## ----global_options, include=FALSE--------------------------------------- knitr::opts_chunk$set(warning=FALSE, message=FALSE, include = TRUE, fig.height = 8, fig.width = 8, fig.align = "center", echo=TRUE ) ## ----install, eval = FALSE----------------------------------------------- # BiocManager::install('Spaniel') ## ---- load libraries----------------------------------------------------- library(Spaniel) library(Seurat) ## ----counts-------------------------------------------------------------- ### read in test data counts <- readRDS(file.path(system.file(package = "Spaniel"), "extdata/counts.rds")) ## ----colnames------------------------------------------------------------ colnames(counts)[1:10] ## ----rownames------------------------------------------------------------ rownames(counts)[1:10] ## ----barcodes------------------------------------------------------------ barcodesFile <- file.path(system.file(package = "Spaniel"), "1000L2_barcodes.txt") ## ----barcodesTop--------------------------------------------------------- barcodes <- read.csv(barcodesFile, sep = "\t", header = FALSE) head(barcodes) ## ----createSeurat-------------------------------------------------------- seuratObj <- createSeurat(counts, barcodesFile, projectName = "TestProj", sectionNumber = 1 ) ## ----createSeurat_meta--------------------------------------------------- head(seuratObj[[]]) ## ----createSeurat_counts------------------------------------------------- GetAssayData(seuratObj, "counts")[1:10, 1:5] ## ----createSeurat_project------------------------------------------------ Project(seuratObj) ## ----readSCE------------------------------------------------------------- sce <- createSCE(counts = counts, barcodeFile = barcodesFile, projectName = "TestProj", sectionNumber = 1) ## ----readSCE_ColData----------------------------------------------------- head(colData(sce)[1:5,1:5]) ## ----readSCE_counts------------------------------------------------------ counts(sce)[1:10, 1:5] ## ----readSCE_Project----------------------------------------------------- colData(sce)$project[1] ## ---- fig.show='hold'---------------------------------------------------- ### Load histological image into R imgFile <- file.path(system.file(package = "Spaniel"), "HE_Rep1_resized.jpg") image <- parseImage(imgFile) ## ---- qcplotting, results = "hide"-------------------------------------- minGenes <- 280 minUMI <- 67500 filter <- seuratObj$nCount_RNA > minUMI & seuratObj$nFeature_RNA > minGenes spanielPlot(object = seuratObj, grob = image, plotType = "NoGenes", showFilter = filter) ## ---- filter_seurat------------------------------------------------------ seuratFiltered <- subset(x = seuratObj, subset = nFeature_RNA > minGenes & nCount_RNA > minUMI) spanielPlot(object = seuratFiltered, grob = image, plotType = "NoGenes") ## ---- select_spots, eval = FALSE----------------------------------------- # selectSpots(seuratFiltered, image) # ## ---- remove_spots------------------------------------------------------- spotsToRemove <- file.path(system.file(package = "Spaniel"), "points_to_remove.txt") seuratFiltered <- removeSpots(seuratFiltered, pointsToRemove = spotsToRemove) spanielPlot(object = seuratFiltered, grob = image, plotType = "NoGenes") ## ---- find_clusters, message=FALSE, warning=FALSE, echo=TRUE, results = "hide"---- seuratFiltered <- NormalizeData(object = seuratFiltered, normalization.method = "LogNormalize", scale.factor = 10000) seuratFiltered <- FindVariableFeatures(object = seuratFiltered, selection.method = "vst", nfeatures = 2000) all.genes <- rownames(x = seuratFiltered) seuratFiltered <- ScaleData(object = seuratFiltered, features = all.genes) seuratFiltered <- RunPCA(object = seuratFiltered, features = VariableFeatures(object = seuratFiltered) ) seuratFiltered <- FindNeighbors(object = seuratFiltered, dims = 1:10) seuratFiltered <- FindClusters(object = seuratFiltered, resolution = c(0.4, 0.5, 0.6, 0.8)) ## ---- genePlot, results = "hide"----------------------------------------- gene = "Nrgn" spanielPlot(object = seuratFiltered, grob = image, plotType = "Gene", gene = gene) ## ---- clusterPlot, warning= FALSE, message = FALSE, results = "hide"----- spanielPlot(object = seuratFiltered, grob = image, plotType = "Cluster", clusterRes = "RNA_snn_res.0.8" ) ## ---- markClusters------------------------------------------------------- seuratFiltered <- markClusterCol(seuratFiltered, "res") ## ---- eval = FALSE, echo=TRUE------------------------------------------- # saveRDS(seuratFiltered, "data.rds") ## ---- eval = FALSE, echo=TRUE-------------------------------------------- # file.path(system.file(package = "Spaniel"), "extdata/SeuratData.rds" ) ## ---- eval = FALSE, echo=TRUE-------------------------------------------- # saveRDS(image, "image.rds") # ## ---- eval = FALSE------------------------------------------------------- # file.path(system.file(package = "Spaniel"), "extdata/image.rds" ) ## ---- markclusterCols, eval = FALSE-------------------------------------- # runShinySpaniel() # ## ---- eval = FALSE------------------------------------------------------- # spanielApp <- file.path(system.file(package = "Spaniel"), "ShinySpaniel" ) ## ---- eval = FALSE------------------------------------------------------- # library(rsconnect) # rsconnect::deployApp(spanielApp) #