celldex - reference cell type datasets

This package provides reference datasets with annotated cell types for convenient use by BiocPy packages and workflows in Python. These references were sourced and uploaded by the celldex R/Bioconductor package.

Each dataset is loaded as a SummarizedExperiment that is ready for further analysis, and may be used for downstream analysis, e.g in the SingleR Python implementation.

Installation

To get started, install the package from PyPI:

pip install celldex

Find reference datasets

The list_references() function will display all available reference datasets along with their metadata.

from celldex import list_references

refs = list_references()
print(refs[["name", "version"]].head(3))

## output
# |    | name             | version    |
# |---:|:-----------------|:-----------|
# |  0 | immgen           | 2024-02-26 |
# |  1 | blueprint_encode | 2024-02-26 |
# |  2 | dice             | 2024-02-26 |

Fetch reference datasets

Fetch a dataset as a SummarizedExperiment:

ref = fetch_reference("immgen", version="2024-02-26")
ref2 = fetch_reference("hpca", "2024-02-26")

print(ref)

## output
# class: SummarizedExperiment
# dimensions: (22134, 830)
# assays(1): ['logcounts']
# row_data columns(0): []
# row_names(22134): ['Zglp1', 'Vmn2r65', 'Gm10024', ..., 'Ifi44', 'Tiparp', 'Kdm1a']
# column_data columns(3): ['label.main', 'label.fine', 'label.ont']
# column_names(830): ['GSM1136119_EA07068_260297_MOGENE-1_0-ST-V1_MF.11C-11B+.LU_1.CEL', 'GSM1136120_EA07068_260298_MOGENE-1_0-ST-V1_MF.11C-11B+.LU_2.CEL', 'GSM1136121_EA07068_260299_MOGENE-1_0-ST-V1_MF.11C-11B+.LU_3.CEL', ..., 'GSM920653_EA07068_201207_MOGENE-1_0-ST-V1_TGD.VG4+24AHI.E17.TH_3.CEL', 'GSM920654_EA07068_201214_MOGENE-1_0-ST-V1_TGD.VG4+24ALO.E17.TH_1.CEL', 'GSM920655_EA07068_201215_MOGENE-1_0-ST-V1_TGD.VG4+24ALO.E17.TH_2.CEL']
# metadata(0):

Search for references

There's limited number of references right now, but if you want to search for references,

res = search_references("human")
res = search_references(define_text_query("Immun%", partial="True"))
res = search_references(define_text_query("10090", field="taxonomy_id"))

Adding new reference datasets

These instructions follow the same steps outlined in the scrnaseq package.

1. Format your dataset as a SummarizedExperiment. Let's mock a reference dataset:

   Note: Experiment object must include an assay ('logcounts') matrix containing log-normalized counts.

   import numpy as np
   from summarizedexperiment import SummarizedExperiment
   from biocframe import BiocFrame
   
   mat = np.random.exponential(1.3, (100, 10))
   row_names = [f"GENE_{i}" for i in range(mat.shape[0])]
   col_names = list("ABCDEFGHIJ")
   sce = SummarizedExperiment(
        assays={"logcounts": mat},
        row_data=BiocFrame(row_names=row_names),
        column_data=BiocFrame(data={"label.fine": col_names}),
   )
   

2. Assemble the metadata for your reference dataset. This should be a dictionary as specified in the Bioconductor metadata schema. Check out some examples from fetch_metadata(). Note that the application.takane property will be automatically added later, and so can be omitted from the list that you create.

   meta = {
        "title": "New reference dataset",
        "description": "This is a new reference dataset",
        "taxonomy_id": ["10090"],  # NCBI ID
        "genome": ["GRCm38"],  # genome build
        "sources": [{"provider": "GEO", "id": "GSE12345"}],
        "maintainer_name": "Jayaram kancherla",
        "maintainer_email": "jayaram.kancherla@gmail.com",
   }
   

3. Save your SummarizedExperiment object to disk with save_reference(). This saves the reference dataset into a "staging directory" using language-agnostic file formats - check out the ArtifactDB framework for more details.

   import tempfile
   from celldex import save_reference
   
   # replace tmp with a staging directory
   staging_dir = tempfile.mkdtemp()
   save_reference(sce, staging_dir, meta)
   

   You can check that everything was correctly saved by reloading the on-disk data for inspection:

   import dolomite_base as dl
   
   dl.read_object(staging_dir)
   

4. Wait for us to grant temporary upload permissions to your GitHub account.

5. Upload your staging directory to gypsum backend with upload_reference(). On the first call to this function, it will automatically prompt you to log into GitHub so that the backend can authenticate you. If you are on a system without browser access (e.g., most computing clusters), a token can be manually supplied via set_access_token().

   from celldex import upload_reference
   
   upload_reference(staging_dir, "my_dataset_name", "my_version")
   

   You can check that everything was successfully uploaded by calling fetch_reference() with the same name and version:

   from celldex import fetch_reference
   
   fetch_reference("my_dataset_name", "my_version")
   

   If you realized you made a mistake, no worries. Use the following call to clear the erroneous dataset, and try again:

   from gypsum_client import reject_probation
   
   reject_probation("celldex", "my_dataset_name", "my_version")
   

6. Comment on the PR to notify us that the dataset has finished uploading and you're happy with it. We'll review it and make sure everything's in order. If some fixes are required, we'll just clear the dataset so that you can upload a new version with the necessary changes. Otherwise, we'll approve the dataset. Note that once a version of a dataset is approved, no further changes can be made to that version; you'll have to upload a new version if you want to modify something.

Note

This project has been set up using PyScaffold 4.5. For details and usage information on PyScaffold see https://pyscaffold.org/.