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README.md

@@ -1,23 +1,112 @@
 ---
-dataset_info:
-  features:
-  - name: sample_idx
-    dtype: string
-  - name: cell_sentence_1
-    dtype: string
-  - name: cell_sentence_2
-    dtype: string
-  - name: adata_link
-    dtype: string
-  splits:
-  - name: test
-    num_bytes: 2663909161
-    num_examples: 94836
-  download_size: 1431179464
-  dataset_size: 2663909161
-configs:
-- config_name: default
-  data_files:
-  - split: test
-    path: data/test-*
+language:
+  - code
+pretty_name: "Transcriptome with text annotations - paired dataset"
+tags:
+  - multimodal
+  - omics
+  - sentence-transformers
+  - anndata
+license: "mit"
+task_categories:
+  - zero-shot-classification
 ---
+
+## 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 $cs_length 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: AGACCATTCAAACCTG_TSP2_Trachea_NA_10X_1_1
+  cell_sentence_1: AGACCATTCAAACCTG_TSP2_Trachea_NA_10X_1_1
+  cell_sentence_2: MALAT1 MT-RNR2 MT-RNR1 NEAT1 S100A2 KRT19 MT-CO2 S100A9 S100A6 KRT5 KRT17 KRT6A SFN FABP5 AQP3 MT-ND1 PERP MT-ND2 WFDC2 HSP90AA1 SERPINB3 SAT1 B2M TAC...
+  adata_link: https://zenodo.org/api/records/17721879/draft/files/all_chunk_0.zarr.zip/content
+```
+
+The processed .h5ad files used to create this dataset are stored remotely. An example file can be accessed here: https://zenodo.org/api/records/17721879/draft/files/all_chunk_0.zarr.zip/content
+
+The AnnData Objects were processed and converted into a Hugging Face dataset using the [adata_hf_datasets](https://github.com/mengerj/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](https://github.com/mengerj/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_gs10k', 'X_geneformer-v1', 'X_cw-geneformer'].
+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](https://doi.org/10.1101/2023.10.30.563174)
+
+  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](https://doi.org/10.1101/2024.10.15.618501)
+  Annotated Data: [CellWhisperer website](https://cellwhisperer.bocklab.org/)
+- **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](https://doi.org/10.1038/s41586-023-06139-9)
+- **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](https://doi.org/10.1101/2021.12.16.473007)
+  scnapy: _Wolf, F., Angerer, P. & Theis, F. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19, 15 (2018)._
+  [Publication](https://doi.org/10.1186/s13059-017-1382-0)
+
+## Usage
+
+To use this dataset in Python:
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("jo-mengr/tabula_sapiens_schafer_single_no_caption_v2")
+```
+
+### 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:
+
+```python
+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")
+```