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---
tags:
- biology
- rna
- gene expression
pretty_name: YolkSac Human
size_categories:
- 10K<n<100K
license: cc0-1.0
---
## Human fetal yolk sac scRNA-seq data
The yolk sac (YS) generates the first blood and immune cells and provides nutritional and metabolic support to the developing embryo. Our current understanding of its functions derives from pivotal studies in model systems, and insights from human studies are limited. Single-cell genomics technologies have facilitated the interrogation of human developmental tissues at unprecedented resolution. Atlases of blood and immune cells from multiple organs have been greatly enhanced by focused, time-resolved analyses of specific tissues.
### Project information
- **Source:** Human fetal yolk sac scRNA-seq data
- Female donor, Carnegie Stage 17
- **Sample ID:**
- F158 for Haniffa Lab
- 16099 for HDBR
- **Raw file:**
- [Array Express (E-MTAB-11673)](https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-11673)
- [17_04_24_YolkSacRaw_F158_WE_annots.h5ad](https://www.ebi.ac.uk/biostudies/files/E-MTAB-11673/17_04_24_YolkSacRaw_F158_WE_annots.h5ad)
- **Species**
- Homo sapiens (Human)
### Assay details
- **Assay:** 10X 5' transcription profiling
- **Number of Cells:** 31,680
- **Number of Genes:** 37,318
### Load the data
Load the dataset using Huggingface's optimized library and create the AnnData object:
```
from datasets import load_dataset
from scipy.sparse import lil_matrix
ds = load_dataset("helical-ai/yolksac_human",trust_remote_code=True, split="train[:65%]",download_mode="reuse_cache_if_exists")
```
```
observation_columns = [obs for obs in list(ds.features.keys()) if not obs == 'raw_counts']
obs_data = pd.DataFrame(ds.select_columns(observation_columns).data.to_pandas(),columns=observation_columns)
lil = lil_matrix((len(ds),ds[0]['size']))
lil.data = np.array(ds['raw_counts'],dtype="object")
lil.rows = np.array(ds['rows'],dtype="object")
ann_data = ad.AnnData(lil.tocsr(),obs=obs_data)
ann_data.var_names = ds.features['raw_counts'].id.split(",")
```
### Cell type annotation
There are three levels of cell annotation you can access in the data:
| Annotation Granularity Level | Cell Type Names |
|------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Level 1 `(LVL1)` | ERYTHROID, LYMPHOID, MK, MYELOID, PROGENITOR, STROMA |
| Level 2 `(LVL2)` | B_CELL, CMP, DC, ELP, ENDODERM, ENDOTHELIUM, EOSINOPHIL_BASOPHIL, ERYTHROID, FIBROBLAST, HSPC, ILC, LMPP, MACROPHAGE, MAST_CELL, MEMP, MESOTHELIUM, MK, MONO MAC DC2, MONO MAC PRE DC2, MONOCYTE, MONOCYTE_MACROPHAGE, MONOCYTE_YS_1, MOP, NEUTROPHIL_PRECURSOR, NK, PDC PRECURSOR, PROMONOCYTE, SMOOTH_MUSCLE |
| Level 3 `(LVL3)` | CMP, DC2_CYCLING, DEF_HSPC_1, DEF_HSPC_2, EARLY_ERYTHROID, ELP, ENDODERM, ENDOTHELIUM_AEC, ENDOTHELIUM_PROLIF_AEC, ENDOTHELIUM_PROLIF_SINUSOIDAL_EC, ENDOTHELIUM_SINUSOIDAL, ENDOTHELIUM_VWF, EO_BASO_MAST_PRECURSOR, ERYTHROID, FIBROBLAST, HE, HSPC_1, HSPC_2, ILC_PRECURSOR, IMMATURE_ENDOTHELIUM, LMPP, LYMPHOID B LIN, MAC DC2, MACROPHAGE_IRON_RECYCLING, MACROPHAGE_KUPFFER_LIKE, MACROPHAGE_LYVE1_HIGH, MACROPHAGE_MICROGLIA, MACROPHAGE_PRE_MAC, MACROPHAGE_PROLIFERATING, MAST_CELL, MEMP, MESOTHELIUM, MID_ERYTHROID, MK, MONO MAC DC2, MONO MAC PRE DC2, MONOCYTE_0, MONOCYTE_1, MONOCYTE_MACROPHAGE, MOP, NEUTROPHIL_PRECURSOR, NK, PDC PRECURSOR, PRE DC2, PRIM_HSPC_1, PRIM_HSPC_2, PROMONOCYTE, SMOOTH_MUSCLE |
### License information
Data was published in EMBL-EBI (Array Express), CC0.
This dataset was used in [Goh et al, Science, 2023](https://www.science.org/doi/10.1126/science.add7564) and the article is licensed under a [BY 4.0](https://creativecommons.org/licenses/by/4.0/) International license.
### Note:
This dataset contains one donor only, and it will be updated in the near future to contain more donors! |