Run Space Ranger tools using spaceranger_workflow

spaceranger_workflow wraps Space Ranger to process spatial transcriptomics data.

A general step-by-step instruction

This section mainly considers jobs starting from BCL files. If your job starts with FASTQ files, and only need to run spaceranger count part, please refer to this subsection.

1. Import spaceranger_workflow

Import spaceranger_workflow workflow to your workspace.

See the Terra documentation for adding a workflow. The spaceranger_workflow workflow is under Broad Methods Repository with name “cumulus/spaceranger_workflow”.

Moreover, in the workflow page, click the Export to Workspace... button, and select the workspace to which you want to export spaceranger_workflow workflow in the drop-down menu.

2. Upload sequencing and image data to Google bucket

Copy your sequencing output to your workspace bucket using gsutil (you already have it if you’ve installed Google cloud SDK) in your unix terminal.

You can obtain your bucket URL in the dashboard tab of your Terra workspace under the information panel.


Use gsutil cp [OPTION]... src_url dst_url to copy data to your workspace bucket. For example, the following command copies the directory at /foo/bar/nextseq/Data/VK18WBC6Z4 to a Google bucket:

gsutil -m cp -r /foo/bar/nextseq/Data/VK18WBC6Z4 gs://fc-e0000000-0000-0000-0000-000000000000/VK18WBC6Z4

-m means copy in parallel, -r means copy the directory recursively, and gs://fc-e0000000-0000-0000-0000-000000000000 should be replaced by your own workspace Google bucket URL.

Similarly, copy all images for spatial data to the same google bucket.


If input is a folder of BCL files, users do not need to upload the whole folder to the Google bucket. Instead, they only need to upload the following files:


If data are generated using MiSeq or NextSeq, the location files are inside lane subfloders L001 under Data/Intensities/. In addition, if users’ data only come from a subset of lanes (e.g. L001 and L002), users only need to upload lane subfolders from the subset (e.g. Data/Intensities/BaseCalls/L001, Data/Intensities/BaseCalls/L002 and Data/Intensities/L001, Data/Intensities/L002 if sequencer is MiSeq or NextSeq).

Alternatively, users can submit jobs through command line interface (CLI) using altocumulus, which will smartly upload BCL folders according to the above rules.


Broad users need to be on an UGER node (not a login node) in order to use the -m flag

Request an UGER node:

reuse UGER
qrsh -q interactive -l h_vmem=4g -pe smp 8 -binding linear:8 -P regevlab

The above command requests an interactive node with 4G memory per thread and 8 threads. Feel free to change the memory, thread, and project parameters.

Once you’re connected to an UGER node, you can make gsutil available by running:

reuse Google-Cloud-SDK

3. Prepare a sample sheet

3.1 Sample sheet format:

Please note that the columns in the CSV can be in any order, but that the column names must match the recognized headings.

The sample sheet describes how to demultiplex flowcells and generate channel-specific count matrices. Note that Sample, Lane, and Index columns are defined exactly the same as in 10x’s simple CSV layout file.

A brief description of the sample sheet format is listed below (required column headers are shown in bold).

Column Description
Sample Contains sample names. Each 10x channel should have a unique sample name.
Provides the reference genome used by Space Ranger for each 10x channel.
The elements in the reference column can be either Google bucket URLs to reference tarballs or keywords such as GRCh38-2020-A.
A full list of available keywords is included in each of the following data type sections (e.g. sc/snRNA-seq) below.
Indicates the Google bucket URLs of uploaded BCL folders.
If starts with FASTQ files, this should be Google bucket URLs of uploaded FASTQ folders.
The FASTQ folders should contain one subfolder for each sample in the flowcell with the sample name as the subfolder name.
Each subfolder contains FASTQ files for that sample.
Tells which lanes the sample was pooled into.
Can be either single lane (e.g. 8) or a range (e.g. 7-8) or all (e.g. *).
Index Sample index (e.g. SI-GA-A12).
Image Google bucket url for a brightfield tissue H&E image in .jpg or .tiff format. This column is mutually exclusive with DarkImage and ColorizedImage columns.
DarkImage Google bucket urls for Multi-channel, dark-background fluorescence image as either a single, multi-layer .tiff file, multiple .tiff or .jpg files, or a pre-combined color .tiff or .jpg file. If multiple files are provided, please separate them by ‘;’. This column is mutually exclusive with Image and ColorizedImage columns.
ColorizedImage Google bucket url for a color composite of one or more fluorescence image channels saved as a single-page, single-file color .tiff or .jpg. This column is mutually exclusive with Image and DarkImage columns.
Slide Visium slide serial number. If both Slide and Area are empty, the –unknown-slide option would be set.
Area Visium capture area identifier. Options for Visium are A1, B1, C1, D1. If both Slide and Area are empty, the –unknown-slide option would be set.
SlideFile Slide layout file indicating capture spot and fiducial spot positions. Only required if internet access is not available.
ReorientImages Use with automatic image alignment to specify that images may not be in canonical orientation with the hourglass in the top left corner of the image. The automatic fiducial alignment will attempt to align any rotation or mirroring of the image.
LoupeAlignment Alignment file produced by the manual Loupe alignment step. Image column must be supplied in this case.
TargetPanel Google bucket url for a target panel CSV for targeted gene expression analysis.

The sample sheet supports sequencing the same 10x channels across multiple flowcells. If a sample is sequenced across multiple flowcells, simply list it in multiple rows, with one flowcell per row. In the following example, we have 2 samples sequenced in two flowcells.



3.2 Upload your sample sheet to the workspace bucket:


gsutil cp /foo/bar/projects/sample_sheet.csv gs://fc-e0000000-0000-0000-0000-000000000000/

4. Launch analysis

In your workspace, open spaceranger_workflow in WORKFLOWS tab. Select the desired snapshot version (e.g. latest). Select Run workflow with inputs defined by file paths as below


and click SAVE button. Select Use call caching and click INPUTS. Then fill in appropriate values in the Attribute column. Alternative, you can upload a JSON file to configure input by clicking Drag or click to upload json.

Once INPUTS are appropriated filled, click RUN ANALYSIS and then click LAUNCH.

5. Notice: run spaceranger mkfastq if you are non Broad Institute users

Non Broad Institute users that wish to run spaceranger mkfastq must create a custom docker image that contains bcl2fastq.

See bcl2fastq instructions.

6. Run spaceranger count only

Sometimes, users might want to perform demultiplexing locally and only run the count part on the cloud. This section describes how to only run the count part via spaceranger_workflow.

  1. Copy your FASTQ files to the workspace using gsutil in your unix terminal.

    You should upload folders of FASTQ files. The uploaded folder (for one flowcell) should contain one subfolder for each sample belong to the this flowcell. In addition, the subfolder name and the sample name in your sample sheet MUST be the same. Each subfolder contains FASTQ files for that sample. Please note that if your FASTQ file are downloaded from the Sequence Read Archive (SRA) from NCBI, you must rename your FASTQs to follow the bcl2fastq file naming conventions.


    gsutil -m cp -r /foo/bar/fastq_path/K18WBC6Z4 gs://fc-e0000000-0000-0000-0000-000000000000/K18WBC6Z4_fastq
  2. Create a sample sheet following the similar structure as above, except the following differences:

    • Flowcell column should list Google bucket URLs of the FASTQ folders for flowcells.
    • Lane and Index columns are NOT required in this case.


  3. Set optional input run_mkfastq to false.

Visium spatial transcriptomics data

To process spatial transcriptomics data, follow the specific instructions below.

Sample sheet

  1. Reference column.

    Pre-built scRNA-seq references are summarized below.

    Keyword Description
    GRCh38-2020-A Human GRCh38 (GENCODE v32/Ensembl 98)
    mm10-2020-A Mouse mm10 (GENCODE vM23/Ensembl 98)
    GRCh38_and_mm10-2020-A Human GRCh38 (GENCODE v32/Ensembl 98) and mouse mm10 (GENCODE vM23/Ensembl 98)
    GRCh38_v3.0.0 Human GRCh38, spaceranger reference 3.0.0, Ensembl v93 gene annotation
    hg19_v3.0.0 Human hg19, cellranger reference 3.0.0, Ensembl v87 gene annotation
    mm10_v3.0.0 Mouse mm10, cellranger reference 3.0.0, Ensembl v93 gene annotation
    GRCh38_and_mm10_v3.1.0 Human (GRCh38) and mouse (mm10), cellranger references 3.1.0, Ensembl v93 gene annotations for both human and mouse
    hg19_and_mm10_v3.0.0 Human (hg19) and mouse (mm10), cellranger reference 3.0.0, Ensembl v93 gene annotations for both human and mouse
    GRCh38_v1.2.0 or GRCh38 Human GRCh38, cellranger reference 1.2.0, Ensembl v84 gene annotation
    hg19_v1.2.0 or hg19 Human hg19, cellranger reference 1.2.0, Ensembl v82 gene annotation
    mm10_v1.2.0 or mm10 Mouse mm10, cellranger reference 1.2.0, Ensembl v84 gene annotation
    GRCh38_and_mm10_v1.2.0 or GRCh38_and_mm10 Human and mouse, built from GRCh38 and mm10 cellranger references, Ensembl v84 gene annotations are used

    Pre-built snRNA-seq references are summarized below.

    Keyword Description
    GRCh38_premrna_v3.0.0 Human, introns included, built from GRCh38 cellranger reference 3.0.0, Ensembl v93 gene annotation, treating annotated transcripts as exons
    GRCh38_premrna_v1.2.0 or GRCh38_premrna Human, introns included, built from GRCh38 cellranger reference 1.2.0, Ensembl v84 gene annotation, treating annotated transcripts as exons
    mm10_premrna_v1.2.0 or mm10_premrna Mouse, introns included, built from mm10 cellranger reference 1.2.0, Ensembl v84 gene annotation, treating annotated transcripts as exons
    GRCh38_premrna_and_mm10_premrna_v1.2.0 or GRCh38_premrna_and_mm10_premrna Human and mouse, introns included, built from GRCh38_premrna_v1.2.0 and mm10_premrna_v1.2.0

Workflow input

For spatial data, spaceranger_workflow takes Illumina outputs and related images as input and runs spaceranger mkfastq and spaceranger count. Revalant workflow inputs are described below, with required inputs highlighted in bold.

Name Description Example Default
input_csv_file Sample Sheet (contains Sample, Reference, Flowcell, Lane, Index as required and Image, DarkImage, ColorizedImage, Slide, Area, SlideFile, ReorientImages, LoupeAlignment, TargetPanel as optional) “gs://fc-e0000000-0000-0000-0000-000000000000/sample_sheet.csv”  
output_directory Output directory “gs://fc-e0000000-0000-0000-0000-000000000000/spaceranger_output” Results are written under directory output_directory and will overwrite any existing files at this location.
run_mkfastq If you want to run spaceranger mkfastq true true
run_count If you want to run spaceranger count true true
delete_input_bcl_directory If delete BCL directories after demux. If false, you should delete this folder yourself so as to not incur storage charges false false
mkfastq_barcode_mismatches Number of mismatches allowed in matching barcode indices (bcl2fastq2 default is 1) 0  
no_bam Turn this option on to disable BAM file generation. false false
secondary Perform Space Ranger secondary analysis (dimensionality reduction, clustering, etc.) false false
spaceranger_version spaceranger version, could be 1.3.0 “1.3.0” “1.3.0”
config_version config docker version used for processing sample sheets, could be 0.2, 0.1 “0.2” “0.2”

Docker registry to use for spaceranger_workflow. Options:

  • “” for images on Red Hat registry;
  • “cumulusprod” for backup images on Docker Hub.
“” “”
spaceranger_mkfastq_docker_registry Docker registry to use for spaceranger mkfastq. Default is the registry to which only Broad users have access. See bcl2fastq for making your own registry. “” “”
zones Google cloud zones “us-central1-a us-west1-a” “us-central1-a us-central1-b us-central1-c us-central1-f us-east1-b us-east1-c us-east1-d us-west1-a us-west1-b us-west1-c”
num_cpu Number of cpus to request for one node for spaceranger mkfastq and spaceranger count 32 32
memory Memory size string for spaceranger mkfastq and spaceranger count “120G” “120G”
mkfastq_disk_space Optional disk space in GB for mkfastq 1500 1500
count_disk_space Disk space in GB needed for spaceranger count 500 500

Cloud infrastructure backend to use. Available options:

  • “gcp” for Google Cloud;
  • “aws” for Amazon AWS;
  • “local” for local machine.
“gcp” “gcp”
preemptible Number of preemptible tries. This works only when backend is gcp. 2 2
awsMaxRetries Number of maximum retries when running on AWS. This works only when backend is aws. 5 5

Workflow output

See the table below for important sc/snRNA-seq outputs.

Name Type Description
output_fastqs_directory Array[String] A list of google bucket urls containing FASTQ files, one url per flowcell.
output_count_directory Array[String] A list of google bucket urls containing count matrices, one url per sample.
metrics_summaries File A excel spreadsheet containing QCs for each sample.
output_web_summary Array[File] A list of htmls visualizing QCs for each sample (spaceranger count output).

Build Space Ranger References

Reference built by Cell Ranger for sc/snRNA-seq should be compatible with Space Ranger. For more details on building references uing Cell Ranger, please refer to here.