## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    fig.path = 'figures/'
)

## ----eval=TRUE, message=FALSE, warning=FALSE----------------------------------
library(Banksy)

library(data.table)
library(SummarizedExperiment)
library(SpatialExperiment)

library(scater)
library(cowplot)
library(ggplot2)

## ----eval=TRUE, echo=FALSE----------------------------------------------------
se <- readRDS(system.file("extdata/STARmap.rds", package = "Banksy"))

## ----eval=FALSE---------------------------------------------------------------
# #' Change paths accordingly
# gcm_path <- "../data/well11processed_expression_pd.csv.gz"
# mdata_path <- "../data/well11_spatial.csv.gz"
# 
# #' Gene cell matrix
# gcm <- fread(gcm_path)
# genes <- gcm$GENE
# gcm <- as.matrix(gcm[, -1])
# rownames(gcm) <- genes
# 
# #' Spatial coordinates and metadata
# mdata <- fread(mdata_path, skip = 1)
# headers <- names(fread(mdata_path, nrows = 0))
# colnames(mdata) <- headers
# #' Orient spatial coordinates
# xx <- mdata$X
# yy <- mdata$Y
# mdata$X <- max(yy) - yy
# mdata$Y <- max(xx) - xx
# mdata <- data.frame(mdata)
# rownames(mdata) <- colnames(gcm)
# 
# locs <- as.matrix(mdata[, c("X", "Y", "Z")])
# 
# #' Create SpatialExperiment
# se <- SpatialExperiment(
#     assay = list(processedExp = gcm),
#     spatialCoords = locs,
#     colData = mdata
# )

## ----eval=FALSE---------------------------------------------------------------
# lambda <- 0.8
# k_geom <- 30
# npcs <- 50
# aname <- "processedExp"
# se <- Banksy::computeBanksy(se, assay_name = aname, k_geom = k_geom)
# 
# set.seed(1000)
# se <- Banksy::runBanksyPCA(se, lambda = lambda, npcs = npcs)
# 
# set.seed(1000)
# se <- Banksy::clusterBanksy(se, lambda = lambda, npcs = npcs, resolution = 0.8)

## ----eval=TRUE----------------------------------------------------------------
head(colData(se))

## ----domain-segment-spatial, eval=FALSE, fig.height=8, fig.width=7, fig.align='center'----
# cnames <- colnames(colData(se))
# cnames <- cnames[grep("^clust", cnames)]
# 
# plotColData(se, x = "X", y = "Y", point_size = 0.01, colour_by = cnames[1]) +
#     scale_color_manual(values = pals::glasbey()) +
#     coord_equal() +
#     theme(legend.position = "none")

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