Changelog¶

[0.2.0] - Feb 11, 2020¶

The default value of optimal_ordering is changed to False in cluster.MIRAC.

Support to sparse matrix in core data structures, including eda.SampleDistanceMatrix, eda.SampleFeatureMatrix, eda.SingleLabelClassifiedSamples, and eda.MDLSingleLabelClassifiedSamples.
Option to avoid pairwise distance computation in eda.SampleDistanceMatrix constructor by specifying use_pdist=False.
Community clustering, cluster.Community.
Community detection extended MIRAC clustering, cluster.CommunityMIRAC.
Option to build k nearest neighbor graph with approximate nearest neighbor (ANN) search using Hierarchical Navigable Small World (HNSW) graph, in eda.SampleDistanceMatrix.s_knn_graph.
Support to Python 3.7.

Fixed sdm.SampleDistanceMatrix.umap. Pass parameters to the UMAP function call.

Renamed qc module to knn, in accordance with the updated manuscript. This change clarifies the meaning of ‘quality control’ in this package, which is different from its typical meaning. In this package, quality control means to explore the data according to certain qualities of samples and features, rather than filtering the raw data according to technical parameters determined by domain specific knowledge.
Renamed qc.SampleKNNFilter to knn.RareSampleDetection, in accordance with the updated manuscript. This change clarifies the purpose of this procedure, which is to identify samples distinct from their nearest neighbors for further inspection rather than removal. Filtering usually refers to the removal of samples.
Renamed qc.FeatureKNNPickUp to knn.FeatureImputation, in accordance with the updated manuscript. This change clarifies the purpose of this procedure in the field of single-cell RNA-seq data analysis, which is to reasonably change zero entries in the data matrix into non-zero entries. This procedure is usually called ‘imputation’ in the field of single-cell RNA-seq data analysis.
Moved qc.remove_constant_features to utils.remove_constant_features.