spacedeconv_paper/16_fig_S5_deconvolution.R at main · ComputationalBiomedicineGroup/spacedeconv_paper · GitHub

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library(spacedeconv)
library(SpatialExperiment)
library(ggplot2)
figures_dir <- "./export/figures"
objects_dir <- "./export/objects"
dir.create(figures_dir, showWarnings = FALSE, recursive = TRUE)
dir.create(objects_dir, showWarnings = FALSE, recursive = TRUE)


spe <- read10xVisium("./data/renal/frozen_b1/")

annot <- read.csv(
    "./data/renal/frozen_b1/outs/spatial/GSM5924046_frozen_b_1_TLS_annotation.csv"
)

spe$tls <- annot$TLS_2_cat

spe <- preprocess(spe)
spe <- spacedeconv::normalize(spe)
rownames(spe) <- rowData(spe)$symbol

deconv <- deconvolute(
    spe,
    method = "quantiseq",
    assay_sp = "cpm",
    tumor = TRUE
)

geneset <- c(
  "IGHA1","IGHG1","IGHG2","IGHG3","IGHG4","IGHGP","IGHM",
  "IGKG","IGLC1","IGLC2","IGLC3","JCHAIN","CD79A","FCRL5",
  "MZB1","SSR4","XBP1","TRBC2","IL7R","CXCL12","LUM",
  "C1QA","C7","CD52","APOE","PTLP","PTGSD","PIM2","DERL3"
)

deconv <- gene_set_score(deconv, genes = geneset, assay = "cpm")

title_size <- 22
font_size <- 18
legend_size <- 20

patho <- plot_spatial(
    deconv,
    result = "tls",
    density = FALSE,
    palette = "BluYl",
    offset_rotation = TRUE,
    title = "Pathology annotation",
    title_size = title_size,
    font_size = font_size,
    legend_size = legend_size,
    spot_size = 1.03
)

geneSet <- plot_spatial(
    deconv,
    result = "geneSet",
    density = FALSE,
    offset_rotation = TRUE,
    title = "TLS score",
    title_size = title_size,
    font_size = font_size,
    legend_size = legend_size,
    spot_size = 1.03
)

qBcells <- plot_spatial(
    deconv,
    result = "quantiseq_B.cell",
    density = FALSE,
    offset_rotation = TRUE,
    smooth = TRUE,
    title = "B cells (sm.)",
    title_size = title_size,
    font_size = font_size,
    legend_size = legend_size,
    spot_size = 1.03
)

plots_5A <- list(patho, geneSet, qBcells)
saveRDS(plots_5A, file = "./export/objects/fig_S5_panels.rds")