## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    out.width = "100%"
)

## ----installation, eval=FALSE-------------------------------------------------
# if (!require("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install("spacexr")

## ----setup, message = FALSE---------------------------------------------------
library(ggplot2)
library(SpatialExperiment)
library(SummarizedExperiment)

library(spacexr)

## ----reference----------------------------------------------------------------
# Load simulated data.
data("rctdSim")

# Create SummarizedExperiment.
reference_se <- SummarizedExperiment(
    assays = list(counts = rctdSim$reference_counts),
    colData = rctdSim$reference_cell_types
)

# Examine reference. (optional)
print(dim(assay(reference_se))) # Gene expression matrix dimensions
table(colData(reference_se)$cell_type) # Number of occurrences of each cell type

## ----spatial, fig.align='center', fig.width=5, fig.height=5, out.width='50%'----
# Create SpatialExperiment.
spatial_spe <- SpatialExperiment(
    assay = rctdSim$spatial_rna_counts,
    spatialCoords = rctdSim$spatial_rna_coords
)

# Examine pixels. (optional)
print(dim(assay(spatial_spe))) # Gene expression matrix dimensions
ggplot(spatialCoords(spatial_spe), aes(x, y)) + 
    geom_point(size = 5) + 
    theme(axis.text = element_blank(), axis.ticks = element_blank()
)

## ----trueProportions----------------------------------------------------------
unique(t(assay(rctdSim$proportions_spe)))

## ----trueProportionsPlot, fig.height = 5, fig.width = 7, out.width='75%'------
plotAllWeights(
    rctdSim$proportions_spe,
    r = 0.05, lwd = 0, title = "Ground Truth"
)

## ----createRctd, message = FALSE----------------------------------------------
# Preprocess data
rctd_data <- createRctd(spatial_spe, reference_se)

## ----runRctd, message = FALSE-------------------------------------------------
results_spe <- runRctd(rctd_data, rctd_mode = "doublet", max_cores = 1)

## ----doubletWeights-----------------------------------------------------------
# Cell type mixture 1
assay(results_spe, "weights")[, 1:6]

# Cell type mixture 2
assay(results_spe, "weights")[, 7:12]

## ----classification-----------------------------------------------------------
classification_df <- data.frame(
    pixel = colnames(assay(results_spe)),
    spot_class = colData(results_spe)$spot_class,
    first_type = colData(results_spe)$first_type,
    second_type = colData(results_spe)$second_type
)
classification_df

## ----doubletVisualization, fig.height = 5, fig.width = 7, out.width='75%'-----
plotAllWeights(results_spe, r = 0.05, lwd = 0, title = "Doublet Results")

## ----fullVisualization, fig.height = 5, fig.width = 7, out.width='75%'--------
plotAllWeights(
    results_spe, "weights_full", r = 0.05, lwd = 0, title = "Full Results"
)

## ----densityVisualization, fig.height = 5, fig.width = 7, out.width='75%'-----
plotCellTypeWeight(
    results_spe, "ct1", "weights_full",
    size = 5, stroke = 0.5, title = "\nDensity of Cell Type 1\n"
)

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