# HG changeset patch # User iuc # Date 1576741376 18000 # Node ID 321bdd834266d1f4539203e9c944df87bcb9e8ff # Parent 7319f83ae7345a5617397700e25f6c258d0ed437 "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/seurat commit 3cf715ec11e2c9944f46572e324e5b2db5aa151f" diff -r 7319f83ae734 -r 321bdd834266 Seurat.R --- a/Seurat.R Mon Dec 09 14:32:16 2019 -0500 +++ b/Seurat.R Thu Dec 19 02:42:56 2019 -0500 @@ -23,7 +23,7 @@ #' --- #+ echo=F, warning = F, message=F -options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } ) +options(show.error.messages = F, error = function(){cat(geterrmessage(), file = stderr()); q("no", 1, F)}) showcode <- as.logical(params$showcode) warn <- as.logical(params$warn) varstate <- as.logical(params$varstate) @@ -33,7 +33,7 @@ tsne <- as.logical(params$tsne) heatmaps <- as.logical(params$heatmaps) -# we need that to not crash galaxy with an UTF8 error on German LC settings. +# we need that to not crash Galaxy with an UTF-8 error on German LC settings. loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8") @@ -57,52 +57,52 @@ print(paste0("Logfold change threshold", logfc_threshold)) #+ echo = FALSE -if(showcode == TRUE){print("Read in data, generate inital Seurat object")} +if(showcode == TRUE) print("Read in data, generate inital Seurat object") #+ echo = `showcode`, warning = `warn`, message = F -counts <- read.delim(params$counts, row.names=1) +counts <- read.delim(params$counts, row.names = 1) seuset <- Seurat::CreateSeuratObject(counts = counts, min.cells = min_cells, min.features = min_genes) #+ echo = FALSE -if(showcode == TRUE && vlnfeat == TRUE){print("Raw data vizualization")} +if(showcode == TRUE && vlnfeat == TRUE) print("Raw data vizualization") #+ echo = `showcode`, warning = `warn`, include=`vlnfeat` -Seurat::VlnPlot(object = seuset, features = c("nFeature_RNA", "nCount_RNA"), axis="v") +Seurat::VlnPlot(object = seuset, features = c("nFeature_RNA", "nCount_RNA")) Seurat::FeatureScatter(object = seuset, feature1 = "nCount_RNA", feature2 = "nFeature_RNA") #+ echo = FALSE -if(showcode == TRUE){print("Filter and normalize for UMI counts")} +if(showcode == TRUE) print("Filter and normalize for UMI counts") #+ echo = `showcode`, warning = `warn` seuset <- subset(seuset, subset = `nCount_RNA` > low_thresholds & `nCount_RNA` < high_thresholds) -seuset <- Seurat::NormalizeData(seuset, normalizeation.method = "LogNormalize", scale.factor = 10000) +seuset <- Seurat::NormalizeData(seuset, normalization.method = "LogNormalize", scale.factor = 10000) #+ echo = FALSE -if(showcode == TRUE && featplot == TRUE){print("Variable Genes")} +if(showcode == TRUE && featplot == TRUE) print("Variable Genes") #+ echo = `showcode`, warning = `warn`, include = `featplot` seuset <- Seurat::FindVariableFeatures(object = seuset, selection.method = "mvp") Seurat::VariableFeaturePlot(seuset, cols = c("black", "red"), selection.method = "disp") seuset <- Seurat::ScaleData(object = seuset, vars.to.regress = "nCount_RNA") #+ echo = FALSE -if(showcode == TRUE && PCplots == TRUE){print("PCA Visualization")} +if(showcode == TRUE && PCplots == TRUE) print("PCA Visualization") #+ echo = `showcode`, warning = `warn`, include = `PCplots` -seuset <- Seurat::RunPCA(seuset, npcs=numPCs) +seuset <- Seurat::RunPCA(seuset, npcs = numPCs) Seurat::VizDimLoadings(seuset, dims = 1:2) -Seurat::DimPlot(seuset, dims = c(1,2), reduction="pca") -Seurat::DimHeatmap(seuset, dims=1:numPCs, nfeatures=30, reduction="pca") +Seurat::DimPlot(seuset, dims = c(1,2), reduction = "pca") +Seurat::DimHeatmap(seuset, dims = 1:numPCs, nfeatures = 30, reduction = "pca") seuset <- Seurat::JackStraw(seuset, dims=numPCs, reduction = "pca", num.replicate = 100) seuset <- Seurat::ScoreJackStraw(seuset, dims = 1:numPCs) Seurat::JackStrawPlot(seuset, dims = 1:numPCs) Seurat::ElbowPlot(seuset, ndims = numPCs, reduction = "pca") #+ echo = FALSE -if(showcode == TRUE && tsne == TRUE){print("tSNE")} +if(showcode == TRUE && tsne == TRUE) print("tSNE") #+ echo = `showcode`, warning = `warn`, include = `tsne` seuset <- Seurat::FindNeighbors(object = seuset) seuset <- Seurat::FindClusters(object = seuset) seuset <- Seurat::RunTSNE(seuset, dims = 1:numPCs, resolution = resolution) -Seurat::DimPlot(seuset, reduction="tsne") +Seurat::DimPlot(seuset, reduction = "tsne") #+ echo = FALSE -if(showcode == TRUE && heatmaps == TRUE){print("Marker Genes")} +if(showcode == TRUE && heatmaps == TRUE) print("Marker Genes") #+ echo = `showcode`, warning = `warn`, include = `heatmaps` markers <- Seurat::FindAllMarkers(seuset, only.pos = TRUE, min.pct = min_pct, logfc.threshold = logfc_threshold) top10 <- dplyr::group_by(markers, cluster) diff -r 7319f83ae734 -r 321bdd834266 seurat.xml --- a/seurat.xml Mon Dec 09 14:32:16 2019 -0500 +++ b/seurat.xml Thu Dec 19 02:42:56 2019 -0500 @@ -1,8 +1,13 @@ - + - toolkit for exploration of single-cell RNA-seq data + + 3.1.2 + - r-seurat - r-rmarkdown + r-seurat + r-rmarkdown + + pandoc - + - - + Seurat Analysis @@ -332,6 +331,7 @@ border: none; display: inline-block; border-radius: 4px; + background-color: transparent; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li { @@ -365,17 +365,17 @@

Seurat Analysis

Performed using Galaxy

-

2019-12-08

+

2019-12-16

## [1] "Read in data, generate inital Seurat object"
-
counts <- read.delim(params$counts, row.names=1)
+
counts <- read.delim(params$counts, row.names = 1)
 seuset <- Seurat::CreateSeuratObject(counts = counts, min.cells = min_cells, min.features = min_genes)

 
## [1] "Filter and normalize for UMI counts"

 
seuset <- subset(seuset, subset = `nCount_RNA` > low_thresholds & `nCount_RNA` < high_thresholds)
-seuset <- Seurat::NormalizeData(seuset, normalizeation.method = "LogNormalize", scale.factor = 10000)

+seuset <- Seurat::NormalizeData(seuset, normalization.method = "LogNormalize", scale.factor = 10000)
## [1] "Variable Genes"
seuset <- Seurat::FindVariableFeatures(object = seuset, selection.method = "mvp")
 Seurat::VariableFeaturePlot(seuset, cols = c("black", "red"), selection.method = "disp")