Space Ranger1.0, printed on 04/25/2024
Space Ranger's pipelines analyze sequencing data produced from Visium Spatial Gene Expression. The analysis involves the following steps:
Run spaceranger mkfastq on the Illumina BCL output folder to generate FASTQ files.
Run spaceranger count on each Capture Area that was demultiplexed by spaceranger mkfastq.
For the following example, assume that the Illumina BCL output is in a folder
named /sequencing/140101_D00123_0111_AHAWT7ADXX.
First, follow the
instructions on running spaceranger mkfastq
to generate FASTQ files. For example, if the flowcell serial number was
HAWT7ADXX, then spaceranger mkfastq will output FASTQ
files in HAWT7ADXX/outs/fastq_path.
To generate spatial feature counts for a single library using automatic fiducial alignment and tissue detection, run spaceranger count with the following arguments. For a complete listing of the arguments accepted, see the Command Line Argument Reference below, or run spaceranger count --help.
| For help on which arguments to use to target a particular set of FASTQs, consult Specifying Input FASTQ Files for 10x Pipelines. |
After determining these input arguments, run spaceranger:
$ cd /home/jdoe/runs $ spaceranger count --id=sample345 \ --transcriptome=/opt/refdata/GRCh38-3.0.0 \ --fastqs=/home/jdoe/runs/HAWT7ADXX/outs/fastq_path \ --sample=mysample \ --image=/home/jdoe/runs/images/sample345.tif \ --slide=V19J01-123 \ --area=A1 \ --localcores=8 \ --localmem=64
To generate spatial feature counts for a single library using a fiducial
alignment and tissue assignment json file generated in Loupe Browser, run
spaceranger count with the following arguments.
After determining these input arguments, run spaceranger:
$ cd /home/jdoe/runs $ spaceranger count --id=sample345 \ --transcriptome=/opt/refdata/GRCh38-3.0.0 \ --fastqs=/home/jdoe/runs/HAWT7ADXX/outs/fastq_path \ --sample=mysample \ --image=/home/jdoe/runs/images/sample345.tif \ --slide=V19J01-123 \ --area=A1 \ --loupe-alignment=sample345.json \ --localcores=8 \ --localmem=64
Following a set of preflight checks to validate input arguments, spaceranger count pipeline stages will begin to run:
Martian Runtime - 3.2.5 Running preflight checks (please wait)... 2016-11-10 14:23:52 [runtime] (ready) ID.sample345.SPATIAL_RNA_COUNTER_CS.SPATIAL_RNA_COUNTER_PREP.SETUP_CHUNKS 2016-11-10 14:23:55 [runtime] (split_complete) ID.sample345.SPATIAL_RNA_COUNTER_CS.SPATIAL_RNA_COUNTER_PREP.SETUP_CHUNKS 2016-11-10 14:23:55 [runtime] (run:local) ID.sample345.SPATIAL_RNA_COUNTER_CS.SPATIAL_RNA_COUNTER_PREP.SETUP_CHUNKS.fork0.chnk0.main ...
By default, spaceranger will use all of the cores available on your
system to execute pipeline stages. You can specify a different number of cores
to use with the --localcores option; for example,
--localcores=16 will limit spaceranger to using up to
sixteen cores at once. Similarly, --localmem will restrict the
amount of memory (in GB) used by spaceranger.
The pipeline will create a new folder named with the sample ID you specified
(e.g. /home/jdoe/runs/sample345) for its output. If this folder
already exists, spaceranger will assume it is an existing
pipestance and attempt to resume running it.
The spaceranger count pipeline accepts slide serial and capture
area arguments, in order to use the most precise fiducial and spot coordinates
for an experiment. The easiest way to pass this information to spaceranger
count is via the --slide and --area arguments.
When --slide is specified, the pipeline will download the layout
file associated with the supplied serial number. If spaceranger is
run in an environment without access to the outside Internet, follow the
instructions below in order to download a slide file locally.
If you do not know the serial number or capture area associated with the
experiment, you can still run spaceranger via the
--unknown-slide option. When specified, spaceranger
will use a default layout file for spot and fiducial coordinates. The typical
per-spot difference between the default layout and a specific slide is under 10
microns.
If the spaceranger is to be run in an environment without access to
the Internet, the pipeline will require a Visium slide layout file via the
--slidefile argument. You can download a layout file for a Visium
slide below. Enter the serial number of the slide (e.g.,
V19S01-123) and press 'Download'. The layout file will start to
download.
A successful spaceranger count run concludes with a message similar to this:
2016-11-10 16:10:09 [runtime] (join_complete) ID.sample345.SPATIAL_RNA_COUNTER_CS.SPATIAL_RNA_COUNTER_CS.SUMMARIZE_REPORTS Outputs: - Run summary HTML: /opt/sample345/outs/web_summary.html - Outputs of spatial pipeline: /opt/sample345/outs/spatial - Run summary CSV: /opt/sample345/outs/metrics_summary.csv - BAM: /opt/sample345/outs/possorted_genome_bam.bam - BAM index: /opt/sample345/outs/possorted_genome_bam.bam.bai - Filtered feature-barcode matrices MEX: /opt/sample345/outs/filtered_feature_bc_matrix - Filtered feature-barcode matrices HDF5: /opt/sample345/outs/filtered_feature_bc_matrix.h5 - Unfiltered feature-barcode matrices MEX: /opt/sample345/outs/raw_feature_bc_matrix - Unfiltered feature-barcode matrices HDF5: /opt/sample345/outs/raw_feature_bc_matrix.h5 - Secondary analysis output CSV: /opt/sample345/outs/analysis - Per-molecule read information: /opt/sample345/outs/molecule_info.h5 - Loupe Browser file: /opt/sample345/outs/cloupe.cloupe Pipestance completed successfully!
The output of the pipeline is contained in a folder named with the sample ID you
specified (e.g. sample345). The subfolder named outs
contains the main pipeline output files:
| File Name | Description |
|---|---|
web_summary.html | Run summary metrics and charts in HTML format |
spatial | Directory containing QC images for aligned fiducials and detetected tissue in jpg format, scalefactors_json.json, high and low resolution versions of the input image in png format, and tissue_positions_list.csv |
spatial/aligned_fiducials.jpg | Aligned fiducials QC image |
spatial/detected_tissue_image.jpg | Detected tissue QC image |
spatial/detected_tissue_image.png | Full resolution image downsampled to 2k pixels on the longest dimension |
spatial/detected_tissue_image.png | Full resolution image downsampled to 600 pixels on the longest dimension |
spatial/tissue_positions_list.csv | CSV containing spot barcode, if the spot was called under (1) or out (0) of tissue, the array position, image pixel position x, and image pixel postion y for the full resolution image |
spatial/scalefactors_json.json | Contains spot diameter estimation in pixels for the full resolution original image, tissue_hires_scalef which is the spot poisition multiplier in pixels for the high resolution image, fiducial spot diameter estimation in pixels for the full resolution original image, and tissue_hires_scalef which is the spot poisition multiplier in pixels for the low resolution image |
metrics_summary.csv | Run summary metrics in CSV format |
possorted_genome_bam.bam | Reads aligned to the genome and transcriptome annotated with barcode information |
possorted_genome_bam.bam.bai | Index for possorted_genome_bam.bam |
filtered_feature_bc_matrix | Filtered feature-barcode matrices containing only spot barcodes in MEX format |
filtered_feature_bc_matrix_h5.h5 | Filtered feature-barcode matrices containing only spot barcodes in HDF5 format |
raw_feature_bc_matrices | Unfiltered feature-barcode matrices containing all barcodes in MEX format |
raw_feature_bc_matrix_h5.h5 | Unfiltered feature-barcode matrices containing all barcodes in HDF5 format |
analysis | Secondary analysis data including dimensionality reduction, spot clustering, and differential expression |
molecule_info.h5 | Molecule-level information used by spaceranger aggr to aggregate samples into larger datasets. |
cloupe.cloupe | Loupe Browser visualization and analysis file |
Once spaceranger count has successfully completed, you can browse the resulting summary HTML file in any supported web browser, open the .cloupe file in Loupe Browser, or refer to the Understanding Output section to explore the data by hand.