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 archs4 🐁

Find the most correlated genes to a gene of interest or find the gene's tissue expression atlas using ARCHS4.
Return format: JSON (command-line) or data frame/CSV (Python).

Positional argument
gene
Short name (gene symbol) of gene of interest, e.g. STAT4.
Alternatively: use flag --ensembl to input an Ensembl gene IDs, e.g. ENSG00000138378.

Optional arguments
-w --which
'correlation' (default) or 'tissue'.
'correlation' returns a gene correlation table that contains the 100 most correlated genes to the gene of interest. The Pearson correlation is calculated over all samples and tissues in ARCHS4.
'tissue' returns a tissue expression atlas calculated from human or mouse samples (as defined by 'species') in ARCHS4.

-s --species
'human' (default) or 'mouse'.
Defines whether to use human or mouse samples from ARCHS4.
(Only for tissue expression atlas.)

-o --out
Path to the file the results will be saved in, e.g. path/to/directory/results.csv (or .json). Default: Standard out.
Python: save=True will save the output in the current working directory.

Flags
-e --ensembl
Add this flag if gene is given as an Ensembl gene ID.

-csv --csv
Command-line only. Returns results in CSV format.
Python: Use json=True to return output in JSON format.

-q --quiet
Command-line only. Prevents progress information from being displayed.
Python: Use verbose=False to prevent progress information from being displayed.

Examples

gget archs4 ACE2

# Python
gget.archs4("ACE2")

→ Returns the 100 most correlated genes to ACE2:

gget archs4 -w tissue ACE2

# Python
gget.archs4("ACE2", which="tissue")

→ Returns the tissue expression of ACE2 (by default, human data is used):

Check out this tutorial by Dave Tang who wrote an R script to create this figure from the gget archs4 JSON output:

More examples

References

If you use gget archs4 in a publication, please cite the following articles:

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

• Lachmann A, Torre D, Keenan AB, Jagodnik KM, Lee HJ, Wang L, Silverstein MC, Ma’ayan A. Massive mining of publicly available RNA-seq data from human and mouse. Nature Communications 9. Article number: 1366 (2018), doi:10.1038/s41467-018-03751-6

• Bray NL, Pimentel H, Melsted P and Pachter L, Near optimal probabilistic RNA-seq quantification, Nature Biotechnology 34, p 525--527 (2016). https://doi.org/10.1038/nbt.3519