Module Documentation


Satija Lab, NY Genome Center, wrapped as a module by Ted Liefeld, Mesirov Lab, UCSD School of Medicine.

Algorithm and scientific questions:

Module wrapping issues:  Ted Liefeld  < jliefeld at cloud dot ucsd dot edu>


Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements, and to integrate diverse types of single-cell data.

Within GenePattern, Seurat is accessed as several different modules exposing different portions of the Seurat workflow; Seurat.Preprocessing, Seurat.BatchCorrection, Seurat.QC and Seurat.Clustering. This module, Seurat.Clustering performs UMAP clustering and marker identification on single-cell RNA-Seq data.


The Seurat.Clustering module performs uses Seurat version 3.0.2. It performs following steps from Seurat to the input dataset;

  1. FindNeighbors
  2. FindClusters
  3. RunUMAP
  4. DimPlot
For details of the Seurat implementation please refer to the Seurat documentation from the Satija lab.


Input Files

  1. input seurat rds file*
    A RDS (from the R programming language) file containing a Seurat object.

Output Files

  1. <output filename>.rds
    Output RDS file containing a Seurat object that contains the cluster details for optional further processing.
  2. <output filename>_all_markers.csv
    Comma delimited file containing "p_val","avg_logFC","pct.1","pct.2","p_val_adj","cluster","gene" for the marker genes.
  3. <output_filename>.clustered.csv
    Comma delimited file containing "p_val","avg_logFC","pct.1","pct.2","p_val_adj","cluster","gene" for the identified gene clusters.
  4. <output_filename>.clustered.pdf
    PDF formatted file containing the UMAP plot of the clusters.

Example Data



GenePattern 3.9.11 or later (dockerized).


Name Description
input seurat rds file* A rds file containig a Seurat object.
output filename* The output filename prefix used for all output files.
maximum dimension The maximum number of clusters to attempt to find.
resolution* The resolution to use to find clusters.
reduction* The reduction to use (UMAP). Other reductions (e.g. TSNE) may be added later.
seed* The random number seed to use.

* - required