RESUMO
Various authors have put their sincere efforts into proposing ratio estimators for estimating the population's mean and variance under different situations and sampling methods. But the problem arises when data is unstable, imprecise, ambiguous, incomplete and vague. In such situations, classical methods of estimation do not yield precise results, as these methods are not meant for such problems. Given this difficulty, Neutrosophic statistics are the only alternative as it deals with indeterminacy. So in this study, we have proposed a generalized Neutrosophic robust ratio type estimator which can be used to provide good results in such situations, as well as in the case of the presence of outliers. For the evaluation point of view, we have made use of real data set and simulation study to check the efficacy of our suggested estimators over the mentioned existed estimators.
RESUMO
The study of cellular decision-making can be approached comprehensively using multimodal single-cell omics technology. Recent advances in multimodal single-cell technology have enabled simultaneous profiling of more than one modality from the same cell, providing more significant insights into cell characteristics. However, learning the joint representation of multimodal single-cell data is challenging due to batch effects. Here we present a novel method, scJVAE (single-cell Joint Variational AutoEncoder), for batch effect removal and joint representation of multimodal single-cell data. The scJVAE integrates and learns joint embedding of paired scRNA-seq and scATAC-seq data modalities. We evaluate and demonstrate the ability of scJVAE to remove batch effects using various datasets with paired gene expression and open chromatin. We also consider scJVAE for downstream analysis, such as lower dimensional representation, cell-type clustering, and time and memory requirement. We find scJVAE a robust and scalable method outperforming existing state-of-the-art batch effect removal and integration methods.