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Seq-Scope Starter Tutorial

This tutorial uses an example SGE from mouse hippocampus, extracted via spatial masking from a Seq-Scope coronal brain slice.

It first runs the whole analysis with a single run_together command, then walks through the same analysis step by step: input preparation, SGE format conversion, FICTURE analysis, asset packaging, and data upload.

Run with run_together

Download the example data, hosted on Zenodo (10.5281/zenodo.17953582), into a work directory:

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work_dir=/path/to/work/directory        # replace with your work directory
mkdir -p ${work_dir} && cd ${work_dir}
wget https://zenodo.org/records/17953582/files/seqscope_starter.raw.tar.gz
tar -zxvf seqscope_starter.raw.tar.gz   # creates raw/ with barcodes.tsv.gz, features.tsv.gz, matrix.mtx.gz

Then run the pipeline, either with a local installation or with the Docker image:

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cartloader run_together \
  --platform seqscope \
  --in-dir ./raw \
  --out-dir ./out/seqscope_hippo \
  --id seqscope_hippo \
  --width 18 \
  --n-factor 6,12 \
  --threads 4 -j 4

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docker run --rm -v ${work_dir}:/data hyunminkang/cartloader:latest \
  run_together \
  --platform seqscope \
  --in-dir /data/raw \
  --out-dir /data/out/seqscope_hippo \
  --id seqscope_hippo \
  --width 18 \
  --n-factor 6,12 \
  --threads 4 -j 4

The Seq-Scope profile reads the MEX directory given by --in-dir and converts its nanometer coordinates to µm. The result to deploy is out/seqscope_hippo/cartl/seqscope_hippo/ (a catalog.yaml and the PMTiles it lists). Add --dry-run to print the commands without running them.

How run_together differs from the step-by-step path

  • FICTURE runs through run_ficture2_multi (a joint model over a single sample) with single-molecule pixel decoding, instead of run_ficture2.
  • Feature filters are the run_together defaults: control features (Unassigned, Neg, BLANK/Blank, Intergenic, Deprecated, System, NCS-, NCP-) are dropped at ingest; predicted (Gm<digit>), mitochondrial (mt-/MT-), and ribosomal (Rps/Rpl) genes stay in the data but are left out of the factor models. Change them with --ingest-exclude-feature-regex and --exclude-feature-regex.
  • Point layers are packaged with pmpoint (--use-pmpoint, 500 gene bins) instead of tippecanoe.
  • Outputs go under --out-dir: tsv/<id>/ (converted SGE), fic/ (FICTURE), and cartl/<id>/ (PMTiles and catalog.yaml; deploy this), instead of sge/, ficture2/, and cartload2/.
  • Publishing is built in: --s3-upload uploads cartl/<id>/ to S3 (see Publishing). For Zenodo, run upload_zenodo on cartl/<id>/ as in the step-by-step path.

In addition, the Seq-Scope profile counts the matrix's second count column ("GeneFull", --icols-mtx 2), whereas sge_convert below uses the first ("Gene", its default), so the counts differ slightly.

Step by Step (Individual Commands)

The same analysis, one command at a time. Use it to understand each stage or to customize it.

Set Up the Environment

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# ====
# Replace the placeholders with values for your system.
# ====
work_dir=/path/to/work/directory        # directory holding the input data; outputs are written here too
cd $work_dir

n_jobs=10                               # number of parallel jobs (also used as threads per job below)

# Activate the Python environment in which CartLoader is installed
conda activate ENV_NAME                 # replace ENV_NAME (or: source /path/to/venv/bin/activate)

No tool paths needed

Once submodules/build.sh has run (see Installation), every CartLoader command finds spatula, punkst, tippecanoe, pmtiles, and pmpoint in the submodules/ directory of the repository, and the default color map at assets/default_color_map.tsv. Pass --spatula, --ficture2 (the punkst repository directory, which contains bin/punkst), --tippecanoe, --pmtiles, or --cmap-file only to use a different build or file. upload_aws runs the aws CLI from your PATH (--aws to override).

Input Data

Use the data downloaded above. Actual input formats are platform-dependent; see the other platform tutorials for theirs.

SeqScope provides SGE with three files:

barcodes.tsv.gz – spatial barcode metadata 
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AAAACAAAAACCTTCTTCGGACACTGGTCT  1   20  1   1   295288  1422349 0,1,0,0,0
AAAACAAAAATCCTGTTATACATGCCATGG  2   45  1   1   1745544 1110720 2,2,1,0,1
AAAACAAAACACGGGAAAAAACTATAGGTG  3   58  1   1   887244  250820  7,7,5,0,1
  • Column 1: Sorted spatial barcodes
  • Column 2: 1-based integer index of spatial barcodes, used in matrix.mtx.gz
  • Column 3: 1-based integer index from the full barcode that is in the STARsolo output
  • Column 4: Lane ID (fixed as 1)
  • Column 5: Tile ID (fixed as 1)
  • Column 6: X-coordinates
  • Column 7: Y-coordinates
  • Column 8: Five comma-separated numbers denote the count per spatial barcode for "Gene", "GeneFull", "Spliced", "Unspliced", and "Ambiguous".
features.tsv.gz – feature metadata 
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ENSMUSG00000100764  Gm29155 1   1,1,1,0,0
ENSMUSG00000100635  Gm29157 2   0,0,0,0,0
ENSMUSG00000100480  Gm29156 3   0,0,0,0,0
  • Column 1: Feature ID
  • Column 2: Feature symbol
  • Column 3: 1-based integer index of genes, used in matrix.mtx.gz
  • Column 4: Five comma-separated numbers denote the count per gene "Gene", "GeneFull", "Spliced", "Unspliced", and "Ambiguous".
matrix.mtx.gz – expression count matrix 
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%%MatrixMarket matrix coordinate integer general
%
33989 2928173 5404336
2487 1 0 1 0 0 0
5104 2 1 1 0 0 1
  • Header: Initial lines form the header, declaring the matrix's adherence to the Market Matrix (MTX) format, outlining its traits. This may include comments (lines beginning with %) for extra metadata, all marked by a “%”.
  • Dimensions: Following the header, the first line details the matrix dimensions: the count of rows (features), columns (barcodes), and non-zero entries.
  • Data Entries: After the dimensions, subsequent lines enumerate non‑zero entries in seven columns: row index (feature index), column index (barcode index), and five values (expression levels) corresponding to "Gene", "GeneFull", "Spliced", "Unspliced", and "Ambiguous".
    • "Gene": represents unique, confidently mapped transcript count ("gene name"-based);
    • "GeneFull": denotes total transcript count assigned to gene (includes ambiguities).

Define ID and Parameters

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# Unique identifier for your dataset
DATA_ID="seqscope_hippo"                # change this to reflect your dataset name
PLATFORM="seqscope"                     # platform information
SCALE=1000                              # coordinate units per micrometer

# LDA parameters
train_width=18                           # define LDA training hexagon width (comma-separated if multiple widths are applied)
n_factor=6,12                            # define number of factors in LDA training (comma-separated if multiple n-factor values are provided)

How to define Scaling Factors for Seq-Scope?

The latest SeqScope with an Illumina NovaSeq 6000 uses NovaScope pipeline to process sequencing data. NovaScope defaults to generate SGE at nanometer (nm) resolution, meaning each pixel corresponds to 1 nm.

Thus, use 1000 as scaling factor from coordinate to micrometer since 1000 nm = 1 µm.

SGE Format Conversion

Convert the raw input to the unified SGE format. See more details in SGE Format Conversion.

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cartloader sge_convert \
  --makefn sge_convert.mk \
  --platform ${PLATFORM} \
  --in-mex ./raw \
  --units-per-um ${SCALE} \
  --out-dir ./sge \
  --exclude-feature-regex '^(BLANK|NegCon|NegPrb)' \
  --sge-visual \
  --n-jobs ${n_jobs}
Parameter Required Type Description
--platform required string Input platform (options: 10x_visium_hd, seqscope, illumina, 10x_xenium, bgi_stereoseq, cosmx_smi, vizgen_merscope, pixel_seq, nova_st, generic)
--in-mex required string Input MEX directory holding barcodes.tsv.gz, features.tsv.gz, and matrix.mtx.gz
--out-dir required string Output directory for the converted SGE files
--units-per-um float Coordinate units per µm in the input (default: 1). Seq-Scope MEX coordinates are in nanometers, so pass 1000
--icols-mtx int Which of the matrix's count columns to use, 1-based (default: 1, the "Gene" count of Seq-Scope data)
--makefn string File name for the generated Makefile (default: sge_convert.mk)
--exclude-feature-regex regex Pattern of feature names to drop (e.g. control probes)
--sge-visual flag Draw the converted SGE as an image (xy.png) for a visual check
--n-jobs int Number of parallel jobs (default: 1)

FICTURE Analysis

Compute spatial factors using punkst (FICTURE2). See more details on the Reference page.

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cartloader run_ficture2 \
  --makefn run_ficture2.mk \
  --main \
  --in-transcript ./sge/transcripts.unsorted.tsv.gz \
  --in-feature ./sge/feature.clean.tsv.gz \
  --in-minmax ./sge/coordinate_minmax.tsv \
  --exclude-feature-regex '^(mt-.*$|Gm\d+$)' \
  --out-dir ./ficture2 \
  --width ${train_width} \
  --n-factor ${n_factor} \
  --n-jobs ${n_jobs} \
  --threads ${n_jobs}
Parameter Required Type Description
--main required 1 flag Run all five main steps
--in-transcript required string Path to the transcript-indexed SGE file from sge_convert
--out-dir required string Output directory
--width required int or comma-separated list Hexagon width(s) in µm for LDA training
--n-factor required int or comma-separated list Number(s) of LDA factors; one model per width × factor combination
--makefn string File name for the generated Makefile (default: run_ficture2.mk)
--in-feature string Path to the per-gene count file from sge_convert (default: the feature list made during tiling)
--in-minmax string Path to the coordinate min/max file from sge_convert (default: the range computed during tiling)
--exclude-feature-regex regex Feature names to leave out of the factor analysis. punkst matches the whole name, so end each alternative with .* or $ as needed (the example drops mitochondrial mt-* and predicted Gm<digits> genes)
--cmap-file string Color map TSV (default: assets/default_color_map.tsv in the CartLoader repository)
--n-jobs int Number of parallel jobs (default: 1)
--threads int Number of threads per job (default: 4)

1: CartLoader requires at least one action: --tile (tiling), --segment (hexagon segmentation), --init-lda (LDA training), --decode (pixel-level decoding), --umap (UMAP of the factors), or --main for all five.

CartLoader Asset Packaging

Package the SGE and the FICTURE results into PMTiles and a catalog.yaml for CartoScope. See more details in its Reference page.

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cartloader run_cartload2 \
  --makefn run_cartload2.mk \
  --fic-dir ./ficture2 \
  --out-dir ./cartload2 \
  --id ${DATA_ID} \
  --n-jobs ${n_jobs} \
  --threads ${n_jobs}

Packaging without factor analysis

To host only the SGE (no factors), use cartloader run_together ... --no-ficture, which tiles the transcripts and packages them without training any model.

Parameter Required Type Description
--fic-dir required string FICTURE output directory from run_ficture2; must contain ficture.params.json (the SGE paths are read from it)
--out-dir required string Output directory for the PMTiles, assets, and catalog.yaml
--id required string Dataset ID used in the catalog and output metadata (no whitespace)
--makefn string File name for the generated Makefile (default: run_cartload2.mk)
--n-jobs int Number of parallel jobs (default: 1)
--threads int Number of threads per job (default: 4)
--spatula, --tippecanoe, --pmtiles string Tool paths (default: the copies built under submodules/)

Upload to Data Repository

Choose a data repository to host/share your output

CartLoader supports two upload options (AWS and Zenodo) for storing PMTiles of SGE and spatial factors in a data repository.

Choose the one that best suits your needs.

Upload the generated CartLoader outputs to your designated AWS S3 directory. The aws CLI must be configured with credentials that can write there (see AWS Upload Failures).

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# AWS S3 target location
S3_DIR=s3://your-bucket/path/to/dir     # recommended: end with DATA_ID, e.g. s3://your-bucket/test-data/${DATA_ID}

cartloader upload_aws \
  --in-dir ./cartload2 \
  --s3-dir "${S3_DIR}" \
  --n-jobs ${n_jobs}
Parameter Required Type Description
--in-dir required string Directory with the CartLoader packaging output (containing catalog.yaml)
--s3-dir required string Target S3 directory
--profile string AWS CLI profile to use
--aws string Path to the AWS CLI (default: aws)
--n-jobs int Number of parallel jobs (default: 2)

Upload the generated CartLoader outputs to a new Zenodo deposition or an existing one (see Zenodo Setup for the access token).

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zenodo_token=/path/to/zenodo/token/file    # file holding your Zenodo access token

cartloader upload_zenodo \
  --in-dir ./cartload2 \
  --upload-method catalog \
  --zenodo-token ${zenodo_token} \
  --title "Your Title" \
  --creators "Lastname, Firstname" \
  --description "This is an example description"
Parameter Required Type Description
--in-dir required string Directory with the CartLoader packaging output
--zenodo-token required string Path to the file holding your Zenodo access token
--upload-method string Which files to upload: all (every file in --in-dir; default), catalog (the files listed in catalog.yaml), or files (the names given with --files)
--catalog-yaml string Catalog to read with --upload-method catalog (default: <in-dir>/catalog.yaml)
--zenodo-deposition-id string Existing deposition to upload into (default: create a new one)
--title required 1 string Title of the deposition
--creators required 1 list of str Creators in "Lastname, Firstname" format, each quoted
--description string Description of the deposition

1: Required when creating a new deposition, or when the existing one lacks it.

Output Data

See output details in the reference pages for run_ficture2 and run_cartload2.