afcluster 0.1.1

Cluster Multiple-Sequence Alignments (MSA) using DBSCAN

AFCluster-api

A Python-API version of AF_Cluster by Wayment-Steele et al. (2023) In their original GitHub repository they include scripts to perform MSA clustering but do not have a functional API interface that easily allows integrating their workflow into custom settings. This project adapts and refactors their original ClusterMSA.py script into a API format.

Installation

The AFCluster-api version can be install via pip using

pip install afcluster

Usage

The core of the API is the AFCluster object which unifies the complete functionality of the package under one hood, including:

• performing DBSCAN with a fixed epsilon value
• performing gridsearch for a suitable epsilon value
• writing a3m output files for identified clusters
• writing a cluster metadata table file (csv)
• plotting PCA or t-SNE for the clustering results

The AFCluster class accepts sequence inputs as either

• list of strings
• pandas dataframe with "sequence" columne
• pandas series of strings in any case the first element is interpreted as the query sequence!

For example:

>>> from afcluster import AFCluster, read_a3m

# load an MSA into a pandas dataframe
>>> msa = read_a3m("tests/test.a3m")
>>> print(msa.head())
                                              header                                           sequence
0                                               >101  MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFA...
1  >UniRef100_A0A964YKG2\t118\t0.907\t2.876E-28\t...  MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFA...
2  >UniRef100_A0A177EKP9\t117\t0.894\t3.948E-28\t...  MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFA...
3  >UniRef100_UPI002231809B\t117\t0.907\t3.948E-2...  MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFA...
4  >UniRef100_A0A6C0JTL4\t117\t0.894\t5.421E-28\t...  MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFA...


# now determine an epsilon value for clustering
# (using multiprocessing for spped)
>>> clusterer = AFCluster()
>>> eps = clusterer.gridsearch_eps(msa)
>>> print(f"determined {eps=}")
determined eps=8.0

# now we can cluster (the clusterer remembers the determined epsilon value)
# we also determine the consensus sequence for each cluster and compute
# levenshtein distances (as 1-d !!!) to the query and consensus sequences 
>>> out_df = clusterer.cluster(msa, consensus_sequence=True, levenshtein=True)
>>> print(out_df.head())
                                            sequence  cluster_id  ... levenshtein_query levenshtein_consensus
0  MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFA...          -1  ...          1.000000              0.697368
1  MQVFIKTLTGKTITLDVEPSDTIESVKQKIQDKEGIPPDQQRLIFA...           0  ...          0.907895              0.907895
2  MQIFVKTLTGKTITLDVENSDTVDTVKTKIQDKEGIPPDQQRLIFA...           0  ...          0.894737              0.894737
3  MQIFVKTLTGKTVTLDVDPSDTIENVKAMIQDKEGIPPDQQRLIFA...           0  ...          0.907895              0.907895
4  MQIFVKTLTGKTITLEVDPSNTIETVKQMIQDKEGIPPDQQRLIYA...           0  ...          0.894737              0.894737

# now generate a PCA plot
>>> ax = clusterer.pca() # ax is a matplotlib.Axes object

# and write a3m output files for each cluster to an output directory
>>> clusterer.write_a3m("clustered_results")
>>> clusterer.write_cluster_table("clustered_results/clusters.csv")

The PCA plot generated with clusterer.pca(). A difference to the original implementation is that we do not simply highlight the first 10 (default) clusters but the 10 biggest clusters instead.

Comparison to the Original

Since we did some code refactoring, we also benchmarked the performance of our implementation versus Wayment-Steele et al.'s original version. We found that our refactored code base runs in roughly half the time that the original implementation required. A workflow invloving a gridsearch for a suitable epsilon value even ran three times faster compared to the original ClusterMSA.py script.

method	task	time (s)
ours	cluster + pca	9.7
original	cluster + pca	17.26
ours	search eps + cluster + pca	29.65
original	search eps + cluster + pca	102.37
ours	cluster + pca + tsne	56.44
original	cluster + pca + tsne	129.55
ours	search eps + cluster + pca + tsne	98.22
original	search eps + cluster + pca + tsne	153.63

Computation times are averages. Performance measures were computed using the scripts in the support/performance directory, on an M4 Macbook Pro (2024) using 5 repeats each.