A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data

Explaining the optimistic performance evaluation of newly proposed methods: A cross-design validation experiment

The constant development of new data analysis methods in many fields of research is accompanied by an increasing awareness that these new methods often perform better in their introductory paper than in subsequent comparison studies conducted by other researchers. We attempt to explain this discrepancy by conducting a systematic experiment that we call "cross-design validation of methods". In the experiment, we select two methods designed for the same data analysis task, reproduce the results shown in each paper, and then reevaluate each method based on the study design (i.e., datasets, competing methods, and evaluation criteria) that was used to show the abilities of the other method. We conduct the experiment for two data analysis tasks, namely cancer subtyping using multiomic data and differential gene expression analysis. Three of the four methods included in the experiment indeed perform worse when they are evaluated on the new study design, which is mainly caused by the different datasets. Apart from illustrating the many degrees of freedom existing in the assessment of a method and their effect on its performance, our experiment suggests that the performance discrepancies between original and subsequent papers may not only be caused by the nonneutrality of the authors proposing the new method but also by differences regarding the level of expertise and field of application. Authors of new methods should thus focus not only on a transparent and extensive evaluation but also on comprehensive method documentation that enables the correct use of their methods in subsequent studies.

scMEB: a fast and clustering-independent method for detecting differentially expressed genes in single-cell RNA-seq data

BACKGROUND

Cell clustering is a prerequisite for identifying differentially expressed genes (DEGs) in single-cell RNA sequencing (scRNA-seq) data. Obtaining a perfect clustering result is of central importance for subsequent analyses, but not easy. Additionally, the increase in cell throughput due to the advancement of scRNA-seq protocols exacerbates many computational issues, especially regarding method runtime. To address these difficulties, a new, accurate, and fast method for detecting DEGs in scRNA-seq data is needed.

RESULTS

Here, we propose single-cell minimum enclosing ball (scMEB), a novel and fast method for detecting single-cell DEGs without prior cell clustering results. The proposed method utilizes a small part of known non-DEGs (stably expressed genes) to build a minimum enclosing ball and defines the DEGs based on the distance of a mapped gene to the center of the hypersphere in a feature space.

CONCLUSIONS

We compared scMEB to two different approaches that could be used to identify DEGs without cell clustering. The investigation of 11 real datasets revealed that scMEB outperformed rival methods in terms of cell clustering, predicting genes with biological functions, and identifying marker genes. Moreover, scMEB was much faster than the other methods, making it particularly effective for finding DEGs in high-throughput scRNA-seq data. We have developed a package scMEB for the proposed method, which could be available at https://github.com/FocusPaka/scMEB .

Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods

Analysis of differential gene expression from RNA-seq data has become a standard for several research areas. The steps for the computational analysis include many data types and file formats, and a wide variety of computational tools that can be applied alone or together as pipelines. This paper presents a review of the differential expression analysis pipeline, addressing its steps and the respective objectives, the principal methods available in each step, and their properties, therefore introducing an organized overview to this context. This review aims to address mainly the aspects involved in the differentially expressed gene (DEG) analysis from RNA sequencing data (RNA-seq), considering the computational methods. In addition, a timeline of the computational methods for DEG is shown and discussed, and the relationships existing between the most important computational tools are presented by an interaction network. A discussion on the challenges and gaps in DEG analysis is also highlighted in this review. This paper will serve as a tutorial for new entrants into the field and help established users update their analysis pipelines.