## ----include = FALSE--------------------------------------------------------------------------------------------------
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)

## ----vignetteSetup, echo=FALSE, message=FALSE, warning = FALSE--------------------------------------------------------
## For links
library("BiocStyle")

## Track time spent on making the vignette
startTime <- Sys.time()

## Bib setup
library("RefManageR")

## Write bibliography information
bib <- c(
    R = citation(),
    BiocStyle = citation("BiocStyle")[1],
    knitr = citation("knitr")[3],
    MatrixGenerics = citation("MatrixGenerics")[1],
    RColorBrewer = citation("RColorBrewer")[1],
    RefManageR = citation("RefManageR")[1],
    rmarkdown = citation("rmarkdown")[1],
    sessioninfo = citation("sessioninfo")[1],
    SpatialExperiment = citation("SpatialExperiment")[1],
    spatialLIBD = citation("spatialLIBD")[1],
    HumanPilot = citation("spatialLIBD")[2],
    spatialDLPFC = citation("spatialLIBD")[3],
    tran2021 = RefManageR::BibEntry(
        bibtype = "Article",
        key = "tran2021",
        author = "Tran, Matthew N. and Maynard, Kristen R. and Spangler, Abby and Huuki, Louise A. and Montgomery, Kelsey D. and Sadashivaiah, Vijay and Tippani, Madhavi and Barry, Brianna K. and Hancock, Dana B. and Hicks, Stephanie C. and Kleinman, Joel E. and Hyde, Thomas M. and Collado-Torres, Leonardo and Jaffe, Andrew E. and Martinowich, Keri",
        title = "Single-nucleus transcriptome analysis reveals cell-type-specific molecular signatures across reward circuitry in the human brain",
        year = 2021, doi = "10.1016/j.neuron.2021.09.001",
        journal = "Neuron"
    )
)

## ----"setup", message = FALSE, warning = FALSE------------------------------------------------------------------------
library("spatialLIBD")
spe <- fetch_data(type = "spatialDLPFC_Visium_example_subset")
spe

## ----"white_matter_genes"---------------------------------------------------------------------------------------------
white_matter_genes <- c("GFAP", "AQP4", "MBP", "PLP1")
white_matter_genes <- rowData(spe)$gene_search[
    rowData(spe)$gene_name %in% white_matter_genes
]

## Our list of white matter genes
white_matter_genes

## ----"single_gene"----------------------------------------------------------------------------------------------------
vis_gene(
    spe,
    geneid = white_matter_genes[1],
    point_size = 1.5
)

## ----"histology_only"-------------------------------------------------------------------------------------------------
plot(imgRaster(spe))

## ----"GFAP_boxplot"---------------------------------------------------------------------------------------------------
modeling_results <- fetch_data(type = "modeling_results")
sce_layer <- fetch_data(type = "sce_layer")
sig_genes <- sig_genes_extract_all(
    n = 400,
    modeling_results = modeling_results,
    sce_layer = sce_layer
)
i_gfap <- subset(sig_genes, gene == "GFAP" &
    test == "WM")$top
i_gfap
set.seed(20200206)
layer_boxplot(
    i = i_gfap,
    sig_genes = sig_genes,
    sce_layer = sce_layer
)

## ----"multi_gene_z"---------------------------------------------------------------------------------------------------
vis_gene(
    spe,
    geneid = white_matter_genes,
    multi_gene_method = "z_score",
    point_size = 1.5
)

## ----"multi_gene_pca"-------------------------------------------------------------------------------------------------
vis_gene(
    spe,
    geneid = white_matter_genes,
    multi_gene_method = "pca",
    point_size = 1.5
)

## ----"multi_gene_sparsity"--------------------------------------------------------------------------------------------
vis_gene(
    spe,
    geneid = white_matter_genes,
    multi_gene_method = "sparsity",
    point_size = 1.5
)

## ----"multi_gene_z_score_top_enriched"--------------------------------------------------------------------------------
vis_gene(
    spe,
    geneid = subset(sig_genes, test == "WM")$ensembl[seq_len(25)],
    multi_gene_method = "z_score",
    point_size = 1.5
)

vis_gene(
    spe,
    geneid = subset(sig_genes, test == "WM")$ensembl[seq_len(50)],
    multi_gene_method = "z_score",
    point_size = 1.5
)

## ----"multi_gene_pca_top_enriched"------------------------------------------------------------------------------------
vis_gene(
    spe,
    geneid = subset(sig_genes, test == "WM")$ensembl[seq_len(25)],
    multi_gene_method = "pca",
    point_size = 1.5
)

vis_gene(
    spe,
    geneid = subset(sig_genes, test == "WM")$ensembl[seq_len(50)],
    multi_gene_method = "pca",
    point_size = 1.5
)

## ----"multi_gene_sparsity_top_enriched"-------------------------------------------------------------------------------
vis_gene(
    spe,
    geneid = subset(sig_genes, test == "WM")$ensembl[seq_len(25)],
    multi_gene_method = "sparsity",
    point_size = 1.5
)

vis_gene(
    spe,
    geneid = subset(sig_genes, test == "WM")$ensembl[seq_len(50)],
    multi_gene_method = "sparsity",
    point_size = 1.5
)

## ----"colData_example"------------------------------------------------------------------------------------------------
vis_gene(
    spe,
    geneid = c("sum_gene", "sum_umi"),
    multi_gene_method = "z_score",
    point_size = 1.5
)

## ----"colData_plus_gene"----------------------------------------------------------------------------------------------
vis_gene(
    spe,
    geneid = c("broad_tangram_astro"),
    point_size = 1.5
)
vis_gene(
    spe,
    geneid = c("broad_tangram_astro", white_matter_genes[1]),
    multi_gene_method = "pca",
    point_size = 1.5
)

## ----createVignette, eval=FALSE---------------------------------------------------------------------------------------
# ## Create the vignette
# library("rmarkdown")
# system.time(render("multi_gene_plots.Rmd"))
# 
# ## Extract the R code
# library("knitr")
# knit("multi_gene_plots.Rmd", tangle = TRUE)

## ----reproduce1, echo=FALSE-------------------------------------------------------------------------------------------
## Date the vignette was generated
Sys.time()

## ----reproduce2, echo=FALSE-------------------------------------------------------------------------------------------
## Processing time in seconds
totalTime <- diff(c(startTime, Sys.time()))
round(totalTime, digits = 3)

## ----reproduce3, echo=FALSE-------------------------------------------------------------------------------------------
## Session info
library("sessioninfo")
options(width = 120)
session_info()

## ----vignetteBiblio, results = 'asis', echo = FALSE, warning = FALSE, message = FALSE---------------------------------
## Print bibliography
PrintBibliography(bib, .opts = list(hyperlink = "to.doc", style = "html"))