Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----eval=FALSE---------------------------------------------------------------
# library("SC.MEB")
## ----eval=FALSE---------------------------------------------------------------
# file = system.file("extdata", "CRC3.rds", package = "SC.MEB")
# CRC = readRDS(file)
## ----eval=FALSE---------------------------------------------------------------
# set.seed(114)
# library(scuttle)
# library(scran)
# library(scater)
# library(BiocSingular)
# CRC <- spatialPreprocess(CRC, platform="Visium")
## ----eval=FALSE---------------------------------------------------------------
# platform = "Visium"
# beta_grid = seq(0,4,0.2)
# K_set= 2:10
# parallel=TRUE
# num_core = 3
# PX = TRUE
# maxIter_ICM = 10
# maxIter = 50
## ----eval=FALSE---------------------------------------------------------------
# library(SingleCellExperiment)
# Adj_sp <- find_neighbors2(CRC, platform = "Visium")
# Adj_sp[1:10,1:10]
## ----eval=FALSE---------------------------------------------------------------
# y = reducedDim(CRC, "PCA")[,1:15]
# fit = SC.MEB(y, Adj_sp, beta_grid = beta_grid, K_set= K_set, parallel=parallel, num_core = num_core, PX = PX, maxIter_ICM=maxIter_ICM, maxIter=maxIter)
# str(fit[,1])
## ----eval=FALSE---------------------------------------------------------------
# selectKPlot(fit, K_set = K_set, criterion = "BIC")
## ----eval=FALSE---------------------------------------------------------------
# selectKPlot(fit, K_set = K_set, criterion = "MBIC")
## ----eval=FALSE---------------------------------------------------------------
# out = selectK(fit, K_set = K_set, criterion = "BIC")
# pos = matrix(cbind(colData(CRC)[,c(4)],20000-colData(CRC)[,c(3)]), 2988, 2)
# ClusterPlot(out, pos, size = 3, shape = 16)
## ----eval=FALSE---------------------------------------------------------------
# out = selectK(fit, K_set = K_set, criterion = "MBIC")
# pos = matrix(cbind(colData(CRC)[,c(4)],20000-colData(CRC)[,c(3)]), 2988, 2)
# ClusterPlot(out, pos, size = 3, shape = 16)
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