jo-mengr/Immune_ALL_human_single

Description

This dataset contains RNA sequencing data (or actually references to anndata files stored on a cloud). It is a dataset supposed to be used for testing and held-out from training.

The Test data is partly taken from the cellwhisperer project (bowel disease dataset) and from Luecken et. al. It was processed and converted into a Hugging Face dataset using the adata_hf_datasets Python package. The dataset can be used to train a multimodal model, aligning transcriptome and text modalities with the sentence-transformers framework. See mmcontext for examples on how to train such a model.

The anndata objects are stored on nextcloud and a sharelink is provided as part of the dataset to download them. These anndata objects contain intial embeddings generated like this: Embeddings were generated with the InitialEmbedder class for methods: ['hvg', 'pca']. Each method stored its embeddings in the corresponding adata.obsm key. These initial embeddings are used as inputs for downstream model inference. $caption_info

Source

• Original Data: CZ CELLxGENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data CZI Single-Cell Biology, et al. bioRxiv 2023.10.30 Publication

  Bowel Disease: Parikh, Kaushal, Agne Antanaviciute, David Fawkner-Corbett, Marta Jagielowicz, Anna Aulicino, Christoffer Lagerholm, Simon Davis, et al. 2019. “Colonic Epithelial Cell Diversity in Health and Inflammatory Bowel Disease.” Nature 567 (7746): 49–55 GEO accession

  Other Test Data: Luecken, Malte D., M. Büttner, K. Chaichoompu, A. Danese, M. Interlandi, M. F. Mueller, D. C. Strobl, et al. “Benchmarking Atlas-Level Data Integration in Single-Cell Genomics.” Nature Methods 19, no. 1 (January 2022): 41–50. Publication.

• Annotated Data: Cell Whisperer: Multimodal learning of transcriptomes and text enables interactive single-cell RNA-seq data exploration with natural-language chats Moritz Schaefer, Peter Peneder, Daniel Malzl, Mihaela Peycheva, Jake Burton, Anna Hakobyan, Varun Sharma, Thomas Krausgruber, Jörg Menche, Eleni M. Tomazou, Christoph Bock Publication Annotated Data: CellWhisperer website

• Embedding Methods: scVI: Lopez, R., Regier, J., Cole, M.B. et al. Deep generative modeling for single-cell transcriptomics. Nat Methods 15, 1053–1058 (2018). https://doi.org/10.1038/s41592-018-0229-2 geneformer: Theodoris, C.V., Xiao, L., Chopra, A. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023). Publication

• Further important packages anndata: Isaac Virshup, Sergei Rybakov, Fabian J. Theis, Philipp Angerer, F. Alexander Wolf. anndata: Annotated data. bioRxiv 2021.12.16.473007 Publication scnapy: Wolf, F., Angerer, P. & Theis, F. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19, 15 (2018). Publication

Usage

To use this dataset in Python:

from datasets import load_dataset

dataset = load_dataset("jo-mengr/Immune_ALL_human_single")

The anndata reference is a json string which contains a share_link to the remotly stored anndata object. It can be obtained like this:

import json
import anndata
import requests

# hf makes a
adata_ref = json.loads(dataset["train]["anndata_ref"][0])
share_link = adata_ref["file_path"]
sample_id = adata_ref["sample_id"]
save_path = "data"
response = requests.get(share_link)
if response.status_code == 200:
  # Write the content of the response to a local file
  with open(save_path, "wb") as file:
    file.write(response.content)
else:
  print("Failed to read data from share link.")

adata = anndata.read_h5ad(save_path)
# The dataset contains several pre-computed embeddings. Lets for example get the embeddings computed with "scvi":
sample_idx = adata.obs.index == sample_id
sample_embedding = adata.obsm["X_scvi"][sample_idx]
# This sample embedding is described the the caption (loaded above)
# Note that you can cache your anndata files so you don't need to reload the anndata object if the filepath is still the same.