Breast Tumor
Last updated: 2022-08-13
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library(SpatialPCA)
library(ggplot2)Loading data
The breast tumor data is available here. We also saved the raw data that we used in our examples in RData format, which can be downloaded from here.
load("./data/Tumor_data.RData")
print(dim(rawcount)) # The count matrix
print(dim(location)) # The location matrixCreate SpatialPCA Object
SpatialPCA takes the raw count data and location coordinates as inputs. We first create a S4 object in the CreateSpatialPCAObject function, then select spatial genes using sparkx in this dataset with large sample size.
# location matrix: n x 2, count matrix: g x n.
# here n is spot number, g is gene number.
# here the column names of sp_count and rownames of location should be matched
ST = CreateSpatialPCAObject(counts=rawcount, location=location, project = "SpatialPCA",gene.type="spatial",sparkversion="spark", gene.number=3000,customGenelist=NULL,min.loctions = 20, min.features=20)Estimate spatial PCs
SpatialPCA constructs a kernel matrix to model the spatial pattern of spatial PCs.
ST = SpatialPCA_buildKernel(ST, kerneltype="gaussian", bandwidthtype="SJ")
ST = SpatialPCA_EstimateLoading(ST,fast=FALSE,SpatialPCnum=20)
ST = SpatialPCA_SpatialPCs(ST, fast=FALSE)In this data, we select “SJ” to use Sheather & Jones (1991) method, which is usually used in small size datasets to calculate the kernel bandwidth. The user can also specify other bandwidth on their own if needed. After specifying the kernel matrix, SpatialPCA estimates the loading matrix and the spatial PCs.
Detect spatial domains
SpatialPCA detects spatial domains through clustering on the spatial PCs. Here we use walktrap’s clustering method for datasets with small number of locations.
clusterlabel= walktrap_clustering(7, ST@SpatialPCs,round(sqrt(dim(ST@location)[1])))
clusterlabel_refine=refine_cluster_10x(clusterlabel,ST@location,shape="square")Spatial domains detected by SpatialPCA.
# set color
cbp_spatialpca = c( "mediumaquamarine", "chocolate1","dodgerblue", "#F0E442","palegreen4","lightblue2","plum1")
# visualize the cluster
plot_cluster(legend="right",location=ST@location,clusterlabel_refine,pointsize=5,text_size=20 ,title_in=paste0("SpatialPCA"),color_in=cbp_spatialpca)
Trajectory inference
In ST data, we focused on trajectory inference on tumor and tumor adjacent regions to investigate how these locations are connected to one another and underlie tumorigenesis. Based on the inferred pseudo-time values, we connected neighboring locations on the tissue to construct trajectories.
library(slingshot)
# trajectory on the whole tissue slice
sim = SingleCellExperiment(assays = SpatialPCA_result$rawcount)
reducedDims(sim) = SimpleList(DRM = t(ST@SpatialPCs))
colData(sim)$Walktrap = factor(clusterlabel_refine)
# in this data we set tumor region as start cluster
sim =slingshot(sim, clusterLabels = 'Walktrap', reducedDim = 'DRM',start.clus="2" )
# focus on tumor and its surrounding region
tumor_ind = which(clusterlabel_refine %in% c(2,3,7))
sim_tumor = SingleCellExperiment(assays = rawcount[,tumor_ind])
reducedDims(sim_tumor) = SimpleList(DRM = t(ST@SpatialPCs[,tumor_ind]))
colData(sim_tumor)$Walktrap = factor(clusterlabel_refine[tumor_ind])
sim_tumor =slingshot(sim_tumor, clusterLabels = 'Walktrap', reducedDim = 'DRM',start.clus="2" )
# in this data we set tumor region as start cluster
summary(sim_tumor@colData@listData)
# visualize on whole tissue
pseudotime_traj1_tumor = sim_tumor@colData@listData$slingPseudotime_1
clusterlabels_tumor = SpatialPCA_result$clusterlabel_refine[tumor_ind]
tumor_ind = which(clusterlabel_refine %in% c(2,3,7))
pseudotime_traj1 = sim@colData@listData$slingPseudotime_1
pseudotime_traj1[-tumor_ind]=NA
pseudotime_traj1[tumor_ind]=pseudotime_traj1_tumor
gridnum = 10
color_in=c( "mediumaquamarine", "chocolate1","dodgerblue", "#F0E442","palegreen4","lightblue2","plum1","black","#CC79A7","mediumpurple","seagreen1")
p_traj1 = plot_trajectory(pseudotime_traj1, location,clusterlabels,gridnum,color_in,pointsize=5 ,arrowlength=0.3,arrowsize=1.3,textsize=15 )
print(ggarrange( p_traj1[[4]],p_traj1[[1]],
ncol = 2, nrow = 1))
High resolution map reconstruction
The users can select data platform (“ST”, “Visium”, or “Other”) in high resolution spatial map reconstruction. We impute nine subspots for each ST spot and impute six subspots for each Visium spot, as the ST and Visium spots are arranged on square and hexagonal lattices. For other types of data, we impute four new locations for each measured location. We also allow users to directly specify the coordinates of unmeasured locations through the “newlocation” option.
STsimu_high_ST = SpatialPCA_highresolution(ST, platform="ST",newlocation=NULL)
cluster_SpatialPCA_high = walktrap_clustering(7, latent_dat=STsimu_high_ST@highPCs,200)
color_in=c( "palegreen4", "chocolate1","plum1", "#F0E442","mediumaquamarine","dodgerblue","lightblue2")
title_in="SpatialPCA High resolution"
plot_cluster(STsimu_high_ST@highPos, as.character(cluster_SpatialPCA_high), pointsize=2,text_size=20 ,title_in,color_in,legend="bottom")
sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
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
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.3.6 SpatialPCA_1.3.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] plyr_1.8.7 igraph_1.2.7 lazyeval_0.2.2
[4] sp_1.5-0 splines_4.2.1 listenv_0.8.0
[7] scattermore_0.8 digest_0.6.29 foreach_1.5.2
[10] htmltools_0.5.3 SPARK_1.1.1 fansi_1.0.3
[13] magrittr_2.0.3 tensor_1.5 cluster_2.1.3
[16] doParallel_1.0.17 ROCR_1.0-11 globals_0.15.0
[19] matrixStats_0.61.0 askpass_1.1 spatstat.sparse_2.1-1
[22] pdist_1.2.1 colorspace_2.0-3 ggrepel_0.9.1
[25] xfun_0.27 dplyr_1.0.9 rgdal_1.5-32
[28] callr_3.7.0 jsonlite_1.8.0 progressr_0.10.1
[31] spatstat.data_2.2-0 survival_3.3-1 zoo_1.8-10
[34] iterators_1.0.14 glue_1.6.2 polyclip_1.10-0
[37] gtable_0.3.0 leiden_0.4.2 future.apply_1.9.0
[40] abind_1.4-5 scales_1.2.0 DBI_1.1.2
[43] miniUI_0.1.1.1 Rcpp_1.0.9 viridisLite_0.4.0
[46] xtable_1.8-4 reticulate_1.22 spatstat.core_2.3-0
[49] matlab_1.0.4 umap_0.2.8.0 htmlwidgets_1.5.4
[52] httr_1.4.3 RColorBrewer_1.1-3 ellipsis_0.3.2
[55] Seurat_4.0.5 ica_1.0-2 pkgconfig_2.0.3
[58] sass_0.4.2 uwot_0.1.11 deldir_1.0-6
[61] utf8_1.2.2 tidyselect_1.1.2 rlang_1.0.2
[64] reshape2_1.4.4 later_1.3.0 munsell_0.5.0
[67] tools_4.2.1 cachem_1.0.6 cli_3.3.0
[70] generics_0.1.3 ggridges_0.5.3 evaluate_0.15
[73] stringr_1.4.0 fastmap_1.1.0 yaml_2.3.5
[76] goftest_1.2-3 processx_3.5.3 knitr_1.36
[79] fs_1.5.2 fitdistrplus_1.1-8 purrr_0.3.4
[82] RANN_2.6.1 pbapply_1.5-0 future_1.26.1
[85] nlme_3.1-157 whisker_0.4 mime_0.12
[88] pracma_2.3.8 compiler_4.2.1 rstudioapi_0.13
[91] plotly_4.10.0 png_0.1-7 spatstat.utils_2.3-1
[94] tibble_3.1.5 bslib_0.4.0 stringi_1.7.8
[97] ps_1.7.0 RSpectra_0.16-1 rgeos_0.5-9
[100] lattice_0.20-45 Matrix_1.4-1 vctrs_0.4.1
[103] CompQuadForm_1.4.3 pillar_1.8.0 lifecycle_1.0.1
[106] spatstat.geom_2.3-0 lmtest_0.9-40 jquerylib_0.1.4
[109] RcppAnnoy_0.0.19 data.table_1.14.2 cowplot_1.1.1
[112] irlba_2.3.5 httpuv_1.6.3 patchwork_1.1.1
[115] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20
[118] gridExtra_2.3 parallelly_1.31.1 codetools_0.2-18
[121] MASS_7.3-57 assertthat_0.2.1 openssl_2.0.2
[124] rprojroot_2.0.3 withr_2.5.0 SeuratObject_4.1.0
[127] sctransform_0.3.3 mgcv_1.8-40 parallel_4.2.1
[130] grid_4.2.1 rpart_4.1.16 tidyr_1.1.4
[133] rmarkdown_2.14 Rtsne_0.16 git2r_0.30.1
[136] getPass_0.2-2 shiny_1.7.2