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Pixel-Seq Starter Tutorial

Since the Pixel-Seq publication provides SGE data only from the mouse olfactory bulb and parabrachial nucleus — neither of which includes the hippocampus — we extract a subregion from the olfactory bulb as the input for this tutorial.

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/pixelseq_starter.raw.tar.gz
tar --strip-components=1 -zxvf pixelseq_starter.raw.tar.gz   # extracts input.tsv.gz

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

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cartloader run_together \
  --platform generic \
  --raw-transcript ./input.tsv.gz \
  --colname-transcript-x xcoord \
  --colname-transcript-y ycoord \
  --colname-transcript-feature geneName \
  --units-per-um 3.076923 \
  --out-dir ./out/pixelseq_hippo \
  --id pixelseq_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 generic \
  --raw-transcript /data/input.tsv.gz \
  --colname-transcript-x xcoord \
  --colname-transcript-y ycoord \
  --colname-transcript-feature geneName \
  --units-per-um 3.076923 \
  --out-dir /data/out/pixelseq_hippo \
  --id pixelseq_hippo \
  --width 18 \
  --n-factor 6,12 \
  --threads 4 -j 4

Pixel-seq has no dedicated run_together profile, so the tab-separated file goes through the generic profile: the --colname-transcript-* options name its coordinate and gene columns (each row counts as one molecule, as in the step-by-step path), and --units-per-um sets the pixel size. The result to deploy is out/pixelseq_hippo/cartl/pixelseq_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.

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.

The Pixel-Seq SGE includes one tab-delimited text file (input.tsv.gz in the starter data), where each row represents a unique RNA molecule detected within a defined field of view (FOV), with associated genomic and spatial metadata.

TSV file format

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FOVx  FOVy  xcoord     ycoord     UMIs     SpatialBarcode            MapStrand  Chrom  Start      STARmapping        Counts  geneID              geneName  bioType                 intronRatio
017   005   26691.5    5786.5     TAACGAA  AAGGTTCATACCTACGACTGTTAA  16         1      24613729   150M               1       ENSMUSG00000101111  Gm28437   unprocessed_pseudogene  0.00
016   007   27590.25   4639.0815  TAATATA  AATGGCGCATTTTGCTGTTTAGGC  16         2      39001628   138M2341N12M       1       ENSMUSG00000062997  Rpl35     protein_coding          0.00
018   006   25099.945  5621.8335  AGTTGTA  CTGCATATGTGTCACCTAGGTAGC  16         1      24615767   150M               1       ENSMUSG00000101249  Gm29216   unprocessed_pseudogene  0.00
  • FOVx, FOVy: Field-of-view indices indicating the imaging tile coordinates in the x and y directions.
  • xcoord, ycoord: Spatial coordinates (in microns or pixels).
  • UMIs: Unique molecular identifier (UMI) sequence.
  • SpatialBarcode: Spatial barcode capturing the location and identity.
  • MapStrand: Indicates the strand orientation of the mapped read.
  • Chrom, Start: Chromosome number and start position of the mapped read on the genome.
  • STARmapping: Alignment pattern (CIGAR string) from the STAR aligner indicating how the transcript maps to the genome.
  • Counts: Number of times the UMI/gene combination was observed.
  • geneID, geneName: Ensembl gene ID and gene symbol.
  • bioType: Gene biotype.
  • intronRatio: Fraction of UMI counts assigned to intronic regions.

Define ID and Parameters

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# Unique identifier for your dataset
DATA_ID="pixelseq_hippo"                # change this to reflect your dataset name
PLATFORM="pixel_seq"                    # platform information
SCALE=3.076923                          # 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 Pixel-Seq?

In Pixel-Seq publication:

"Because polonies have varied sizes and shapes, to maximize the feature resolution we developed a base-calling pipeline to determine the major barcode species in each pixel (0.325 × 0.325 µm²) of gel images to construct a spatial barcode map".

Accordingly, we defined scale as 1/0.325 = 3.076923

SGE Format Conversion

Convert the raw input to the unified SGE format. See more details in its Reference page.

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cartloader sge_convert \
  --makefn sge_convert.mk \
  --platform ${PLATFORM} \
  --in-csv ./input.tsv.gz \
  --units-per-um ${SCALE} \
  --out-dir ./sge \
  --exclude-feature-regex '^(BLANK|Neg|Intergenic|Deprecated|Unassigned)' \
  --sge-visual \
  --n-jobs ${n_jobs}
Parameter Required Type Description
--platform required string Input platform, which sets the default input columns and delimiter (options: 10x_visium_hd, seqscope, illumina, 10x_xenium, bgi_stereoseq, cosmx_smi, vizgen_merscope, pixel_seq, nova_st, generic)
--in-csv required string Path to the input transcript CSV/TSV file
--out-dir required string Output directory for the converted SGE files
--units-per-um float Coordinate units per µm in the input (default: 1, i.e. already in µm; 1000 for the Seq-Scope raw TSV; detected from the barcodes for illumina)
--csv-colname-feature-name string Gene-name column in the input (default: set by --platform)
--csv-colname-count string Count column in the input (default: set by --platform; without one, each row counts 1)
--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.