## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
eval = FALSE
)
## ------------------------------------------------------------------------
# library(Seurat)
# library(SeuratAddon)
# library(jackstraw)
# # Load the PBMC dataset, change the directory if necessary
# pbmc.data <- Read10X(data.dir = "data/pbmc3k/filtered_gene_bc_matrices/hg19/")
#
# # Initialize the Seurat object with the raw (non-normalized data)
# pbmc <- CreateSeuratObject(raw.data = pbmc.data, min.cells = 3, min.genes = 200, project = "10X_PBMC")
## ------------------------------------------------------------------------
# mito.genes <- grep(pattern = "^MT-", x = rownames(x = pbmc@data), value = TRUE)
# percent.mito <- Matrix::colSums(pbmc@raw.data[mito.genes, ])/Matrix::colSums(pbmc@raw.data)
#
# pbmc <- AddMetaData(object = pbmc, metadata = percent.mito, col.name = "percent.mito")
#
# pbmc <- FilterCells(object = pbmc, subset.names = c("nGene", "percent.mito"),
# low.thresholds = c(200, -Inf), high.thresholds = c(2500, 0.05))
## ------------------------------------------------------------------------
# pbmc <- NormalizeData(object = pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
## ------------------------------------------------------------------------
# pbmc <- FindVariableGenes(object = pbmc, mean.function = ExpMean, dispersion.function = LogVMR, do.plot=FALSE, x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5)
## ------------------------------------------------------------------------
# pbmc <- ScaleData(object = pbmc, vars.to.regress = c("nUMI", "percent.mito"))
## ------------------------------------------------------------------------
# pbmc <- RunPCA(object = pbmc, pc.genes = pbmc@var.genes, do.print = FALSE)
## ------------------------------------------------------------------------
# # ClusterCellsKmeans is a modified version of Seurat::DoKMeans
# pbmc <- ClusterCellsKmeans(pbmc, k.cells = 3)
## ------------------------------------------------------------------------
# # Use optional arguments, e.g.,batch_size=100, num_init=5, max_iters=100
# pbmc <- ClusterCellsKmeans(pbmc, k.cells = 3, minibatch = TRUE)
## ------------------------------------------------------------------------
# pbmc <- EvaluateIdent(pbmc, clustering = "kmeans")
## ------------------------------------------------------------------------
# pbmc <- EvaluateIdent(pbmc, clustering = "KMeans", prob.use = "p.adj", p.adjust.methods = "BH")
## ------------------------------------------------------------------------
# pbmc <- EvaluateIdent(pbmc, clustering = "MiniBatchKMeans", ...)
## ------------------------------------------------------------------------
# pbmc_jackstraw <- EvaluateIdent(pbmc, clustering = "KMeans", return.jackstraw = TRUE)
## ------------------------------------------------------------------------
# #### Run Non-linear dimensional reduction (tSNE)
# pbmc <- RunTSNE(object = pbmc, dims.use = 1:10, do.fast = TRUE)
# # note that you can set do.label=T to help label individual clusters
# tsne_all <- TSNEPlot(object = pbmc, do.label=T)
## ------------------------------------------------------------------------
# ggplot(pbmc@meta.data["ident_prob"]) + geom_histogram(aes(x=ident_prob))
## ------------------------------------------------------------------------
# tsne_piphard <- TSNEPlot2(object = pbmc, do.label=T, ident.threshold=0.9)
## ------------------------------------------------------------------------
# tsne_pipsoft <- TSNEPlot2(object = pbmc, do.label=T, ident.threshold="soft")
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