jo-mengr/human_immune_health_atlas_50k_single_no_caption

Description

This dataset contains a representation of RNA sequencing data and text descriptions. Dataset type: single (suitable for relevant contrastive-learning or inference tasks).

Cell Sentence Length: The cell sentences in this dataset have a length of 4096 genes.

The RNA sequencing data used for training was originally gathered and annotated in the CellWhisperer project. It is derived from CellxGene and GEO. Detailed information on the gathering and annotation of the data can be read in the CellWhisperer Manuscript.

Example Data Row

The dataset contains the following column structure (example from the first row):

  sample_idx: 10f27ae2671711ed94878208818934b6
  cell_sentence_1: 10f27ae2671711ed94878208818934b6
  cell_sentence_2: MALAT1 IGLC2 RPS12 RPS27 RPS18 RPS8 RPS4X RPL30 RPL32 CD74 RPS27A RPS3A HLA-DRA ...
  cell_sentence_3: This profile resembles remaining cell_name cells, based on genes like MALAT1, IG...
  cell_sentence_4: This cell shows high expression of B2M, MALAT1, FTL, AIF1, FTH1, S100A4, TMSB4X,...
  share_link: https://nxc-fredato.imbi.uni-freiburg.de/s/QnWXdHwFNHHDXzc

The processed .h5ad files used to create this dataset are stored remotely. An example file can be accessed here: https://nxc-fredato.imbi.uni-freiburg.de/s/QnWXdHwFNHHDXzc

The AnnData Objects were 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: Each AnnData contained the following embedding keys: ['X_pca', 'X_scvi_fm', 'X_geneformer', 'X_gs', 'X_gs10k', 'X_geneformer-v1']. These initial embeddings are used as inputs for downstream model training / inference.

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

  GEO Database: Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository Nucleic Acids Res. 2002 Jan 1;30(1):207-10

• 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

# Load the dataset
dataset = load_dataset("jo-mengr/human_immune_health_atlas_50k_single_no_caption_v7")

Understanding the Data Structure

• sample_idx: This column maps to the adata.obs.index of the original AnnData objects
• Chunking: Larger datasets were chunked, so each AnnData object contains only a subset of the indices from the complete dataset
• Share Links: Each row contains a share_link that can be used with requests to download the corresponding AnnData object

Loading AnnData Objects

The share links in the dataset can be used to download the corresponding AnnData objects:

import requests
import anndata as ad

# Get the share link from a dataset row
row = dataset["train"][0]  # First row as example
share_link = row["share_link"]
sample_idx = row["sample_idx"]

# Download and load the AnnData object
response = requests.get(share_link)
if response.status_code == 200:
    with open("adata.h5ad", "wb") as f:
        f.write(response.content)
    adata = ad.read_h5ad("adata.h5ad")

    # The sample_idx corresponds to adata.obs.index
    sample_data = adata[adata.obs.index == sample_idx]
    print(f"Found sample: {sample_data.shape}")
else:
    print("Failed to download AnnData object")