Loading Datasets

The hdxms_datasets package features a central DataVault object, which is used to fetch datasets from an online database to a local cache dir, as well as parse those locally saved peptide sets into a pandas DataFrame.

Basic usage

from hdxms_datasets import DataVault

# Creating a DataVault without giving a cache path name uses $home/.hdxms_datasets by default
vault = DataVault()

# Download a remote dataset to the local cache
vault.fetch_dataset("20221007_1530_SecA_Krishnamurthy")

# Load the dataset
ds = vault.load_dataset("20221007_1530_SecA_Krishnamurthy")

# Print a string describing the states in the dataset
print(ds.describe())

# Load FD control peptides as a pandas DataFrame
fd_control = ds.load_peptides(0, "FD_control") 

# Load experimental peptides as pandas dataframe
peptides = ds.load_peptides(0, 'experiment')

The code above initiates a DataVault object, thereby creating a cache directory in the default location (~/.hdxms_datasets/datasets) if it does not yet exist. Then the dataset "20221007_1530_SecA_Krishnamurthy" is fetched from the database and stored in the cache dir.

Datavault.load_dataset loads the dataset which is returned as HDXDataSet object. From the HDXDataSet object, users can load peptides from the available states. In the example, the Fully Deuterated control peptides are loaded from the first HDX state as a pandas DataFrame. The experimental peptides are loaded in the same way.