Linear-time cluster ensembles of large-scale single-cell RNA-seq and multimodal data

A fundamental task in single-cell RNA-seq (scRNA-seq) analysis is the identification of transcriptionally distinct groups of cells. Numerous methods have been proposed for this problem, with a recent focus on methods for the cluster analysis of ultra-large scRNA-seq data sets produced by droplet-based sequencing technologies. Most existing methods rely on a sampling step to bridge the gap between algorithm scalability and volume of the data. Ignoring large parts of the data, however, often yields inaccurate groupings of cells and risks overlooking rare cell types. We propose method Specter that adopts and extends recent algorithmic advances in (fast) spectral clustering. In contrast to methods that cluster a (random) subsample of the data, we adopt the idea of landmarks that are used to create a sparse representation of the full data from which a spectral embedding can then be computed in linear time. We exploit Specter’s speed in a cluster ensemble scheme that achieves a substantial improvement in accuracy over existing methods and that is sensitive to rare cell types. Its linear time complexity allows Specter to scale to millions of cells and leads to fast computation times in practice. Furthermore, on CITE-seq data that simultaneously measures gene and protein marker expression we demonstrate that Specter is able to utilize multimodal omics measurements to resolve subtle transcriptomic differences between subpopulations of cells. Specter is open source and available at https://github.com/canzarlab/Specter.

RESEARCH OF ALPHA-PARTICLE TRANSFER REACTIONS

This paper presents the capabilities of nuclear physics for the development of nuclear cluster physics. Frequencies of several nucleons, acting as component clusters, are one of the key aspects of the nuclear device. Therefore, the basis for the clustering was helium nuclei (α-particles). This article explores the different types of cluster ensembles and different types of unmanaged nuclei as the fundamental components of new quantum environments. The experimental results of the α-particle transfer reactions under the complete synthesis of some of the cluster nuclei show the formula for those reactions. Specifically, this work focuses on the so-called "cluster nucleus model" - a model that describes the excited states of the nuclei, ie helium nuclei. One of the most important goals of this work is to carefully analyze the author's available data on research of alpha-particle reactions and to compare them with today's promising models of nuclei, to identify and explain new patterns. The author of the article received experimental data from various publications in journal articles and "personal contact". Relevant sources in the article have reference to sources.

Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning

Motivation In recent years, single-cell RNA sequencing enables us to discover cell types or even subtypes. Its increasing availability provides opportunities to identify cell populations from single-cell RNA-seq data. Computational methods have been employed to reveal the gene expression variations among multiple cell populations. Unfortunately, the existing ones can suffer from realistic restrictions such as experimental noises, numerical instability, high dimensionality and computational scalability. Results We propose an evolutionary multiobjective ensemble pruning algorithm (EMEP) that addresses those realistic restrictions. Our EMEP algorithm first applies the unsupervised dimensionality reduction to project data from the original high dimensions to low-dimensional subspaces; basic clustering algorithms are applied in those new subspaces to generate different clustering results to form cluster ensembles. However, most of those cluster ensembles are unnecessarily bulky with the expense of extra time costs and memory consumption. To overcome that problem, EMEP is designed to dynamically select the suitable clustering results from the ensembles. Moreover, to guide the multiobjective ensemble evolution, three cluster validity indices including the overall cluster deviation, the within-cluster compactness and the number of basic partition clusters are formulated as the objective functions to unleash its cell type discovery performance using evolutionary multiobjective optimization. We applied EMEP to 55 simulated datasets and seven real single-cell RNA-seq datasets, including six single-cell RNA-seq dataset and one large-scale dataset with 3005 cells and 4412 genes. Two case studies are also conducted to reveal mechanistic insights into the biological relevance of EMEP. We found that EMEP can achieve superior performance over the other clustering algorithms, demonstrating that EMEP can identify cell populations clearly. Availability and implementation EMEP is written in Matlab and available at https://github.com/lixt314/EMEP Supplementary information Supplementary data are available at Bioinformatics online.