10x Visium

Run Space Ranger tools using spaceranger_workflow

spaceranger_workflow wraps Space Ranger to process 10x Visium data.

Note

Space Ranger will send anonymized telemetry data to 10x Genomics starting from v4.0. Here is the details on Space Ranger Pipeline Telemetry.

This option has been turned off in this spaceranger_workflow, thus no data will be sent to 10x Genomics.

A Step-by-step instruction

The workflow starts with FASTQ files.

Note

Starting from v3.0.0, Cumulus spaceranger_workflow drops support for mkfastq. If your data start from BCL files, please first run BCL Convert to demultiplex folwcells to generate FASTQ files.

1. Import spaceranger_workflow

Import spaceranger_workflow workflow to your workspace by following instructions in Import workflows to Terra. You should choose workflow github.com/lilab-bcb/cumulus/Spaceranger to import.

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 FASTQ files to your workspace bucket using gcloud storage (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.

_images/google_bucket_link.png

There are two cases:

  • Case 1: All the FASTQ files are in one top-level folder. Then you can simply upload this folder to Cloud, and in your sample sheet, make sure Sample names are consistent with the filename prefix of their corresponding FASTQ files.

  • Case 2: In the top-level folder, each sample has a dedicated subfolder containing its FASTQ files. In this case, you need to upload the whole top-level folder, and in your sample sheet, make sure Sample names and their corresponding subfolder names are identical.

Notice that if your FASTQ files are downloaded from the Sequence Read Archive (SRA) from NCBI, you must rename your FASTQs to follow the Illumina file naming conventions.

Example:

gcloud storage cp -r /foo/bar/fastq_path/K18WBC6Z4/Fastq gs://fc-e0000000-0000-0000-0000-000000000000/K18WBC6Z4_fastq

where -r means copy the directory recursively, and fc-e0000000-0000-0000-0000-000000000000 should be replaced by your own workspace Google bucket name.

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

Alternatively, users can submit jobs through command line interface (CLI) using altocumulus, which will smartly upload FASTQ files to cloud.

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.

For FFPE data, ProbeSet column is mandatory.

The sample sheet describes how to generate gene-count matrices from sequencing reads.

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.

Reference

Provides the reference genome used by Space Ranger for each 10x channel. The elements in the reference column can be either keywords of pre-built references (see the list in this section below) or Google bucket URLs to reference tarballs.

Flowcell

Indicates the cloud URI of the uploaded folder containing FASTQ files for each sample.

ProbeSet
Probeset for FFPE samples. This can be a 10x official probeset selected from the table in this section below, or a URI to a custom probeset.
Notice: For non-FFPE samples, such as Visium Fresh Frozen, Visium HD 3’, etc., set ProbeSet to "".

Image

Cloud 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

Cloud 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

Cloud 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.

CytaImage

Cloud bucket url for a brightfield image generated by the CytAssist instrument.

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.

LoupeAlignment

Alignment file produced by the manual Loupe alignment step.

Pre-built scRNA-seq references are summarized below:

Keyword

Description

GRCh38-2024-A

Human GRCh38 (GENCODE v44/Ensembl 110). Notice: This reference is only supported by Space Ranger v4.0.1+.

GRCm39-2024-A

Mouse GRCm39 (GENCODE vM33/Ensembl 110). Notice: This reference is only supported by Space Ranger v4.0.1+.

GRCh38-2020-A

Human GRCh38 (GENCODE v32/Ensembl 98)

mm10-2020-A

Mouse mm10 (GENCODE vM23/Ensembl 98)

The list of available Visium probe sets is below:

Probe Set

Genome Reference

Compatible Assay

Space Ranger version

human_probe_v2.1

GRCh38-2024-A

Visium HD, Visium CytAssist

v4.0+

human_probe_v2

GRCh38-2020-A

Visium HD, Visium CytAssist

v3.0+

human_probe_v1

GRCh38-2020-A

Visium CytAssist

v2.0+

mouse_probe_v2.1

GRCm39-2024-A

Visium HD, Visium CytAssist

v4.0+

mouse_probe_v2

mm10-2020-A

Visium HD, Visium CytAssist

v3.0+

mouse_probe_v1

mm10-2020-A

Visium CytAssist

v2.0+

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.

Example:

Sample,Reference,ProbeSet,Flowcell,Image,Slide,Area
sample_1,GRCh38-2024-A,human_probe_v2.1,gs://fc-e0000000-0000-0000-0000-000000000000/VK18WBC6Z4/Fastq,gs://image/image1.tif,V19J25-123,A1
sample_2,GRCh38-2020-A,,gs://fc-e0000000-0000-0000-0000-000000000000/VK18WBC6Z4/Fastq,gs://image/image2.tif,V19J25-123,B1
sample_1,GRCh38-2024-A,human_probe_v2.1,gs://fc-e0000000-0000-0000-0000-000000000000/VK10WBC9Z2/Fastq,gs://image/image1.tif,V19J25-123,A1
sample_2,GRCh38-2020-A,,gs://fc-e0000000-0000-0000-0000-000000000000/VK10WBC9Z2/Fastq,gs://image/image2.tif,V19J25-123,B1

3.2 Upload your sample sheet to the workspace bucket:

Example:

gcloud storage 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

_images/single_workflow.png

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.

Visium spatial transcriptomics data

Workflow input

For spatial data, spaceranger_workflow takes sequencing reads as input (FASTQ files, or TAR files containing FASTQ files), and runs 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 ProbeSet, Image, DarkImage, ColorizedImage, CytaImage, Slide, Area, SlideFile, LoupeAlignment 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.

reorient_images

For use with automatic fiducial alignment. This option will apply to all samples in the sample sheet. Spaceranger will attempt to find the best alignment of the fiducial markers by any rotation or mirroring of the image.

true

true

filter_probes

Whether to filter the probe set using the “included” column of the probe set CSV.

true

true

dapi_index

Index of DAPI channel (1-indexed) of fluorescence image, only used in the CytaAssist case, with dark background image.

2

unknown_slide

Use this option if the slide serial number and area identifier have been lost. Choose from visium-1, visium-2 and visium-2-large.

visium-2

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

r1_length

Hard trim the input Read 1 to this length before analysis

28

r2_length

Hard trim the input Read 1 to this length before analysis. This value will be set to 50 automatically for FFPE samples if spaceranger version < 2.0.0.

50

spaceranger_version

spaceranger version, could be: 4.1.0, 4.0.1, 3.1.3, 3.0.1

“4.1.0”

“4.1.0”

docker_registry

Docker registry to use for spaceranger_workflow. Options:

  • “quay.io/cumulus” for images on Red Hat registry;

  • “cumulusprod” for backup images on Docker Hub.

“quay.io/cumulus”

“quay.io/cumulus”

acronym_file
The link/path of an index file in TSV format for fetching preset genome references, Visium probe sets, etc. by their names.
Set an GS URI if running on GCP; an S3 URI for AWS; an absolute file path for HPC or local machines.

“s3://xxxx/index.tsv”

“gs://cumulus-ref/resources/cellranger/index.tsv”

zones

Google cloud zones. For GCP Batch backend, the zones are automatically restricted by the Batch settings.

“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 count

32

32

memory

Memory size string for spaceranger count

“120G”

“120G”

count_disk_space

Disk space in GB needed for spaceranger count

500

500

preemptible

Number of preemptible tries. Only works for GCP

2

2

awsQueueArn

The AWS ARN string of the job queue to be used. Only works for AWS

“arn:aws:batch:us-east-1:xxx:job-queue/priority-gwf”

“”

Workflow output

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

Name

Type

Description

count_outputs

Array[String]

A list of cloud urls containing spaceranger count outputs, one url per sample.

metrics_summaries

File

A excel spreadsheet containing QCs for each sample.

spaceranger_count.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.