inst/doc/basic_functions_vignette.R

In spatialGE: Visualization and Analysis of Spatial Heterogeneity in Spatially-Resolved Gene Expression

## ----vig_settings, include=F--------------------------------------------------
knitr::opts_chunk$set(
  collapse=TRUE,
  comment="#>",
  fig.width=8, fig.height=5
)
options(rmarkdown.html_vignette.check_title=F)

## ----cran_install-------------------------------------------------------------
# install.packages('spatialGE')

## ----install_devtools---------------------------------------------------------
# if("devtools" %in% rownames(installed.packages()) == FALSE){
#   install.packages("devtools")
# }

#devtools::install_github("fridleylab/spatialGE")

## ----load_spatialGE, message=F------------------------------------------------
library('spatialGE')

## ----extrenal_figs1, echo=FALSE, fig.align='center'---------------------------
knitr::include_graphics("img/dir_structure.png")

## ----data_download1-----------------------------------------------------------
lk='https://github.com/FridleyLab/spatialGE_Data/raw/refs/heads/main/tnbc_bassiouni.zip?download='

## ----temp_dir-----------------------------------------------------------------
visium_tmp = tempdir()
unlink(visium_tmp, recursive=TRUE)
dir.create(visium_tmp)

## ----data_download2-----------------------------------------------------------
tryCatch({ # In case data is not available from network
  download.file(lk, destfile=paste0(visium_tmp, '/', 'tnbc_bassiouni.zip'), mode='wb')
  zip_tmp = list.files(visium_tmp, pattern='tnbc_bassiouni.zip$', full.names=TRUE)
  unzip(zipfile=zip_tmp, exdir=thrane_tmp)
  net_check <<- TRUE
}, error = function(e) {
  message("Could not download data. Are you connected to the internet?")
  net_check <<- FALSE
})

## ----extrenal_figs2, echo=FALSE, fig.align='center', eval=net_check-----------
# knitr::include_graphics("img/metadata_file.png")

## ----data_fpaths, eval=net_check----------------------------------------------
# visium_folders <- list.dirs(paste0(visium_tmp, '/tnbc_bassiouni'), full.names=TRUE, recursive=FALSE)

## ----data_fpaths2, eval=net_check---------------------------------------------
# clin_file <- list.files(paste0(visium_tmp, '/tnbc_bassiouni'), full.names=TRUE, recursive=FALSE, pattern='clinical')

## ----create_stlist, warning=F, eval=net_check---------------------------------
# tnbc <- STlist(rnacounts=visium_folders, samples=clin_file, cores=2)

## ----call_stlist, eval=net_check----------------------------------------------
# tnbc

## ----count_stats, eval=net_check----------------------------------------------
# summarize_STlist(tnbc)

## ----count_dstr, eval=net_check-----------------------------------------------
# cp <- distribution_plots(tnbc, plot_type='violin', plot_meta='total_counts')
# cp[['total_counts']]

## ----filter_chunk, eval=net_check---------------------------------------------
# tnbc <- filter_data(tnbc, spot_minreads=5000, spot_mingenes=1000, spot_maxreads=150000)
# 
# cp2 <- distribution_plots(tnbc, plot_type='violin', plot_meta='total_counts')
# cp2[['total_counts']]

## ----pseudobulk, eval=net_check-----------------------------------------------
# tnbc <- pseudobulk_samples(tnbc, max_var_genes=5000)

## ----pca_chunk, fig.align='center', eval=net_check----------------------------
# pseudobulk_dim_plot(tnbc, plot_meta='patient_id')

## ----heatmap_chunk, fig.align='center', eval=net_check------------------------
# hm_p <- pseudobulk_heatmap(tnbc, plot_meta='patient_id', hm_display_genes=30)

## ----norm_chunk, eval=net_check-----------------------------------------------
# tnbc <- transform_data(tnbc, method='log', cores=2)

## ----genequilt_chunk, eval=net_check------------------------------------------
# quilts1 <- STplot(tnbc,
#                   genes=c('NDRG1', 'IGKC'),
#                   samples='sample_094c',
#                   color_pal='BuRd',
#                   ptsize=0.8)

## ----genequilt_chunk2, fig.align='center', eval=net_check---------------------
# ggpubr::ggarrange(plotlist=quilts1, nrow=1, ncol=2, legend='bottom')

## ----genekrige_chunk, eval=net_check------------------------------------------
# tnbc <- gene_interpolation(tnbc,
#                            genes=c('NDRG1', 'IGKC'),
#                            samples=c('sample_094c', 'sample_117e'), cores=2)

## ----plotkrige_chunk1, fig.align='center', eval=net_check---------------------
# kriges1 <- STplot_interpolation(tnbc,
#                                 genes=c('NDRG1', 'IGKC'),
#                                 samples='sample_094c')
# #ggpubr::ggarrange(plotlist=kriges1, nrow=1, ncol=2, common.legend=TRUE, legend='bottom')

## ----plot_tissue, eval=net_check----------------------------------------------
# ip = plot_image(tnbc, samples='sample_094c')
# 
# ip[['image_sample_094c']]

## ----clustespots_chunk, warning=F, message=F, eval=net_check------------------
# tnbc <- STclust(tnbc,
#                 ks='dtc',
#                 ws=c(0, 0.025, 0.05, 0.2), cores=2)

## ----plotclustspots_chunk, fig.align='center', eval=net_check-----------------
# cluster_p <- STplot(tnbc,
#                     samples='sample_094c',
#                     ws=c(0, 0.025, 0.05, 0.2),
#                     color_pal='highcontrast')
# ggpubr::ggarrange(plotlist=cluster_p, nrow=2, ncol=2, legend='right')

## ----sthet_chunk, eval=net_check----------------------------------------------
# tnbc <- SThet(tnbc,
#               genes=c('NDRG1', 'IGKC'),
#               method='moran', cores=2)

## ----clinplot_chunk, fig.align='center', eval=net_check-----------------------
# p <- compare_SThet(tnbc,
#                    samplemeta='overall_survival_days',
#                    color_by='patient_id',
#                    gene=c('NDRG1', 'IGKC'),
#                    color_pal='muted',
#                    ptsize=3)
# 
# p

## ----stats_slot, eval=net_check-----------------------------------------------
# get_gene_meta(tnbc, sthet_only=TRUE)

## ----sphet_table, echo=F, eval=net_check--------------------------------------
# library('magrittr') # Use of pipe operator
# sphet_info <- tibble::tibble(
#   "Statistic"=c('Moran’s I', 'Geary’s C'),
#   "Clustered expression"=c('Closer to 1', 'Closer to 0'),
#   "No expression pattern"=c('Closer to 0', 'Closer to 1'),
#   "Uniform expression"=c('Closer to -1', 'Closer to 2')
#   )
# 
# kbl_t = kableExtra::kbl(sphet_info, align='c', centering=TRUE) %>%
#   kableExtra::kable_styling(position="center", full_width=FALSE)
# kableExtra::add_footnote(kbl_t,
#                          c("Note: The boundaries indicated for each statistic are reached when the number of spots/cells is very large."),
#                          notation="none")

## ----load_simpkg, message=F, eval=net_check-----------------------------------
# library('rpart')
# library('spatstat')
# library('scSpatialSIM')
# library('tidyverse')
# library('janitor')

## ----tissue_sim_1, warning=F, message=F, eval=net_check-----------------------
# wdw <- owin(xrange=c(0, 3), yrange=c(0, 3))
# sim_visium <- CreateSimulationObject(sims=1, cell_types=1, window=wdw)

## ----tissue_sim_2, warning=F, fig.height=4, fig.width=6, eval=net_check-------
# # Generate point process
# # Then, simulate tissue compartments
# set.seed(12345)
# sim_visium <- GenerateSpatialPattern(sim_visium, gridded=TRUE, overwrite=TRUE) %>%
#   GenerateTissue(k=1, xmin=1, xmax=2, ymin=1, ymax=2, sdmin=1, sdmax=2, overwrite=TRUE)
# 
# PlotSimulation(sim_visium, which=1, what="tissue points") +
#   scale_shape_manual(values=c(19, 1))

## ----tissue_sim_3, warning=F, eval=net_check----------------------------------
# # Extract tissue assignments from the `SpatSimObj` object
# # Simulate expression of 'gene_1'
# sim_visium_df <- sim_visium@`Spatial Files`[[1]] %>%
#   clean_names() %>%
#   mutate(gene_1=case_when(tissue_assignment == 'Tissue 1' ~ 1, TRUE ~ 0.1))
# 
# # Generate expression patter of "gene_2"
# for(i in 1:nrow(sim_visium_df)){
#   if(i%%2 == 0){
#     sim_visium_df[i, 'gene_2'] = 1
#   } else{
#     sim_visium_df[i, 'gene_2'] = 0.1
#   }
# }
# 
# # Generate expression of "gene_3"
# # Set seed for resproducibility
# set.seed(12345)
# sim_visium_df[['gene_3']] <- sample(sim_visium_df[['gene_2']])

## ----tissue_sim_4, warning=F, fig.height=4, eval=net_check--------------------
# # Extract simulated expression data
# sim_expr <- sim_visium_df %>%
#   add_column(libname=paste0('spot', seq(1:nrow(.)))) %>%
#   select(c('libname', 'gene_1', 'gene_2', 'gene_3')) %>%
#   column_to_rownames('libname') %>% t() %>%
#   as.data.frame() %>% rownames_to_column('genename')
# 
# # Extract simulated spot locations
# sim_xy <- sim_visium_df %>%
#   add_column(libname=paste0('spot', seq(1:nrow(.)))) %>%
#   select(c('libname', 'y', 'x'))
# 
# # The `STlist` function can take a list of data frames
# simulated <- STlist(rnacounts=list(sim_visium=sim_expr),
#                     spotcoords=list(sim_visium=sim_xy), cores=2)
# 
# # Plot expression
# ps <- STplot(simulated, genes=c('gene_1', 'gene_2', 'gene_3'), data_type='raw', color_pal='sunset', ptsize=1)
# ggpubr::ggarrange(plotlist=ps, ncol=3)

## ----tissue_sim_5, warning=F, message=F, eval=net_check-----------------------
# # The `SThet` function requires normalized data
# simulated <- transform_data(simulated, cores=2)
# 
# # Run `SThet`
# simulated <- SThet(simulated, genes=c('gene_1', 'gene_2', 'gene_3'), method=c('moran', 'geary'))
# 
# # Extract results
# get_gene_meta(simulated, sthet_only=TRUE)

## -----------------------------------------------------------------------------
sessionInfo()