Run Space Ranger tools using spaceranger_workflow¶
spaceranger_workflow wraps Space Ranger to process 10x Visium 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.
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 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.
gsutil cp [OPTION]... src_url dst_urlto 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
-mmeans copy in parallel,
-rmeans copy the directory recursively, and
gs://fc-e0000000-0000-0000-0000-000000000000should 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:
RunInfo.xml RTAComplete.txt runParameters.xml Data/Intensities/s.locs Data/Intensities/BaseCalls
If data are generated using MiSeq or NextSeq, the location files are inside lane subfloders
Data/Intensities/. In addition, if users’ data only come from a subset of lanes (e.g.
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.
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 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. Reference 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. Flowcell 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. Lane 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). ProbeSet Probe set for FFPE samples. Choosing from
human_probe_v1(10x human probe set, CytoAssist-incompatible),
human_probe_v2(10x human probe set, CytoAssist-compatible) and
mouse_probe_v1(10x mouse probe set). Alternatively, a CSV file describing the probe set can be directly used. Setting ProbeSet to
""for a sample implies the sample is not FFPE.
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. TargetPanel Cloud 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.
Example:Sample,Reference,Flowcell,Lane,Index,Image,Slide,Area sample_1,GRCh38-2020-A,gs://fc-e0000000-0000-0000-0000-000000000000/VK18WBC6Z4,1-2,SI-GA-A8,gs://image/image1.tif,V19J25-123,A1 sample_2,GRCh38-2020-A,gs://fc-e0000000-0000-0000-0000-000000000000/VK18WBC6Z4,3-4,SI-GA-B8,gs://image/image2.tif,V19J25-123,B1 sample_1,GRCh38-2020-A,gs://fc-e0000000-0000-0000-0000-000000000000/VK10WBC9Z2,1-2,SI-GA-A8,gs://image/image1.tif,V19J25-123,A1 sample_2,GRCh38-2020-A,gs://fc-e0000000-0000-0000-0000-000000000000/VK10WBC9Z2,3-4,SI-GA-B8,gs://image/image2.tif,V19J25-123,B1
3.2 Upload your sample sheet to the workspace bucket:
Example:gsutil cp /foo/bar/projects/sample_sheet.csv gs://fc-e0000000-0000-0000-0000-000000000000/
4. Launch analysis¶
In your workspace, open
WORKFLOWStab. Select the desired snapshot version (e.g. latest). Select
Run workflow with inputs defined by file pathsas below
Use call cachingand click
INPUTS. Then fill in appropriate values in the
Attributecolumn. 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 ANALYSISand then click
5. Notice: run
spaceranger mkfastq if you are non Broad Institute users¶
Non Broad Institute users that wish to run
spaceranger mkfastqmust create a custom docker image that contains
bcl2fastq.See bcl2fastq instructions.
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
Copy your FASTQ files to the workspace using gsutil in your unix terminal. 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 bcl2fastq file naming conventions.
gsutil -m cp -r /foo/bar/fastq_path/K18WBC6Z4 gs://fc-e0000000-0000-0000-0000-000000000000/K18WBC6Z4_fastq
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.
Set optional input
Visium spatial transcriptomics data¶
To process spatial transcriptomics data, follow the specific instructions below.
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)
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 ProbeSet, Image, DarkImage, ColorizedImage, CytaImage, Slide, Area, SlideFile, 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
true true run_count If you want to run
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 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:
“2.0.0” “2.0.0” config_version config docker version used for processing sample sheets, could be 0.3. “0.3” “0.3” 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” 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.
“gcr.io/broad-cumulus” “gcr.io/broad-cumulus” 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 backend
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
2 2 awsQueueArn The AWS ARN string of the job queue to be used. This only works for
See the table below for important sc/snRNA-seq outputs.
|fastq_outputs||Array[String]?||A list of cloud urls containing FASTQ files, one url per flowcell.|
|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).|