Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----eval = FALSE-------------------------------------------------------------
# set.seed(2024) # set a random seed for reproducibility.
# library(ProFAST) # load the package of FAST method
# data(CosMx_subset)
# CosMx_subset
## ----eval = FALSE-------------------------------------------------------------
# library(Seurat)
## ----eval = FALSE-------------------------------------------------------------
# CosMx_subset <- NormalizeData(CosMx_subset)
## ----eval = FALSE-------------------------------------------------------------
# CosMx_subset <- FindVariableFeatures(CosMx_subset)
## ----eval = FALSE, fig.width= 6, fig.height= 4.5------------------------------
# dat_cor <- diagnostic.cor.eigs(CosMx_subset)
# q_est <- attr(dat_cor, "q_est")
# cat("q_est = ", q_est, '\n')
## ----eval = FALSE-------------------------------------------------------------
# pos <- as.matrix(CosMx_subset@meta.data[,c("x", "y")]) # Extract the spatial coordinates
# Adj_sp <- AddAdj(pos) ## calculate the adjacency matrix
# CosMx_subset <- NCFM_fast(CosMx_subset, Adj_sp = Adj_sp, q = q_est)
# CosMx_subset
## ----eval = FALSE-------------------------------------------------------------
# CosMx_subset <- pdistance(CosMx_subset, reduction = "fast")
## ----eval = FALSE-------------------------------------------------------------
# print(table(CosMx_subset$cell_type))
# Idents(CosMx_subset) <- CosMx_subset$cell_type
# df_sig_list <- find.signature.genes(CosMx_subset)
# str(df_sig_list)
## ----eval = FALSE-------------------------------------------------------------
# dat <- get.top.signature.dat(df_sig_list, ntop = 2, expr.prop.cutoff = 0.1)
# head(dat)
## ----eval = FALSE, fig.width=10,fig.height=7----------------------------------
# CosMx_subset <- coembedding_umap(
# CosMx_subset, reduction = "fast", reduction.name = "UMAP",
# gene.set = unique(dat$gene))
## ----eval = FALSE, fig.width=8,fig.height=5-----------------------------------
# ## choose beutifual colors
# cols_cluster <- c("black", PRECAST::chooseColors(palettes_name = "Blink 23", n_colors = 21, plot_colors = TRUE))
# p1 <- coembed_plot(
# CosMx_subset, reduction = "UMAP",
# gene_txtdata = subset(dat, label=='tumor 5'),
# cols=cols_cluster, pt_text_size = 3)
# p1
## ----eval = FALSE, fig.width=9,fig.height=6-----------------------------------
# p2 <- coembed_plot(
# CosMx_subset, reduction = "UMAP",
# gene_txtdata = dat, cols=cols_cluster,
# pt_text_size = 3, alpha=0.2)
# p2
## ----eval = FALSE, fig.width=9,fig.height=6-----------------------------------
# cols_type <- cols_cluster[-1]
# names(cols_type)<- sort(levels(Idents(CosMx_subset)))
# DimPlot(CosMx_subset, reduction = 'UMAP', cols=cols_type)
## ----eval = FALSE, fig.width=8,fig.height=3.6---------------------------------
# FeaturePlot(CosMx_subset, reduction = 'UMAP', features = c("PSCA", "CEACAM6"))
## -----------------------------------------------------------------------------
sessionInfo()
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