Vizgen MERSCOPE Starter Tutorial

Input Data

The input is a spatial gene expression (SGE) dataset from the adult mouse hippocampus, extracted by masking a coronal brain section (Slice Number: 2；Replicate Number: 1; file: detected_transcripts.csv) from Vizgen MERSCOPE Neuroscience Showcase.

File Format

The MERSCOPE input SGE includes one comma delimited text file with the following format:

CSV file format

,barcode_id,global_x,global_y,global_z,x,y,fov,gene
0,22,56.930107,3851.851,5.0,147.80061,1711.9067,0,Adgre1
1,22,183.60107,3874.0085,5.0,1320.6799,1917.0692,0,Adgre1
2,22,59.750736,3666.5576,5.0,132.66754,1844.2372,1,Adgre1

• Column 1: Unique numeric index for each transcript within a field of view (non-consecutive, ascending).
• barcode_id: Zero-based index of the transcript barcode in the codebook; forms a composite key with fov.
• global_x: Transcript x coordinates (µm) in the experimental region; may be negative due to alignment.
• global_y: Transcript y coordinates (µm) in the experimental region; may be negative due to alignment.
• global_z: The index of the z-position. The position is a zero-indexed integer.
• x: The x-coordinate of the transcript (µm), within the coordinate space of the field of view.
• y: The y-coordinate of the transcript (µm), within the coordinate space of the field of view.
• fov: Zero-based field of view index; forms a composite key with barcode_id.
• gene: Gene name associated with the transcript.

Data Access

The example data is hosted on Zenedo ().

Follow the commands below to download the example data.

work_dir=/path/to/work/directory
cd $work_dir
wget  https://zenodo.org/records/15786632/files/merscope_starter.raw.tar.gz
tar --strip-components=1 -zxvf merscope_starter.raw.tar.gz

Set Up the Environment

Define paths to all required binaries and resources. Optionally, specify a fixed color map for consistent rendering.

# ====
# Replace each placeholder with the actual path on your system.  
# ====

work_dir=/path/to/work/directory        # path to work directory that contains the downloaded input data
cd $work_dir

# Define paths to required binaries and resources
spatula=/path/to/spatula/binary         # path to spatula executable
punkst=/path/to/punkst/binary           # path to FICTURE2/punkst executable
tippecanoe=/path/to/tippecanoe/binary   # path to tippecanoe executable
pmtiles=/path/to/pmtiles/binary         # path to pmtiles executable
aws=/path/to/aws/cli/binary             # path to AWS CLI binary

# (Optional) Define path to color map. 
cmap=/path/to/color/map                 # Path to the fixed color map for rendering. cartloader provides a fixed color map at cartloader/assets/fixed_color_map_256.tsv.

# Number of jobs
n_jobs=10                               # If not specify, the number of jobs defaults to 1.

# Activate the bioconda environment
conda activate ENV_NAME                 # replace BIOENV_NAME with your bioconda environment name

Define data ID and analysis parameters:

# Unique identifier for your dataset
DATA_ID="merscope_hippo"                # change this to reflect your dataset name
PLATFORM="vizgen_merscope"              # platform information
SCALE=1                                 # scale from coordinate to micrometer

# LDA parameters
train_width=12                           # 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 are applied)

How to Define Scaling Factors for MERSCOPE?

The MERSCOPE example data currently used here provides SGE in micrometer units. Use define scaling factor from coordinate to micrometer as 1.

SGE Format Conversion

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

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|NegCon|NegPrb)' \
  --sge-visual \
  --spatula ${spatula} \
  --n-jobs ${n_jobs}

Parameter	Required	Type	Description
--platform	required	string	Platform (options: "10x_visium_hd", "seqscope", "10x_xenium", "bgi_stereoseq", "cosmx_smi", "vizgen_merscope", "pixel_seq", "generic")
--in-csv	required	string	Path to the input TSV/CSV file
--units-per-um	required	float	Scale to convert coordinates to microns (default: 1.0)
--out-dir	required	string	Output directory for the converted SGE files
--makefn		string	File name for the generated Makefile (default: sge_convert.mk)
--exclude-feature-regex		regex	Pattern to exclude control features
--sge-visual		flag	Enable SGE visualization step (generates diagnostic image) (default: FALSE)
--spatula		string	Path to the spatula binary (default: spatula)
--n-jobs		int	Number of parallel jobs for processing (default: 1)

FICTURE Analysis

Compute spatial factors using punkst (FICTURE2 mode). See more details in Reference page.

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 \
  --cmap-file ${cmap} \
  --exclude-feature-regex '^(mt-.*$|Gm\d+$)' \
  --out-dir ./ficture2 \
  --width ${train_width} \
  --n-factor ${n_factor} \
  --spatula ${spatula} \
  --ficture2 ${punkst} \
  --n-jobs ${n_jobs} \
  --threads ${n_jobs}

Parameter	Required	Type	Description
--main	required 1	flag	Enable cartloader to run all five steps
--in-transcript	required	string	Path to input transcript-level SGE file
--out-dir	required	string	Path to output directory
--width	required	int or comma-separated list	LDA training hexagon width(s)
--n-factor	required	int or comma-separated list	Number of LDA factors
--makefn		string	File name for the generated Makefile (default: run_ficture2.mk )
--in-feature		string	Path to input feature file
--in-minmax		string	Path to input coordinate min/max file
--cmap-file		string	Path to color map file
--exclude-feature-regex		regex	Pattern to exclude features
--spatula		string	Path to the spatula binary (default: spatula)
--ficture2		string	Path to the punkst directory (defaults to punkst repository within submodules directory of cartloader)
--n-jobs		int	Number of parallel jobs (default: 1)
--threads		int	Number of threads per job (default: 1)

_{¹: cartloader requires the user to specify at least one action. Available actions includes: --tile to run tiling step; --segment to run segmentation step; --init-lda to run LDA training step; --decode to run decoding step; --summary to run summarization step; --main to run all above five actions.}

cartloader Compilation

Generate pmtiles and web-compatible tile directories. See more details in Reference page.

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

Parameter	Required	Type	Description
--fic-dir	required	string	Path to the input directory containing FICTURE2 output
--out-dir	required	string	Path to the output directory for PMTiles and web tiles
--id	required	string	Dataset ID used for naming outputs and metadata
--makefn		string	File name for the generated Makefile (default: run_cartload2.mk)
--spatula		string	Path to the spatula binary (default: spatula)
--pmtiles		string	Path to the pmtiles binary (default: pmtiles)
--tippecanoe		string	Path to the tippecanoe binary (default: tippecanoe)
--n-jobs		int	Number of parallel jobs (default: 1)
--threads		int	Number of threads per job (default: 1)

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.

AWS Uploads

Upload the generated cartloader outputs to your designated AWS S3 directory:

# AWS S3 target location for cartostore
AWS_BUCKET="EXAMPLE_AWS_BUCKET"         # replace EXAMPLE_AWS_BUCKET with your actual S3 bucket name

cartloader upload_aws \
  --in-dir ./cartload2 \
  --s3-dir "s3://${AWS_BUCKET}/${DATA_ID}" \
  --aws ${aws} \
  --n-jobs ${n_jobs}

Parameter	Required	Type	Description
--in-dir	required	string	Path to the input directory containing the cartloader compilation output
--s3-dir	required	string	Path to the target S3 directory for uploading
--aws		string	Path to the AWS CLI binary
--n-jobs		int	Number of parallel jobs

Zenodo Uploads

Upload the generated cartloader outputs to your designated Zenodo deposition or a new deposition.

zenodo_token=/path/to/zenodo/token/file    # replace /path/to/zenodo/token/file by path to your zenodo token file

cartloader upload_zenodo \
  --in-dir ./cartload2 \
  --upload-method catalog \
  --zenodo-token $zenodo_token \
  --create-new-deposition \
  --title  "Yur Title" \
  --creators "Your Name" \
  --description "This is an example description"

Parameter	Required	Type	Description
--in-dir	required	string	Path to the input directory containing the cartloader compilation output
--upload-method	required	string	Method to determine which files to upload. Options: all to upload all files in --in-dir; catalog to upload files listed in a catalog YAML file, user_list to upload files explicitly listed via --in-list
--catalog-yaml		string	Required if --upload-method catalog. Path to the catalog.yaml file generated in run_cartload2. If absent, will use the catalog.yaml in the input directory specified by --in-dir.
--zenodo-token	required	string	Path to your Zenodo access file
--create-new-deposition		flag	a new Zenodo deposition will be created.
--title	required	string	Required if --create-new-deposition. Title for the new Zenodo deposition.
--creators	required	list of str	List of creators in "Lastname, Firstname" format.

Output Data

See more details of output at the Reference pages for run_ficture2 and run_cartload2.

Spatial Factor Inference from FICTURE

Below is an example of spatial factor inference results produced by FICTURE using a training width of 12, 12 factors, a fit width of 12, and an anchor resolution of 6.

Factor	RGB	Weight	PostUMI	TopGene_pval	TopGene_fc	TopGene_weight
0	255,101,101	0.30225	799546	Slc17a7,Epha4,Amigo2,Kit,Syt4,Epha6,Anxa11,Npy2r,Nrp1,Adora1,Chrm3,Gpr26,Ackr1,Gpr158,Celsr2,Gabbr2,Gpr22,Chrm1,Adra1d,Epha5	Avpr1b,Blank-85,Kit,Amigo2,Blank-158,Hcrtr2,Npy6r,Blank-49,Oxtr,Npy2r,Gpr26,Hcrtr1,Blank-146,Cckbr,Adgrg7,Mrgprb1,Blank-28,Adgrf4,Blank-32,Blank-151	Slc17a7,Epha4,Cx3cl1,Epha6,Gabbr1,Gabbr2,Adora1,Baiap2,Celsr2,Grin2b,Chrm1,Syt4,Anxa11,Grm5,Epha7,Ntrk3,Adgrl1,Nrp1,Chrm3,Lmtk2
1	237,238,0	0.14118	373464	Grm2,Ntrk3,Baiap2,Epha7,Tmem108,Cxcl12,Adcyap1r1,Chrm1,Htr4,Mas1,Npy1r,Grm7,Anxa11,Grm1,Drd1,Epha4,Sstr2,Grin2b,Gprc5b,Grm5	Grm2,Ghsr,Drd1,Npy1r,Glp2r,Sstr2,Htr4,Igfbpl1,Gpr139,Mc4r,Rho,Sstr1,Lgr6,P2ry4,Tmem108,Mas1,Cxcl12,Blank-31,Epha1,Peg10	Slc17a7,Epha4,Ntrk3,Baiap2,Gabbr1,Epha7,Cx3cl1,Grin2b,Chrm1,Gabbr2,Grm5,Grm2,Gprc5b,Anxa11,Adgrl1,Celsr2,Grm7,Adcyap1r1,Epha6,Gad1
2	101,255,101	0.12849	339911	Sstr4,Gpr161,Cx3cl1,Chrm3,Grin2b,Epha6,Htr1a,Epha7,Man1a,Htr1b,Chrm5,Baiap2,Tyro3,Adgrb3,Gpr63,Grm5,Gabbr1,Htr5b,Ror1,Chrm1	Chrm5,Htr5b,Gpr161,Htr1b,Gpr101,Ackr2,Htr1a,Sstr4,Ror1,Ntsr1,Man1a,Gpr151,Ephb3,Gpr63,Chrm4,Gpr6,Htr1f,Tyro3,Chrm3,Crhr1	Slc17a7,Cx3cl1,Gabbr1,Epha6,Epha4,Grin2b,Epha7,Baiap2,Sstr4,Chrm3,Chrm1,Grm5,Celsr2,Ntrk3,Gabbr2,Adgrb3,Adgrl1,Adgrb1,Lmtk2,Gpr161
3	101,254,255	0.09486	250947	Gpr37l1,Ntsr2,Fgfr3,S1pr1,Aqp4,Cspg5,Ntrk2,Aldh1l1,Sox9,Slc25a18,Mertk,Ednrb,Mlc1,Timp4,Gfap,Fzd2,Axl,Gramd3,Gprc5b,Adgrg1	Gpr37l1,Fgfr3,Aqp4,Sctr,Fzd2,Celsr1,S1pr1,Ppp1r3g,Ntsr2,Hrh1,Ednrb,Blank-133,Egfr,Gpr156,Adora2b,Aldh1l1,Mertk,Blank-45,Sox9,Slc25a18	Gpr37l1,Ntsr2,Cspg5,Gfap,Ntrk2,Fgfr3,S1pr1,Aqp4,Gprc5b,Sox9,Slc25a18,Aldh1l1,Gabbr1,Mertk,Slc17a7,Mlc1,Adcyap1r1,Timp4,Axl,Adgrl1
4	101,101,255	0.08956	236909	Gfap,Adgrl1,Gpr27,Slc17a7,Ntrk2,Adora1,Mlc1,Timp4,Aldh1l1,Slc25a18,Axl,Sox9,Cspg5,Gpr162,Fgfrl1,Adra2c,Gpr26,Gprc5b,Kiss1r,Slc32a1	Adra2c,Gpr82,Vmn1r54,Blank-65,Cxcr2,Gpr27,Gpr143,Blank-20,Vmn1r42,Gfap,Blank-128,Blank-51,Mlc1,Blank-121,Kiss1r,F2rl2,Timp4,Cxcr6,Th,Prokr1	Slc17a7,Gfap,Adgrl1,Gabbr1,Adora1,Ntrk2,Cspg5,Gabbr2,Gpr27,Gprc5b,Gpr162,Olig1,Ntsr2,Mlc1,Chrm1,Epha6,Slc25a18,Epha7,Sox9,Celsr2
5	255,101,254	0.05543	146630	Grm4,Slc17a6,Adgra1,Gabbr1,Hrh3,Drd2,Gabbr2,Opn3,Adra1b,Epha8,Fzd10,Gpr153,Ptger3,Prokr2,Grm1,Adgrl2,Cx3cl1,Htr7,Adrb1,Hrh2	Slc17a6,Opn3,Drd2,Epha8,Adra1b,Grm4,Agtr1b,Ptger3,Adgra1,Fzd10,Hrh3,Adgrd1,Blank-157,Prokr2,Adra2b,Dgkk,Gpr153,Blank-93,Gpr139,Blank-43	Gabbr1,Gabbr2,Slc17a7,Cx3cl1,Grm4,Adgra1,Epha4,Slc17a6,Adgrl1,Grin2b,Hrh3,Cspg5,Celsr2,Gpr162,Grm1,Syt4,Igf1r,Lmtk2,Ntrk2,Adgrb1
6	255,178,101	0.05248	138815	Gad1,Cnr1,Erbb4,Slc32a1,Slc17a8,Alk,Oprd1,Rxfp3,Chrm2,Nmbr,Grpr,Oprm1,Adra1a,Qrfpr,Hrh3,Hrh2,Gabbr1,Gpr176,Gpr83,Oprk1	Slc17a8,Rxfp3,Chrm2,Qrfpr,Grpr,Oprm1,Slc32a1,Gad1,Nmbr,Oprk1,Alk,Erbb4,Oprd1,Tacr1,Rxfp1,Cnr1,Gpr149,Blank-83,Htr2b,Ptger2	Gad1,Cnr1,Gabbr1,Erbb4,Gabbr2,Cx3cl1,Epha6,Slc32a1,Slc17a7,Cspg5,Ntrk3,Grm5,Adgrl1,Syt4,Grin2b,Epha4,Celsr2,Adgrb1,Gpr162,Epha7
8	0,223,95	0.03778	99949	C1qb,C1qa,Cx3cr1,Selplg,Gfap,Csf1r,P2ry12,P2yr13,Adgre1,Slc15a3,Cmklr1,Mertk,P2ry6,Gpr183,Gpr34,Cd300c2,Adrb2,C3ar1,Gpr84,Ptafr	Selplg,Cx3cr1,Gpr84,Adora3,P2yr13,Gpr183,C3ar1,C1qa,C1qb,Csf1r,Slc15a3,P2ry12,Ptafr,Cmklr1,Adgre1,Cd300c2,Gpr34,Cxcr6,P2ry6,Adrb2	Gfap,C1qb,C1qa,Cx3cr1,Selplg,Csf1r,Slc17a7,P2ry12,Ntrk2,Cspg5,Mertk,Gprc5b,Gabbr1,Ntsr2,Aqp4,Gpr37l1,Sox9,Adgrl1,Adgre1,Slc25a18
7	178,255,101	0.03655	96700	Flt1,Cldn5,Fn1,Adgrf5,Adgrl4,Kdr,Cxcl12,Tek,Slco1a4,Abcc9,Atp13a5,Tie1,Igf1r,Emcn,Arhgap29,Pdgfrb,Kcnj8,Flt4,Gpr4,Fzd6	Cldn5,Flt1,Fn1,Adgrf5,Flt4,Abcc9,Adgrl4,Tek,Gper1,Tie1,Slco1a4,Kdr,Ccrl2,Vmn1r46,Atp13a5,Emcn,Gpr39,Gpr4,Tbxa2r,Blank-82	Flt1,Cldn5,Fn1,Adgrf5,Igf1r,Cxcl12,Adgrl4,Kdr,Gfap,S1pr1,Arhgap29,Slc17a7,Slco1a4,Pdgfrb,Tek,Gabbr1,Atp13a5,Abcc9,Tie1,Nrp1
9	101,178,255	0.03245	85847	Gjc3,Olig1,Lpar1,S1pr5,Erbb3,Gpr62,Sox8,Gprc5b,Plxnb3,Fgfr2,Sema4d,Ddr1,Tyro3,Grm3,Tjap1,Ephb1,Gipr,Gramd3,Peg10,Erbb4	S1pr5,Erbb3,Gjc3,Plxnb3,Lpar1,Gpr62,Gipr,Adgrg2,Fgfr2,Olig1,Sox8,Sema4d,Grm3,Ddr1,Fzd5,Tyro3,Blank-41,Tjap1,Blank-36,Gprc5b	Gjc3,Olig1,Gprc5b,Sox8,Ddr1,Lpar1,Sema4d,Tyro3,Gabbr1,Gfap,Fgfr2,Grm3,S1pr5,Gpr62,Erbb3,Plxnb3,Ephb1,Tjap1,Igf1r,Adora1
10	178,101,255	0.01681	44462	Gpr17,Olig1,Pdgfra,Cspg5,Pcdh15,Traf4,Sox8,Mki67,Gjc3,Top2a,Gpr37l1,Ascl1,Plxnb3,Ednrb,Fzd9,Erbb3,Sstr1,Mc5r,S1pr2,Calcrl	Gpr17,Mki67,Pdgfra,Top2a,Pcdh15,Traf4,Blank-109,Ascl1,Olig1,Mc5r,Fzd9,S1pr2,Sox8,Sstr1,Prlhr,Prokr1,Blank-116,Plxnb3,Blank-72,Cspg5	Olig1,Cspg5,Gpr17,Pdgfra,Gpr37l1,Gjc3,Sox8,Traf4,Pcdh15,Ntrk2,Gfap,Ntrk3,Adcyap1r1,Adgrb1,Gabbr1,Ednrb,Adora1,Erbb4,Slc17a7,Adgrl1
11	255,153,204	0.01216	32170	Htr2c,Gpr182,Efemp1,Cxcl12,Myh11,Crhr2,Igfbpl1,Lmod1,Cxcr4,Fzd4,Gprc5c,Ptgdr,Ddr2,Arhgap29,Hcar1,Ror2,Dlk1,Sox9,Ddr1,Fzd2	Gpr55,Gpr182,Ptgdr,Lpar3,Hcar1,Htr2c,Cxcr3,Ror2,Mc3r,Crhr2,Lmod1,Musk,Slc47a1,Gpr20,Gpr88,Gpr132,Mrgprf,Efemp1,Igfbpl1,Dlk1	Cxcl12,Sox9,Ddr1,Ntrk2,Htr2c,Anxa11,Gprc5b,Igf1r,Arhgap29,Gfap,Gpr182,Efemp1,Myh11,Fgfr2,Smo,Pdgfra,Tmem108,Fzd4,Axl,Man1a

Packed SGE and Spatial Factor Outputs from run_cartload2

The packed SGE data and spatial factor inferences generated by FICTURE are available in PMTile format on Zenodo: DOI:10.5281/zenodo.15824932.

These datasets can also be loaded directly using the following catalog YAML file:
https://zenodo.org/records/15824933/catalog.yaml