Fast and interpretable consensus clustering via minipatch learning.

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 lead 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.

Correlation Imputation for Single-Cell RNA-seq.

Recent advances in single-cell RNA sequencing (scRNA-seq) technologies have yielded a powerful tool to measure gene expression of individual cells. One major challenge of the scRNA-seq data is that it usually contains a large amount of zero expression values, which often impairs the effectiveness of downstream analyses. Numerous data imputation methods have been proposed to deal with these "dropout" events, but this is a difficult task for such high-dimensional and sparse data. Furthermore, there have been debates on the nature of the sparsity, about whether the zeros are due to technological limitations or represent actual biology. To address these challenges, we propose Single-cell RNA-seq Correlation completion by ENsemble learning and Auxiliary information (SCENA), a novel approach that imputes the correlation matrix of the data of interest instead of the data itself. SCENA obtains a gene-by-gene correlation estimate by ensembling various individual estimates, some of which are based on known auxiliary information about gene expression networks. Our approach is a reliable method that makes no assumptions on the nature of sparsity in scRNA-seq data or the data distribution. By extensive simulation studies and real data applications, we demonstrate that SCENA is not only superior in gene correlation estimation, but also improves the accuracy and reliability of downstream analyses, including cell clustering, dimension reduction, and graphical model estimation to learn the gene expression network.

Correlation Imputation in Single cell RNA-seq using Auxiliary Information and Ensemble Learning.

Single cell RNA sequencing is a powerful technique that measures the gene expression of individual cells in a high throughput fashion. However, due to sequencing inefficiency, the data is unreliable due to dropout events, or technical artifacts where genes erroneously appear to have zero expression. Many data imputation methods have been proposed to alleviate this issue. Yet, effective imputation can be difficult and biased because the data is sparse and high-dimensional, resulting in major distortions in downstream analyses. In this paper, we propose a completely novel approach that imputes the gene-by-gene correlations rather than the data itself. We call this method SCENA: Single cell RNA-seq Correlation completion by ENsemble learning and Auxiliary information. The SCENA gene-by-gene correlation matrix estimate is obtained by model stacking of multiple imputed correlation matrices based on known auxiliary information about gene connections. In an extensive simulation study based on real scRNA-seq data, we demonstrate that SCENA not only accurately imputes gene correlations but also outperforms existing imputation approaches in downstream analyses such as dimension reduction, cell clustering, graphical model estimation.