Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----eval = FALSE-------------------------------------------------------------
# set.seed(2024) # set a random seed for reproducibility.
# library(Seurat)
# pbmc3k <- SeuratData::LoadData("pbmc3k")
# ## filter the seurat_annotation is NA
# idx <- which(!is.na(pbmc3k$seurat_annotations))
# pbmc3k <- pbmc3k[,idx]
# pbmc3k
## ----eval = FALSE-------------------------------------------------------------
# library(ProFAST) # load the package of FAST method
# library(Seurat)
## ----eval = FALSE-------------------------------------------------------------
# pbmc3k <- NormalizeData(pbmc3k)
## ----eval = FALSE-------------------------------------------------------------
# pbmc3k <- FindVariableFeatures(pbmc3k)
## ----eval = FALSE, fig.width= 6, fig.height= 4.5------------------------------
# dat_cor <- diagnostic.cor.eigs(pbmc3k)
# q_est <- attr(dat_cor, "q_est")
# cat("q_est = ", q_est, '\n')
## ----eval = FALSE-------------------------------------------------------------
# pbmc3k <- NCFM(pbmc3k, q = q_est)
# pbmc3k
## ----eval = FALSE-------------------------------------------------------------
# pbmc3k <- pdistance(pbmc3k, reduction = "ncfm")
## ----eval = FALSE-------------------------------------------------------------
# print(table(pbmc3k$seurat_annotations))
# Idents(pbmc3k) <- pbmc3k$seurat_annotations
# df_sig_list <- find.signature.genes(pbmc3k)
# str(df_sig_list)
## ----eval = FALSE-------------------------------------------------------------
# dat <- get.top.signature.dat(df_sig_list, ntop = 5, expr.prop.cutoff = 0.1)
# head(dat)
## ----eval = FALSE, fig.width=10,fig.height=7----------------------------------
# pbmc3k <- coembedding_umap(
# pbmc3k, reduction = "ncfm", 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 = "Light 13", n_colors = 9, plot_colors = TRUE))
# p1 <- coembed_plot(
# pbmc3k, reduction = "UMAP",
# gene_txtdata = subset(dat, label=='B'),
# cols=cols_cluster,pt_text_size = 3)
# p1
## ----eval = FALSE, fig.width=8,fig.height=5-----------------------------------
# p2 <- coembed_plot(
# pbmc3k, reduction = "UMAP",
# gene_txtdata = dat, cols=cols_cluster,
# pt_text_size = 3)
# p2
## ----eval = FALSE, fig.width=7,fig.height=4-----------------------------------
# cols_type <- cols_cluster[-1]
# names(cols_type)<- sort(levels(Idents(pbmc3k)))
# DimPlot(pbmc3k, reduction = 'UMAP', cols=cols_type)
## ----eval = FALSE, fig.width=8,fig.height=3.6---------------------------------
# FeaturePlot(pbmc3k, reduction = 'UMAP', features = c("CD79A", "VPREB3"))
## -----------------------------------------------------------------------------
sessionInfo()
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