## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  tidy = TRUE,
  fig.width = 11,
  tidy.opts = list(width.cutoff = 95),
  message = FALSE,
  warning = FALSE,
  time_it = TRUE
)
all_times <- list()  # store the time for each chunk
knitr::knit_hooks$set(time_it = local({
  now <- NULL
  function(before, options) {
    if (before) {
      now <<- Sys.time()
    } else {
      res <- difftime(Sys.time(), now, units = "secs")
      all_times[[options$label]] <<- res
    }
  }
}))

## ----eval =FALSE--------------------------------------------------------------
# suppressPackageStartupMessages(library(Seurat))
# suppressPackageStartupMessages(library(SingleCellExperiment))
# name_ID4 <- as.character(c(151673, 151674, 151675, 151676))
# 
# ### Read data in an online manner
# n_ID <- length(name_ID4)
# url_brainA <- "https://github.com/feiyoung/DR-SC.Analysis/raw/main/data/DLPFC_data/"; url_brainB <- ".rds"
# seuList <- list()
# if(!require(ProFAST)){
#   remotes::install_github("feiyoung/ProFAST")
# }
# for(i in 1:n_ID){
#   # i <- 1
#   cat('input brain data', i, '\n')
#   # load and read data
#   dlpfc <- readRDS(url(paste0(url_brainA, name_ID4[i],url_brainB) ))
#   # dlpfc <- readRDS((paste0('', name_ID4[i],url_brainB) ))
#   count <- dlpfc@assays@data$counts
#   row.names(count) <- ProFAST::transferGeneNames(row.names(count), species = "Human")
#   row.names(count) <- make.unique(row.names(count))
#   seu1 <- CreateSeuratObject(counts = count,
#                              meta.data = as.data.frame(colData(dlpfc)),
#                               min.cells = 10,min.features = 10)
#   seuList[[i]] <- seu1
# }
# # saveRDS(seuList, file='seuList4.RDS')
# 

## ----eval = FALSE-------------------------------------------------------------
# library(PRECAST)

## ----eval = FALSE-------------------------------------------------------------
# seuList <- readRDS("seuList4.RDS")
# seuList ## a list including  Seurat objects

## ----eval = FALSE-------------------------------------------------------------
# metadataList <- lapply(seuList, function(x) x@meta.data)
# 
# for(r in seq_along(metadataList)){
#   meta_data <- metadataList[[r]]
#   cat(all(c("row", "col") %in% colnames(meta_data)), '\n') ## the names are correct!
# 
# }

## ----eval = FALSE-------------------------------------------------------------
# set.seed(2023)
# preobj <- CreatePRECASTObject(seuList = seuList, selectGenesMethod="HVGs",
#                               gene.number = 2000) #

## ----eval = FALSE-------------------------------------------------------------
# ## check the number of genes/features after filtering step
# preobj@seulist
# ## Add adjacency matrix list for a PRECASTObj object to prepare for PRECAST model fitting.
# PRECASTObj <-  AddAdjList(preobj, platform = "Visium")
# ## Add a model setting in advance for a PRECASTObj object. verbose =TRUE helps outputing the
# ## information in the algorithm. We provide two initial model to obtain initial values: 'mclust' and 'kmeans',
# ## and recommend using 'mclust' in LIBD data.
# PRECASTObj <- AddParSetting(PRECASTObj, Sigma_equal = TRUE, coreNum = 1,  int.model='mclust',
#                             maxIter=30, verbose = TRUE)
# 

## ----eval = FALSE-------------------------------------------------------------
# ### Given K
# PRECASTObj <- PRECAST(PRECASTObj, K= 7)

## ----eval = FALSE-------------------------------------------------------------
# ## backup the fitting results in resList
# resList <- PRECASTObj@resList
# PRECASTObj <- SelectModel(PRECASTObj)
# ari_precast <- sapply(1:length(seuList), function(r) mclust::adjustedRandIndex(PRECASTObj@resList$cluster[[r]], PRECASTObj@seulist[[r]]$layer_guess_reordered))
# mat <- matrix(round(ari_precast,2), nrow=1)
# name_ID4 <- as.character(c(151673, 151674, 151675, 151676))
# colnames(mat) <-   name_ID4
# DT::datatable(mat)

## ----eval =FALSE--------------------------------------------------------------
# PRECASTObj2 <- AddParSetting(PRECASTObj, Sigma_equal = FALSE, coreNum = 4,
#                             maxIter=30, verbose = TRUE) # set 4 cores to run in parallel.
# PRECASTObj2 <- PRECAST(PRECASTObj2, K= 5:8)
# ## backup the fitting results in resList
# resList2 <- PRECASTObj2@resList
# PRECASTObj2 <- SelectModel(PRECASTObj2)
# 

## ----eval = FALSE-------------------------------------------------------------
# print(PRECASTObj@seuList)
# seuInt <- IntegrateSpaData(PRECASTObj, species='Human')
# seuInt
# ## The low-dimensional embeddings obtained by PRECAST are saved in PRECAST reduction slot.

## ----eval =FALSE--------------------------------------------------------------
# ## assign the raw Seurat list object to it.
# ## For illustration, we generate a new seuList with more genes;
# ## For integrating all genes, users can set `seuList <- bc2`.
# genes <- c(row.names(PRECASTObj@seulist[[1]]), row.names(seuList[[1]])[1:10])
# seuList_sub <- lapply(seuList, function(x) x[genes,])
# PRECASTObj@seuList <- seuList_sub #
# seuInt <- IntegrateSpaData(PRECASTObj, species='Human')
# seuInt

## ----eval =FALSE--------------------------------------------------------------
# PRECASTObj@seuList <- NULL
# ## At the same time, we can set subsampling to speed up the computation.
# seuInt <- IntegrateSpaData(PRECASTObj, species='Human', seuList=seuList_sub, subsample_rate = 0.5)
# seuInt

## ----eval = FALSE-------------------------------------------------------------
# cols_cluster <- chooseColors(palettes_name = 'Classic 20', n_colors=7, plot_colors = TRUE)

## ----eval = FALSE, fig.height = 8, fig.width=9--------------------------------
# 
# p12 <- SpaPlot(seuInt, item='cluster', batch=NULL,point_size=1, cols=cols_cluster, combine=TRUE, nrow.legend=7)
# p12
# 
# # users can plot each sample by setting combine=FALSE

## ----eval = FALSE, fig.height = 8, fig.width=8.5------------------------------
# library(ggplot2)
# pList <- SpaPlot(seuInt, item='cluster', batch=NULL,point_size=2.5, cols=cols_cluster, combine=FALSE, nrow.legend=7)
# pList <- lapply(pList, function(x) x +  coord_flip() + scale_x_reverse())
# drawFigs(pList, layout.dim = c(2,2), common.legend = TRUE, legend.position = 'right', align='hv')
# 

## ----eval = FALSE, fig.height = 6, fig.width=5.5------------------------------
# seuInt <- AddUMAP(seuInt)
# p13List <- SpaPlot(seuInt, batch=NULL,item='RGB_UMAP',point_size=2, combine=FALSE, text_size=15)
# p13List <- lapply(p13List, function(x) x +  coord_flip() + scale_x_reverse())
# drawFigs(p13List, layout.dim = c(2,2), common.legend = TRUE, legend.position = 'right', align='hv')
# #seuInt <- AddTSNE(seuInt)
# #SpaPlot(seuInt, batch=NULL,item='RGB_TSNE',point_size=2, combine=T, text_size=15)

## ----eval = FALSE, fig.height = 4.5, fig.width=12-----------------------------
# seuInt <- AddTSNE(seuInt, n_comp = 2)
# p1 <- dimPlot(seuInt, item='cluster', point_size = 0.5, font_family='serif', cols=cols_cluster,border_col="gray10", nrow.legend=14, legend_pos='right') # Times New Roman
# p2 <- dimPlot(seuInt, item='batch', point_size = 0.5,  font_family='serif', legend_pos='right')
# 
# drawFigs(list(p1, p2), layout.dim = c(1,2), legend.position = 'right', align='hv')
# 

## ----eval = FALSE-------------------------------------------------------------
# library(Seurat)
# dat_deg <- FindAllMarkers(seuInt)
# library(dplyr)
# n <- 5
# dat_deg %>%
#   group_by(cluster) %>%
#   top_n(n = n, wt = avg_log2FC) -> top10
# 
# 

## ----eval = FALSE, fig.height = 6, fig.width=8--------------------------------
# library(ggplot2)
# ## HeatMap
# p1 <- DotPlot(seuInt, features = unique(top10$gene), col.min = 0, col.max = 1) +
#   theme(axis.text.x = element_text(angle = 45, hjust = 1, size=8))
# p1

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