Subject clustering (i.e., the use of measured features to cluster subjects,
such as patients or cells, into multiple groups) is a problem of great
interest. In recent years, many approaches were proposed, among which
unsupervised deep learning (UDL) has received a great deal of attention. Two
interesting questions are (a) how to combine the strengths of UDL and other
approaches, and (b) how these approaches compare to one other.
We combine Variational Auto-Encoder (VAE), a popular UDL approach, with the
recent idea of Influential Feature PCA (IF-PCA), and propose IF-VAE as a new
method for subject clustering. We study IF-VAE and compare it with several
other methods (including IF-PCA, VAE, Seurat, and SC3) on $10$ gene microarray
data sets and $8$ single-cell RNA-seq data sets. We find that IF-VAE
significantly improves over VAE, but still underperforms IF-PCA. We also find
that IF-PCA is quite competitive, which slightly outperforms Seurat and SC3
over the $8$ single-cell data sets. IF-PCA is conceptually simple and permits
delicate analysis. We demonstrate that IF-PCA is capable of achieving the phase
transition in a Rare/Weak model. Comparatively, Seurat and SC3 are more complex
and theoretically difficult to analyze (for these reasons, their optimality
remains unclear).

本文提出了一种将 VAE 和 IF‐PCA 相结合的新方法 IF-VAE，应用于受试者聚类问题，实验结果表明，IF-VAE 在某些情况下比 VAE 具有更好的表现，但是 IF-PCA 仍然是更优秀的选择。

通过 IF-PCA 和几种最近的方法进行主题聚类

Subject clustering by IF-PCA and several recent methods

We present a novel coupled two-way clustering approach to gene microarray
data analysis. The main idea is to identify subsets of the genes and samples,
such that when one of these is used to cluster the other, stable and
significant partitions emerge. The search for such subsets is a computationally
complex task: we present an algorithm, based on iterative clustering, which
performs such a search. This analysis is especially suitable for gene
microarray data, where the contributions of a variety of biological mechanisms
to the gene expression levels are entangled in a large body of experimental
data. The method was applied to two gene microarray data sets, on colon cancer
and leukemia. By identifying relevant subsets of the data and focusing on them
we were able to discover partitions and correlations that were masked and
hidden when the full dataset was used in the analysis. Some of these partitions
have clear biological interpretation; others can serve to identify possible
directions for future research.

本文介绍了一种新颖的双向耦合聚类方法，用于基因芯片数据的分析：通过基于迭代聚类的算法，找到适合聚类的基因和样本的子集，从而发现在纯数据聚类时被掩盖和隐藏的新的数据分区和相关性；本方法被应用于两个基因芯片数据集，包括结肠癌和白血病数据集。