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Single-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. In practice, single-cell multi-omics sequencing actually detects multiple traits such as DNA, RNA, methylation information and/or protein profiles from the same cell for many individuals in a tissue sample. Multi-omics sequencing has been widely applied to systematically unravel interplay mechanisms of key components and pathways in cell. This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic. We also summarize machine learning and bioinformatics techniques used in the analysis of the intercorrelated multilayer heterogeneous data. We observed that variational inference and graph-based learning are popular approaches, and Seurat V3 is a commonly used tool to transfer the missing variables and labels. We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods. The survey is concluded with some open questions and opportunities for this extraordinary field.

State-of-the-art assessment method of speech-transmission quality (e.g., PESQ or TOSQA) predict the mean-opinion score (MOS) quite accurately, but cannot provide diagnostic information, which is, however, highly desirable for system developers. In our research project, we aim at the development of an attribute-based speech-quality measure, which provides estimates of different attributes of speech samples and then maps them to one integral-quality estimate. Three dominant, mutually orthogonal perceptual dimensions were firstly identified by auditory experiments and multidimensional analysis (MDA) for narrow-band speech transmission: “directness/ frequency content,” “continuity,” and “noisiness.” The present paper focuses on the further decomposition and measurement of the global dimension “Noisiness.” Therefore, an auditory test including samples degraded by different kinds of noises has been conducted. The subsequent MDA indicates that at least two sub-dimensions (SD), “Speech Contamination” and (perceived) “Additive-Noise Level,” are further describing the global dimension “Noisiness.” The first SD characterizes the degree the noise distorts the speech signal as such, whereas the second SD reflects the degree the additive circuit or background noise itself annoys the listener. The instrumental estimation methods for both SDs and the mapping to the integral-quality ratings are presented in this paper.