reorg singlecell

inst/templates/singlecell/README.md

@@ -1,6 +1,10 @@
-# Tipical steps for scRNAseq downstream analysis
+# Project name
 
-# DropBox
+## Start with cell-ranger
+
+`pre-process-w-cellranger.md` contains step by step guidelines on how to run cellranger in O2 and load data into R. This `scripts/seurat_init.R` script contains all the pieces to go from cellranger output to Seurat obj. It is assuming a mouse genome.
+
+## DropBox
 
 -   In `reports/QC`
     -   [ ] copy QC `Rmd/R/html/figures`
@@ -10,7 +14,7 @@
 -   In `reports/DE`, for *each analysis*:
     -   TBD
 
-# GitHub
+## GitHub
 
 -   [ ] Push all `*Rmd` `*R` files used for the *QC* and *DE* analysis respecting folder structure.
 

inst/templates/singlecell/starting_steps.md → inst/templates/singlecell/pre-process-w-cellranger.md

@@ -1,3 +1,4 @@
+# Tipical steps for scRNAseq downstream analysis
 ---
 title: "From raw data to Seurat"
 ---
@@ -272,83 +273,3 @@ seurat_merge[["RNA"]] <- JoinLayers(seurat_merge[["RNA"]])
 saveRDS(seurat_merge, file = "seurat_pre-filtered.rds")
 write.csv([email protected], file = "metadata_pre-filtered.csv")
 ```
-
-## Full script
-
-Below find all pieces to copy and paste. We are assuming a mouse genome.
-
-```
-
-library(Seurat)
-library(data.table)
-library(hdf5r)
-
-### Set up run information
-data_dir <- "/path/to/cellranger/output/folders/"
-
-samples <- c("sample1",  "sample2",  "sample3")
-
-### Make individual seurat objects for each sample
-
-for (i in 1:length(samples)){
-  seurat_data <- Read10X_h5(paste(c(data_dir,samples[i],"/outs/raw_feature_bc_matrix.h5"),sep="",collapse = ""))         
-  seurat_obj <- CreateSeuratObject(counts = seurat_data, 
-                                   min.features = 100,  ## only keep cells with at least 100 genes
-                                   project = samples[i]) 
-  assign(paste0(samples[i], "_seurat"), 
-         seurat_obj) # stores Seurat object in variable of corresponding sample name
-}
-
-### Merge all seurat objects
-
-seurat_ID <- paste0(samples, "_seurat") # get names of all objects
-
-
-u <- get(seurat_ID[2])
-for (i in 3:length(seurat_ID)) {
-  u <- c(u, get(seurat_ID[i]))
-} ## makes a list of all seurat objects
-
-seurat_merge <- merge(x = get(seurat_ID[1]), 
-                      y = u,
-                      add.cell.ids = all_samples, 
-                      project = "my_scRNA_project")
-
-
-# Mitochondrial genes for mouse genome
-idx <- grep("^mt-", rownames(GetAssay(seurat_merge, "RNA")))
-rownames(GetAssay(seurat_merge, "RNA"))[idx]
-# Mitochondrial genes vs. nuclear genes ratio
-seurat_merge$mitoRatio <- PercentageFeatureSet(object = seurat_merge, pattern = "^mt-")
-seurat_merge$mitoRatio <- [email protected]$mitoRatio/100 # Divide by 100 for Ratio instead of Percentage
-
-# Number of genes per UMI for each cell
-seurat_merge$Log10GenesPerUMI <- log10(seurat_merge$nFeature_RNA) / log10(seurat_merge$nCount_RNA)
-
-# Import experimental metadata
-metaexp <- read.csv("/path/to/experimental/metadata/meta.csv")
-
-# Check matching of IDs
-all(metaexp$sample %in% metadata$orig.ident)
-all(metadata$orig.ident %in% metaexp$sample)
-
-#change headings to match
-colnames(metaexp)[1] <- "orig.ident"
-
-metafull <- plyr::join(metadata, metaexp,
-                       by = c("orig.ident"))
-
-# Replace seurat object metadata
-if(all(metafull$barcode == rownames([email protected]))) {
-  rownames(metafull) <- metafull$barcode
-  [email protected] <- metafull
-}
-
-
-## Join layers (each sample is a separate layer)
-seurat_merge[["RNA"]] <- JoinLayers(seurat_merge[["RNA"]])
-
-### Save Seurat object for future processing
-saveRDS(seurat_merge, file = "seurat_pre-filtered.rds")
-write.csv([email protected], file = "metadata_pre-filtered.csv")
-```

inst/templates/singlecell/scripts/seurat_init.R

@@ -0,0 +1,73 @@
+
+library(Seurat)
+library(data.table)
+library(hdf5r)
+
+### Set up run information
+data_dir <- "/path/to/cellranger/output/folders/"
+
+samples <- c("sample1",  "sample2",  "sample3")
+
+### Make individual seurat objects for each sample
+
+for (i in 1:length(samples)){
+  seurat_data <- Read10X_h5(paste(c(data_dir,samples[i],"/outs/raw_feature_bc_matrix.h5"),sep="",collapse = ""))
+  seurat_obj <- CreateSeuratObject(counts = seurat_data,
+                                   min.features = 100,  ## only keep cells with at least 100 genes
+                                   project = samples[i])
+  assign(paste0(samples[i], "_seurat"),
+         seurat_obj) # stores Seurat object in variable of corresponding sample name
+}
+
+### Merge all seurat objects
+
+seurat_ID <- paste0(samples, "_seurat") # get names of all objects
+
+
+u <- get(seurat_ID[2])
+for (i in 3:length(seurat_ID)) {
+  u <- c(u, get(seurat_ID[i]))
+} ## makes a list of all seurat objects
+
+seurat_merge <- merge(x = get(seurat_ID[1]),
+                      y = u,
+                      add.cell.ids = all_samples,
+                      project = "my_scRNA_project")
+
+
+# Mitochondrial genes for mouse genome
+idx <- grep("^mt-", rownames(GetAssay(seurat_merge, "RNA")))
+rownames(GetAssay(seurat_merge, "RNA"))[idx]
+# Mitochondrial genes vs. nuclear genes ratio
+seurat_merge$mitoRatio <- PercentageFeatureSet(object = seurat_merge, pattern = "^mt-")
+seurat_merge$mitoRatio <- seurat_merge@meta.data$mitoRatio/100 # Divide by 100 for Ratio instead of Percentage
+
+# Number of genes per UMI for each cell
+seurat_merge$Log10GenesPerUMI <- log10(seurat_merge$nFeature_RNA) / log10(seurat_merge$nCount_RNA)
+
+# Import experimental metadata
+metaexp <- read.csv("/path/to/experimental/metadata/meta.csv")
+
+# Check matching of IDs
+all(metaexp$sample %in% metadata$orig.ident)
+all(metadata$orig.ident %in% metaexp$sample)
+
+#change headings to match
+colnames(metaexp)[1] <- "orig.ident"
+
+metafull <- plyr::join(metadata, metaexp,
+                       by = c("orig.ident"))
+
+# Replace seurat object metadata
+if(all(metafull$barcode == rownames(seurat_merge@meta.data))) {
+  rownames(metafull) <- metafull$barcode
+  seurat_merge@meta.data <- metafull
+}
+
+
+## Join layers (each sample is a separate layer)
+seurat_merge[["RNA"]] <- JoinLayers(seurat_merge[["RNA"]])
+
+### Save Seurat object for future processing
+saveRDS(seurat_merge, file = "seurat_pre-filtered.rds")
+write.csv(seurat_merge@meta.data, file = "metadata_pre-filtered.csv")