Python arguments are equivalent to long-option arguments (--arg), unless otherwise specified. Flags are True/False arguments in Python. The manual for any gget tool can be called from the command-line using the -h --help flag.

gget 8cube 🔬

Screenshot 2026-01-19 at 5 34 43 PM

Query 8cubeDB (snRNA-sequencing data of 8 different mouse strains, tissues, and individuals (four of each sex)) for gene-level specificity metrics and normalized expression values.

Return format: JSON (command-line) or data frame/CSV (Python).

This module was written by Nikhila Swarna.


gget 8cube specificity 🎯

Retrieve ψ and ζ specificity statistics for one or more genes.

gget 8cube specificity <GENES...>

Positional argument
genes
Gene symbols or Ensembl gene IDs. Multiple genes allowed.

Optional arguments
-csv --csv
Returns CSV instead of JSON (command-line only).
Python: Use json=False (default DataFrame) or json=True for JSON.

-o --out
Output file path (CSV or .json depending on --csv).
Python: save=True saves automatically to the current directory.

Flags
-q --quiet
Suppresses progress information. Python: use verbose=False.

Example

gget 8cube specificity Acsm2 ENSMUSG00000046623.9

# Python
from gget.gget_8cube import specificity
specificity(["Acsm2", "ENSMUSG00000046623.9"])

→ Returns ψ and ζ specificity values for Acsm2.

gget 8cube psi_block 🧩

Retrieve ψ-block (block-level specificity) values for one or more genes.

gget 8cube psi_block <GENES...> --analysis_level <LEVEL> --analysis_type <TYPE>

Positional argument
genes
Gene symbols or Ensembl IDs.

Required arguments
-al --analysis_level
Biological analysis level (e.g., Kidney, Across_tissues).

-at --analysis_type
Partition type (e.g., Sex:Celltype, Sex:Strain).

Optional arguments
-csv --csv
Return CSV instead of JSON.
Python: use json=True for JSON.

-o --out
Output file location.

Flags
-q --quiet
Suppress progress printing.

Example

gget 8cube psi_block Acsm2 \
    --analysis_level Kidney \
    --analysis_type "Sex:Celltype"

# Python
from gget.gget_8cube import psi_block
psi_block(["Acsm2"], analysis_level="Kidney", analysis_type="Sex:Celltype")

→ Returns ψ-block partition-level specificity scores for Acsm2.

gget 8cube expression 📊

Retrieve mean and variance of normalized expression values for one or more genes.

gget 8cube expression <GENES...> --analysis_level <LEVEL> --analysis_type <TYPE>

Positional argument
genes
Gene symbols or Ensembl IDs. Multiple accepted.

Required arguments
-al --analysis_level
Biological grouping (e.g., Kidney, Across_tissues).

-at --analysis_type
Partition layout (e.g., Sex:Celltype).

Optional arguments
-csv --csv
Return CSV instead of JSON.
Python: use json=True.

-o --out
Output file path.

Flags
-q --quiet
Suppress progress messages.

Example

gget 8cube expression ENSMUSG00000046623.9 \
    --analysis_level Across_tissues \
    --analysis_type Strain

# Python
from gget.gget_8cube import gene_expression
gene_expression(["ENSMUSG00000046623.9"], analysis_level="Across_tissues", analysis_type="Strain")

→ Returns normalized expression values grouped by cell type and sex.

Example workflow

# Specificity
gget 8cube specificity Gjb4

# ψ-block specificity
gget 8cube psi_block Gjb4 --analysis_level Across_tissues --analysis_type Strain

# Expression values
gget 8cube expression Gjb4 --analysis_level Across_tissues --analysis_type Strain

Python API

from gget.gget_8cube import specificity, psi_block, gene_expression

or

from gget import specificity, psi_block, gene_expression

Notes

  • Works with gene symbols and Ensembl IDs (with or without version numbers).
  • All three functions accept multiple genes at once.
  • Default Python output is a pandas DataFrame; use json=True for JSON.
  • CLI defaults to JSON, unless --csv is used.

References

If you use gget 8cube in a publication, please cite:

  • Swarna NP, et al. Determining gene specificity from multivariate single-cell RNA sequencing data (2025). DOI forthcoming.

  • Luebbert, L., & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac836

  • Rebboah E, et al. Systematic cell-type resolved transcriptomes of 8 tissues in 8 lab and wild-derived mouse strains captures global and local expression variation (2025). https://doi.org/10.1101/2025.04.21.649844