  Consensus clustering has been widely used in bioinformatics and other
applications to improve the accuracy, stability and reliability of clustering
results. This approach ensembles cluster co-occurrences from multiple
clustering runs on subsampled observations. For application to large-scale
bioinformatics data, such as to discover cell types from single-cell sequencing
data, for example, consensus clustering has two significant drawbacks: (i)
computational inefficiency due to repeatedly applying clustering algorithms,
and (ii) lack of interpretability into the important features for
differentiating clusters. In this paper, we address these two challenges by
developing IMPACC: Interpretable MiniPatch Adaptive Consensus Clustering. Our
approach adopts three major innovations. We ensemble cluster co-occurrences
from tiny subsets of both observations and features, termed minipatches, thus
dramatically reducing computation time. Additionally, we develop adaptive
sampling schemes for observations, which result in both improved reliability
and computational savings, as well as adaptive sampling schemes of features,
which leads to interpretable solutions by quickly learning the most relevant
features that differentiate clusters. We study our approach on synthetic data
and a variety of real large-scale bioinformatics data sets; results show that
our approach not only yields more accurate and interpretable cluster solutions,
but it also substantially improves computational efficiency compared to
standard consensus clustering approaches.
