Meta-analysis is the quantitative, scientific synthesis of research results. Fixed-effect and random-effect models are two popular statistical models for meta-analysis. The selection of the appropriate model is crucial. In this paper, we introduce some noval views of models and explain key assumptions, hypothesis, and interpretation of each model. We conclude with a discussion of factors to consider in model selection, and provide a recommendation on selection of appropriate statistical models for traditional meta-analysis.
Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.