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spaceranger-fgcz

Runs SpaceRanger for Visium and VisiumHD spatial transcriptomics on FGCZ SUSHI infrastructure.

Updated May 28, 2026
Search Tags:#bioinformatics#spatial transcriptomics#visium#10x genomics#demultiplexing#sushi#spaceranger
One-click install
npx skills add https://github.com/cpanse/skills --skill spaceranger-fgcz-cpanse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: spaceranger-fgcz
Source: https://github.com/cpanse/skills/tree/main/sequencing-pipelines/skills/spaceranger-fgcz
Command: npx skills add https://github.com/cpanse/skills --skill spaceranger-fgcz-cpanse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Running 10x Genomics SpaceRanger on FGCZ infrastructure involves many platform-specific pitfalls: wrong demultiplexing masks for VisiumHD (R1=43bp), missing slide/area/image metadata in SUSHI datasets, probe-based vs 3' polyA chemistry confusion, and Ruby API submission gotchas. This Skill encodes the exact workflows, parameters, and troubleshooting steps to get SpaceRanger jobs right the first time. ## Core Features & Use Cases - Platform decision tree: Covers standard Visium (frozen/FFPE), CytAssist, VisiumHD, and CytAssist VisiumHD (H1- and H2- slides) with correct BasesMask, probeset, and cmdOptions for each. - SUSHI dataset preparation: SQL templates for creating annotated child datasets with Slide, Area, Image, and CytaImage columns, plus JPG-to-TIFF conversion for H&E images. - Demux and submission recovery: Manual bcl2fastq workflow for VisiumHD lanes, Ruby API submission with Hash-default parameter fixes, and a troubleshooting table for common failures. - Use Case: A bioinformatician receives VisiumHD CytAssist FFPE samples, verifies chemistry via TSO scan, prepares the annotated SUSHI dataset, and submits SpaceRangerCount 4.1.0 with the correct probeset and --disable-cell-annotation flag. ## Quick Start Ask the agent to prepare and submit a SpaceRangerCount job in SUSHI for your Visium or VisiumHD samples, specifying the slide type and species.

Frequently Asked Questions about spaceranger-fgcz

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run SpaceRanger on VisiumHD samples at FGCZ?▼

Submit SpaceRangerCount via SUSHI on an annotated dataset, using SpaceRanger 4.1.0, BasesMask y43n*,I10n*,I10n*,y50n*, and 100GB RAM. For V-slides without slide info set cmdOptions to --unknown-slide=visium-hd; for CytAssist H-slides the cytaimage encodes slide and area.

What BasesMask should I use for VisiumHD demultiplexing?▼

VisiumHD requires y43n*,I10n*,I10n*,y50n* because R1 is 43bp (28bp barcode plus 15bp UMI). Standard Visium uses y28n*,I10n*,I10n*,y90n* for frozen or y50n* for FFPE. Verify R1 length after demux with zcat and awk.

How do I tell probe-based VisiumHD FFPE from 3' polyA chemistry?▼

Check the B-Fabric order's libraryprotocoloption for FF versus FFPE, then confirm by scanning R2 reads for the 10x TSO sequence AAGCAGTGGTATCAACGCAGAGTAC. High TSO counts indicate 3' polyA capture needing no probesetFile; near-zero counts indicate probe-based FFPE requiring a matching probeset CSV.

Why does my Ruby API SpaceRanger submission fail with special characters error?▼

Dropdown parameters from set_default_parameters default to Ruby Hashes, which ezRun rejects when stringified. Explicitly assign string values to transcriptTypes, probesetFile, and panelFile before calling app.run, and set partition to employee to avoid the -p nan SLURM error.

Why does SpaceRanger fail at the end with a 10x cloud token error?▼

On SpaceRanger 4.1 or later, runSegmentation=true also triggers cloud-based cell annotation, which needs a 10x cloud token that the trxcopy user lacks. Add --disable-cell-annotation to cmdOptions so segmentation runs without the annotation step.

Can I use a JPG H&E image for CytAssist VisiumHD samples?▼

No. ezRun unconditionally runs tiffsplit on the Image column for H-prefix slides, which fails on JPG or PNG. Convert the image to TIFF with Python PIL using LZW compression, then update both the SBATCH input and the SUSHI sample row.

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