(data-format)=

# Data Format

## ST Data

The input ST data must be an h5ad file containing at least the gene expression matrix and spatial coordinates. The gene expression matrix should be stored in the `layers` attribute, and the spatial coordinates should be in the obsm `attribute` with the key `spatial`. Optionally, the h5ad file may include spot (cell) annotations in the obs attribute.

```python
import scanpy as sc

adata = sc.read_h5ad("gsMap_example_data/ST/E16.5_E1S1.MOSTA.h5ad")

print(adata.layers["count"].shape)
print(adata.obsm["spatial"].shape)
print(adata.obs["annotation"].value_counts().head())
```

## GWAS Data

The input GWAS data is a text file containing at least the columns for SNP (rs number), Z (Z-statistics), and N (sample size). Column headers are keywords used by gsMap.

```shell
zcat gsMap_example_data/GWAS/IQ_NG_2018.sumstats.gz | head -n 5

SNP  A1 A2 Z N
rs12184267 T C 0.916 225955
rs12184277 G A 0.656 226215
rs12184279 A C 1.050 226224
rs116801199 T G 0.300 226626
```

### How to format the GWAS data

You can convert GWAS summary data into the required format using custom code. For convenience, gsMap provides a command to do this. Below is an example of how to use the command.

Download the human height GWAS data and decompress it.

```bash
wget https://portals.broadinstitute.org/collaboration/giant/images/4/4e/GIANT_HEIGHT_YENGO_2022_GWAS_SUMMARY_STATS_ALL.gz

gzip -d GIANT_HEIGHT_YENGO_2022_GWAS_SUMMARY_STATS_ALL.gz
```

Convert the summary statistics to the required format.

```bash
gsmap format_sumstats \
--sumstats 'GIANT_HEIGHT_YENGO_2022_GWAS_SUMMARY_STATS_ALL' \
--out 'HEIGHT'
```

You will obtain a file named HEIGHT.sumstats.gz

```bash
zcat HEIGHT.sumstats.gz | head -n 5

SNP  A1 A2 Z N
rs3131969 G A 0.328 1494218.000
rs3131967 C T 0.386 1488150.000
rs12562034 A G 1.714 1554976.000
rs4040617 G A -0.463 1602016.000
```

For more usage options, please refer to:

```bash
gsMap format_sumstats -h
```