Clustering single-cell multimodal omics data with jrSiCKLSNMF.

Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise. Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") that extracts latent factors shared across omics modalities within the same set of single cells. Results: We compare our clustering algorithm to several existing methods on four sets of data simulated from third party software. We also apply our algorithm to a real set of cell line data. Discussion: We show overwhelmingly better clustering performance than several existing methods on the simulated data. On a real multimodal omics dataset, we also find our method to produce scientifically accurate clustering results.

SAREV: A review on statistical analytics of single-cell RNA sequencing data.

Due to the development of next-generation RNA sequencing (NGS) technologies, there has been tremendous progress in research involving determining the role of genomics, transcriptomics and epigenomics in complex biological systems. However, scientists have realized that information obtained using earlier technology, frequently called 'bulk RNA-seq' data, provides information averaged across all the cells present in a tissue. Relatively newly developed single cell (scRNA-seq) technology allows us to provide transcriptomic information at a single-cell resolution. Nevertheless, these high-resolution data have their own complex natures and demand novel statistical data analysis methods to provide effective and highly accurate results on complex biological systems. In this review, we cover many such recently developed statistical methods for researchers wanting to pursue scRNA-seq statistical and computational research as well as scientific research about these existing methods and free software tools available for their generated data. This review is certainly not exhaustive due to page limitations. We have tried to cover the popular methods starting from quality control to the downstream analysis of finding differentially expressed genes and concluding with a brief description of network analysis.

Factors affecting live birth rates in donor oocytes from commercial egg banks vs. program egg donors: an analysis of 40,485 cycles from the Society for Assisted Reproductive Technology registry in 2016-2018.

OBJECTIVE: To examine the differences in live birth rates (LBRs), with single embryo transfer (SET), using oocytes from program generated egg donors vs. commercial egg bank donors and other factors affecting LBRs using donor oocytes. DESIGN: Retrospective cohort study. SETTING: Not applicable. PATIENT(S): A total of 40,485 in vitro fertilization cycles using donor oocytes reported to the Society for Assisted Reproductive Technology registry in 2016-2018. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Live birth rate and cumulative LBR for SET using donor oocytes. RESULT(S): Multivariate results from the first SET from 19,128 cycles, including 15,429 from program generated egg donors and 3,699 from commercial egg banks, showed, when controlling for all other variables, the following: the LBR in the first SET cycle using commercial egg banks was 53.3% compared with 55.4% using program recruited egg donors (odds ratio [OR], 0.92); a reduction in the LBR with increasing recipient age, ages 40-44 years (OR, 0.80), 45-49 years (OR, 0.77), and >49 years (OR, 0.65); a steady decline in the LBR with increases in recipient body mass index above normal; and a steady increase in the LBR in association with >16 oocytes retrieved. Double embryo transfer increased the LBR (SET, 52%, vs. double embryo transfer, 58%) but also significantly increased the multiple pregnancy LBR, with 43% twins and 0.9% triplets. Blastocyst transfer had a higher LBR than cleavage stage embryos (52.5% vs. 39.5%). Intracytoplasmic sperm injection vs. conventional insemination when using fresh oocytes from program donors had similar LBRs. CONCLUSION(S): When performing in vitro fertilization using donor oocytes with SET, the LBR is affected by oocyte source, recipient age, recipient body mass index, stage of embryo at transfer, and number of oocytes retrieved.

Unraveling City-Specific Microbial Signatures and Identifying Sample Origins for the Data From CAMDA 2020 Metagenomic Geolocation Challenge.

The composition of microbial communities has been known to be location-specific. Investigating the microbial composition across different cities enables us to unravel city-specific microbial signatures and further predict the origin of unknown samples. As part of the CAMDA 2020 Metagenomic Geolocation Challenge, MetaSUB provided the whole genome shotgun (WGS) metagenomics data from samples across 28 cities along with non-microbial city data for 23 of these cities. In our solution to this challenge, we implemented feature selection, normalization, clustering and three methods of machine learning to classify the cities based on their microbial compositions. Of the three methods, multilayer perceptron obtained the best performance with an error rate of 19.60% based on whether the correct city received the highest or second highest number of votes for the test data contained in the main dataset. We then trained the model to predict the origins of samples from the mystery dataset by including these samples with the additional group label of "mystery." The mystery dataset compromised of samples collected from a subset of the cities in the main dataset as well as samples collected from new cities. For samples from cities that belonged to the main dataset, error rates ranged from 18.18 to 72.7%. For samples from new cities that did not belong to the main dataset, 57.7% of the test samples could be correctly labeled as "mystery" samples. Furthermore, we also predicted some of the non-microbial features for the mystery samples from the cities that did not belong to main dataset to draw inferences and narrow the range of the possible sample origins using a multi-output multilayer perceptron algorithm.

COVID-19: Reduced Lung Function and Increased Psycho-emotional Stress.

The COVID-19 outbreak causing reduced lung function and increased psycho-emotional stress in the community worldwide. Therefore, it is of interest to document such viral outbreak related emotional stress data in the community with known molecular and patho-physiological parameters of the affected individuals. We provide a concise, coherent, critical, precise, specific and direct narration of such events from a community research viewpoint using known molecular data in this editorial.