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Latest version: v0.2.0

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0.2.0

----------------------

Changed
~~~~~~~

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

Added
~~~~~

- 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.

0.1.7

---------------------

Changed
~~~~~~~

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

0.1.6

---------------------

Added
~~~~~

- Changelog to keep track of changes in each update.

Changed
~~~~~~~

- 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``.

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