Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----eval=FALSE---------------------------------------------------------------
# githubURL <- "https://github.com/feiyoung/FAST/blob/main/vignettes_data/seulist2_ID9_10.RDS?raw=true"
# download.file(githubURL,"seulist2_ID9_10.RDS",mode='wb')
#
## ----eval =FALSE--------------------------------------------------------------
# dlpfc2 <- readRDS("./seulist2_ID9_10.RDS")
# dlpfc <- dlpfc2[[1]]
## ----eval =FALSE--------------------------------------------------------------
# library(ProFAST) # load the package of FAST method
# library(PRECAST)
# library(Seurat)
## ----eval =FALSE--------------------------------------------------------------
# dlpfc ## a list including three Seurat object with default assay: RNA
## ----eval =FALSE--------------------------------------------------------------
# print(row.names(dlpfc)[1:10])
# count <- dlpfc[['RNA']]@counts
# row.names(count) <- unname(transferGeneNames(row.names(count), now_name = "ensembl",
# to_name="symbol",
# species="Human", Method='eg.db'))
# print(row.names(count)[1:10])
# seu <- CreateSeuratObject(counts = count, meta.data = dlpfc@meta.data)
# seu
## ----eval =FALSE--------------------------------------------------------------
# ## Check the spatial coordinates: they are named as "row" and "col"!
# print(head(seu@meta.data))
## ----eval =FALSE--------------------------------------------------------------
# seu <- NormalizeData(seu)
# seu <- FindVariableFeatures(seu)
# print(seu)
# print(seu[['RNA']]@var.features[1:10])
## ----eval = FALSE-------------------------------------------------------------
# set.seed(2023)
# seu <- DR.SC::FindSVGs(seu)
# ### Check the results
# print(seu[['RNA']]@var.features[1:10])
## ----eval =FALSE--------------------------------------------------------------
# Adj_sp <- AddAdj(as.matrix(seu@meta.data[,c("row", "col")]), platform = "Visium")
# ### set q= 15 here
# set.seed(2023)
# seu <- FAST_single(seu, Adj_sp=Adj_sp, q= 15, fit.model='poisson')
# seu
## ----eval = FALSE-------------------------------------------------------------
# Adj_sp <- AddAdj(as.matrix(seu@meta.data[,c("row", "col")]), platform = "Visium")
# set.seed(2023)
# seu <- FAST_single(dlpfc, Adj_sp=Adj_sp, q= 15, fit.model='gaussian')
# ### Check the results
# seu
## ----eval =FALSE--------------------------------------------------------------
# ## Obtain the true labels
# y <- seu$layer_guess_reordered
# ### Evaluate the MacR2
# Mac <- get_r2_mcfadden(Embeddings(seu, reduction='fast'), y)
# ### output them
# print(paste0("MacFadden's R-square of FAST is ", round(Mac, 3)))
## ----eval =FALSE--------------------------------------------------------------
# seu <- FindNeighbors(seu, reduction = 'fast')
# seu <- FindClusters(seu, resolution = 0.4)
# seu$fast.cluster <- seu$seurat_clusters
# ARI.fast <- mclust::adjustedRandIndex(y, seu$fast.cluster)
# print(paste0("ARI of PCA is ", round(ARI.fast, 3)))
## ----eval =FALSE--------------------------------------------------------------
# seu <- ScaleData(seu)
# seu <- RunPCA(seu, npcs=15, verbose=FALSE)
# Mac.pca <- get_r2_mcfadden(Embeddings(seu, reduction='pca'), y)
# print(paste0("MacFadden's R-square of PCA is ", round(Mac.pca, 3)))
# set.seed(1)
# seu <- FindNeighbors(seu, reduction = 'pca', graph.name ="pca.graph")
# seu <- FindClusters(seu, resolution = 0.8,graph.name = 'pca.graph')
# seu$pca.cluster <- seu$seurat_clusters
# ARI.pca <- mclust::adjustedRandIndex(y, seu$pca.cluster)
# print(paste0("ARI of PCA is ", round(ARI.pca, 3)))
## ----eval =FALSE--------------------------------------------------------------
# cols_cluster <- chooseColors(palettes_name = "Nature 10", n_colors = 8, plot_colors = TRUE)
## ----eval =FALSE, fig.width=8, fig.height=3-----------------------------------
# seu <- PRECAST::Add_embed(embed = as.matrix(seu@meta.data[,c("row", "col")]), seu, embed_name = 'Spatial')
# seu
# p1 <- DimPlot(seu, reduction = 'Spatial', group.by = 'pca.cluster',cols = cols_cluster, pt.size = 1.5)
# p2 <- DimPlot(seu, reduction = 'Spatial', group.by = 'fast.cluster',cols = cols_cluster, pt.size = 1.5)
# drawFigs(list(p1, p2),layout.dim = c(1,2) )
## ----eval =FALSE, fig.width=5, fig.height=4-----------------------------------
# seu <- RunUMAP(seu, reduction = "fast", dims=1:15)
# seu
# DimPlot(seu, reduction='umap', group.by = "fast.cluster")
## ----eval =FALSE--------------------------------------------------------------
# Idents(seu) <- seu$fast.cluster
# dat_deg <- FindAllMarkers(seu)
# library(dplyr)
# n <- 5
# dat_deg %>%
# group_by(cluster) %>%
# top_n(n = n, wt = avg_log2FC) -> top5
# top5
## -----------------------------------------------------------------------------
sessionInfo()
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.