## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", out.width="100%", fig.width=7, fig.height=5, dpi=300, fig.path="figures/BayesSpace-", message=FALSE, warning=FALSE, error=FALSE ) ## ----setup-------------------------------------------------------------------- library(SingleCellExperiment) library(ggplot2) library(BayesSpace) ## ----readVisium, eval=FALSE--------------------------------------------------- # sce <- readVisium("path/to/spaceranger/outs/") ## ----download----------------------------------------------------------------- melanoma <- getRDS(dataset="2018_thrane_melanoma", sample="ST_mel1_rep2") ## ----manual.sce, eval=FALSE--------------------------------------------------- # library(Matrix) # # rowData <- read.csv("path/to/rowData.csv", stringsAsFactors=FALSE) # colData <- read.csv("path/to/colData.csv", stringsAsFactors=FALSE, row.names=1) # counts <- read.csv("path/to/counts.csv.gz", # row.names=1, check.names=F, stringsAsFactors=FALSE)) # # sce <- SingleCellExperiment(assays=list(counts=as(counts, "dgCMatrix")), # rowData=rowData, # colData=colData) ## ----preprocess--------------------------------------------------------------- set.seed(102) melanoma <- spatialPreprocess(melanoma, platform="ST", n.PCs=7, n.HVGs=2000, log.normalize=FALSE) ## ----tuning_q----------------------------------------------------------------- melanoma <- qTune(melanoma, qs=seq(2, 10), platform="ST", d=7) qPlot(melanoma) ## ----cluster------------------------------------------------------------------ set.seed(149) melanoma <- spatialCluster(melanoma, q=4, platform="ST", d=7, init.method="mclust", model="t", gamma=2, nrep=1000, burn.in=100, save.chain=TRUE) ## ----cluster.results---------------------------------------------------------- head(colData(melanoma)) ## ----cluster.plot, fig.width=7, fig.height=5---------------------------------- clusterPlot(melanoma) ## ----cluster.plot.customize, fig.width=7, fig.height=5------------------------ clusterPlot(melanoma, palette=c("purple", "red", "blue", "yellow"), color="black") + theme_bw() + xlab("Column") + ylab("Row") + labs(fill="BayesSpace\ncluster", title="Spatial clustering of ST_mel1_rep2") ## ----enhance, eval=TRUE------------------------------------------------------- melanoma.enhanced <- spatialEnhance(melanoma, q=4, platform="ST", d=7, model="t", gamma=2, jitter_prior=0.3, jitter_scale=3.5, nrep=1000, burn.in=100, save.chain=TRUE) ## ----enhance.results---------------------------------------------------------- head(colData(melanoma.enhanced)) ## ----enhance.plot, eval=TRUE, fig.width=7, fig.height=5----------------------- clusterPlot(melanoma.enhanced) ## ----enhanceFeatures---------------------------------------------------------- markers <- c("PMEL", "CD2", "CD19", "COL1A1") melanoma.enhanced <- enhanceFeatures(melanoma.enhanced, melanoma, feature_names=markers, nrounds=0) ## ----enhanced.logcount-------------------------------------------------------- logcounts(melanoma.enhanced)[markers, 1:5] ## ----enhanced.rmse------------------------------------------------------------ rowData(melanoma.enhanced)[markers, ] ## ----enhanced.featurePlot----------------------------------------------------- featurePlot(melanoma.enhanced, "PMEL") ## ----enhanced.markers, fig.width=12, fig.height=8----------------------------- enhanced.plots <- purrr::map(markers, function(x) featurePlot(melanoma.enhanced, x)) patchwork::wrap_plots(enhanced.plots, ncol=2) ## ----compare.resolution, fig.width=16, fig.height=8--------------------------- spot.plots <- purrr::map(markers, function(x) featurePlot(melanoma, x)) patchwork::wrap_plots(c(enhanced.plots, spot.plots), ncol=4) ## ----mcmcChain, eval=TRUE----------------------------------------------------- chain <- mcmcChain(melanoma) chain[1:5, 1:5]