9        utf8_1.2.4        class_7.3-22      stringi_1.8.4    
 [5] lattice_0.22-6    digest_0.6.36     magrittr_2.0.3    evaluate_0.24.0  
 [9] grid_4.3.3        fastmap_1.2.0     rprojroot_2.0.4   jsonlite_1.8.8   
[13] Matrix_1.6-5      processx_3.8.4    whisker_0.4.1     RSpectra_0.16-1  
[17] doSNOW_1.0.20     ps_1.7.6          promises_1.3.0    httr_1.4.7       
[21] fansi_1.0.6       codetools_0.2-20  jquerylib_0.1.4   cli_3.6.2        
[25] rlang_1.1.4       cachem_1.1.0      yaml_2.3.8        tools_4.3.3      
[29] httpuv_1.6.15     reticulate_1.38.0 vctrs_0.6.5       R6_2.5.1         
[33] png_0.1-8         proxy_0.4-27      lifecycle_1.0.4   git2r_0.33.0     
[37] stringr_1.5.1     fs_1.6.4          pkgconfig_2.0.3   callr_3.7.6      
[41] pillar_1.9.0      bslib_0.7.0       later_1.3.2       glue_1.7.0       
[45] Rcpp_1.0.12       highr_0.11        xfun_0.45         tibble_3.2.1     
[49] rstudioapi_0.16.0 knitr_1.47        snow_0.4-4        htmltools_0.5.8.1
[53] rmarkdown_2.27    compiler_4.3.3    getPass_0.2-4     askpass_1.2.0    
[57] openssl_2.2.0