- Documentation: https://ibm.github.io/Hestia-OOD
- Source Code: https://github.com/IBM/Hestia-OOD
- Paper pre-print: https://www.biorxiv.org/content/10.1101/2024.03.14.584508
Contents¶
Table of Contents
Installing in a conda environment is recommended. For creating the environment, please run:
conda create -n hestia python
conda activate hestia
1. Python Package¶
1.1.From PyPI¶
pip install hestia-ood
1.2. Directly from source¶
pip install git+https://github.com/IBM/Hestia-OOD
3. Optional dependencies¶
3.1. Molecular similarity¶
RDKit is a dependency necessary for calculating molecular similarities:
pip install rdkit
3.2. Sequence alignment¶
- MMSeqs2 https://github.com/steineggerlab/mmseqs2
# static build with AVX2 (fastest) (check using: cat /proc/cpuinfo | grep avx2) wget https://mmseqs.com/latest/mmseqs-linux-avx2.tar.gz; tar xvfz mmseqs-linux-avx2.tar.gz; export PATH=$(pwd)/mmseqs/bin/:$PATH # static build with SSE4.1 (check using: cat /proc/cpuinfo | grep sse4) wget https://mmseqs.com/latest/mmseqs-linux-sse41.tar.gz; tar xvfz mmseqs-linux-sse41.tar.gz; export PATH=$(pwd)/mmseqs/bin/:$PATH # static build with SSE2 (slowest, for very old systems) (check using: cat /proc/cpuinfo | grep sse2) wget https://mmseqs.com/latest/mmseqs-linux-sse2.tar.gz; tar xvfz mmseqs-linux-sse2.tar.gz; export PATH=$(pwd)/mmseqs/bin/:$PATH # MacOS brew install mmseqs2
To use Needleman-Wunch, either:
conda install -c bioconda emboss
sudo apt install emboss
- Windows: Download binaries from EMBOSS and MMSeqs2-latest
3.3. Structure alignment¶
- To use Foldseek https://github.com/steineggerlab/foldseek:
# Linux AVX2 build (check using: cat /proc/cpuinfo | grep avx2)
wget https://mmseqs.com/foldseek/foldseek-linux-avx2.tar.gz; tar xvzf foldseek-linux-avx2.tar.gz; export PATH=$(pwd)/foldseek/bin/:$PATH
# Linux SSE2 build (check using: cat /proc/cpuinfo | grep sse2)
wget https://mmseqs.com/foldseek/foldseek-linux-sse2.tar.gz; tar xvzf foldseek-linux-sse2.tar.gz; export PATH=$(pwd)/foldseek/bin/:$PATH
# Linux ARM64 build
wget https://mmseqs.com/foldseek/foldseek-linux-arm64.tar.gz; tar xvzf foldseek-linux-arm64.tar.gz; export PATH=$(pwd)/foldseek/bin/:$PATH
# MacOS
wget https://mmseqs.com/foldseek/foldseek-osx-universal.tar.gz; tar xvzf foldseek-osx-universal.tar.gz; export PATH=$(pwd)/foldseek/bin/:$PATH
Documentation ¶
1. DatasetGenerator¶
The HestiaDatasetGenerator allows for the easy generation of training/validation/evaluation partitions with different similarity thresholds. Enabling the estimation of model generalisation capabilities. It also allows for the calculation of the ABOID (Area between the similarity-performance curve (Out-of-distribution) and the In-distribution performance).
from hestia.dataset_generator import HestiaDatasetGenerator, SimilarityArguments
# Initialise the generator for a DataFrame
generator = HestiaDatasetGenerator(df)
# Define the similarity arguments (for more info see the documentation page https://ibm.github.io/Hestia-OOD/datasetgenerator)
args = SimilarityArguments(
data_type='protein', field_name='sequence',
similarity_metric='mmseqs2+prefilter', verbose=3
)
# Calculate the similarity
generator.calculate_similarity(args)
# Calculate partitions
generator.calculate_partitions(min_threshold=0.3,
threshold_step=0.05,
test_size=0.2, valid_size=0.1)
# Save partitions
generator.save_precalculated('precalculated_partitions.gz')
# Load pre-calculated partitions
generator.from_precalculated('precalculated_partitions.gz')
# Training code
for threshold, partition in generator.get_partitions():
train = df.iloc[partition['train']]
valid = df.iloc[partition['valid']]
test = df.iloc[partition['test']]
# ...
# Calculate AU-GOOD
generator.calculate_augood(results, 'test_mcc')
# Plot GOOD
generator.plot_good(results, 'test_mcc')
# Compare two models
results = {'model A': [values_A], 'model B': [values_B]}
generator.compare_models(results, statistical_test='wilcoxon')
2. Similarity calculation¶
Calculating pairwise similarity between the entities within a DataFrame df_query
or between two DataFrames df_query
and df_target
can be achieved through the calculate_similarity
function:
from hestia.similarity import sequence_similarity_mmseqs
import pandas as pd
df_query = pd.read_csv('example.csv')
# The CSV file needs to have a column describing the entities, i.e., their sequence, their SMILES, or a path to their PDB structure.
# This column corresponds to `field_name` in the function.
sim_df = sequence_similarity_mmseqs(df_query, field_name='sequence', prefilter=True)
More details about similarity calculation can be found in the Similarity calculation documentation.
3. Clustering¶
Clustering the entities within a DataFrame df
can be achieved through the generate_clusters
function:
from hestia.similarity import sequence_similarity_mmseqs
from hestia.clustering import generate_clusters
import pandas as pd
df = pd.read_csv('example.csv')
sim_df = sequence_similarity_mmseqs(df, field_name='sequence')
clusters_df = generate_clusters(df, field_name='sequence', sim_df=sim_df,
cluster_algorithm='CDHIT')
There are three clustering algorithms currently supported: CDHIT
, greedy_cover_set
, or connected_components
. More details about clustering can be found in the Clustering documentation.
4. Partitioning¶
Partitioning the entities within a DataFrame df
into a training and an evaluation subsets can be achieved through 4 different functions: ccpart
, graph_part
, reduction_partition
, and random_partition
. An example of how cc_part
would be used is:
from hestia.similarity import sequence_similarity_mmseqs
from hestia.partition import ccpart
import pandas as pd
df = pd.read_csv('example.csv')
sim_df = sequence_similarity_mmseqs(df, field_name='sequence')
train, test, partition_labs = cc_part(df, threshold=0.3, test_size=0.2, sim_df=sim_df)
train_df = df.iloc[train, :]
test_df = df.iloc[test, :]
License ¶
Hestia is an open-source software licensed under the MIT Clause License. Check the details in the LICENSE file.