Seurat

Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. For more information, please see here.

Available containers

Name	sha256	mpi	labels	descripion
Singularity.Seurat-4.1.1-foss-2021b-R-4.2.0.localimage.sif	fde39a80179020bb244c2c461c7055ecc8c2a9774d006279829a380111e57747	OpenMPI 4.1.2	pmix	benchmark

Running the container

Example invocation of Seurat benchmark

# Note: $PLCR is base path of container repository and $CONT is the container filename
export I_MPI_PMI_LIBRARY=$PLCR/local/pmi2/libpmi2.so

cd $TMPDIR

wget -q 'https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz'
tar -xzf pbmc3k_filtered_gene_bc_matrices.tar.gz
time srun --mpi=pmix --cpu-bind=cores singularity -s run -B $PWD:/host_pwd --pwd /host_pwd $PLCR/images/$CONT R --no-save << eor
library(dplyr)
library(Seurat)
library(patchwork)
pbmc.data <- Read10X(data.dir = "./filtered_gene_bc_matrices/hg19/")
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)
pbmc
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
top10 <- head(VariableFeatures(pbmc), 10)
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
pbmc <- JackStraw(pbmc, num.replicate = 100)
eor