Changes between Version 20 and Version 21 of SOP/scRNA-seq

Timestamp:

08/25/20 15:30:21 (5 years ago)

Author:

ibarrasa

Comment:

--

SOP/scRNA-seq

@@ -45 +45 @@
 === Run quality control and filter cells === 
 
-We typically use the [https://satijalab.org/seurat/ Seurat] R package for these steps.
+We typically use the [https://satijalab.org/seurat/ Seurat] R package for these steps. The commands below are for Seurat 3.
 
     * Start out by loading the counts matrix from cellranger:
@@ -52 +52 @@
 message("Loaded Seurat version", packageDescription("Seurat")$Version)
 # Load the barcodes*, features*, and matrix* files in your 10x Genomics directory
-counts.all <- Read10X(data.dir = input.counts.filename)
-}}}
-
+counts.data <- Read10X(data.dir = input.counts.filename)
+}}}
+
+    * This is how you would load the counts from a file with gene counts: 
+{{{
+library("Seurat") 
+message("Loaded Seurat version", packageDescription("Seurat")$Version)
+counts.data <- read.table(file = paste0("./exp1_forSeurat.txt"))
+}}}
+
+    * Example of input data if starting with gene counts (only a few samples):  
+{{{
+E25-35_A4       E25-35_A5       E25-35_A6       E25-35_B4       E25-35_B5       E25-35_B6       E25-35_C4       E25-35_C5       E25-35_C6
+ENSMUSG00000064372_mt-Tp        7       10      7       10      3       17      13      4
+ENSMUSG00000064371_mt-Tt        0       0       0       1       0       5       0       0
+}}}
+    * Make the Seurat object and calculate the percentage of mitochondrial reads
+{{{
+seuratObject <- CreateSeuratObject(counts = counts.data,project = "ProjectName")
+seuratObject[["percent.mt"]] <- PercentageFeatureSet(object = seuratObject, pattern = "^mt-")
+}}}
+=== Filter cells with high % reads mapping to mitochondrial transcripts and with low number of genes detected === 
+These cutoffs are specific for each experiment
+{{{
+MIN.NUM.GENES = 200
+MAX.NUM.GENES = 8000 
+MAX.PERCENT.MITO = 20
+all_Filt <- subset(x = seuratObject, subset = nFeature_RNA > MIN.NUM.GENES & nFeature_RNA < MAX.NUM.GENES & percent.mt < MAX.PERCENT.MITO)
+}}}
+
+=== Normalize data === 
+{{{
+all_Filt <- NormalizeData(object = all_Filt, normalization.method = "LogNormalize", scale.factor = 10000)
+}}}
+
+=== Identify of highly variable features === 
+{{{
+num.variable.features.to.find = 2000
+all_Filt <- FindVariableFeatures(object = all_Filt, selection.method = "vst", nfeatures = num.variable.features.to.find)
+
+}}}
+=== Scale data === 
+{{{
+all.genes <- rownames(x = all_Filt)
+all_Filt <- ScaleData(object = all_Filt, features = all.genes)
+}}}
 
 === Perform and visualize dimensional analysis === 
-
+{{{
+all_Filt <- RunPCA(object = all_Filt, features = VariableFeatures(object = all_Filt))
+}}}
 === Partition cells into clusters ===