Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.
IMPORTANCE COVID-19 is a life-threatening illness for many patients. Prior studies have established hematologic cancers as a risk factor associated with particularly poor outcomes from COVID-19. To our knowledge, no studies have established a beneficial role for anti-COVID-19 interventions in this at-risk population. Convalescent plasma therapy may benefit immunocompromised individuals with COVID-19, including those with hematologic cancers.OBJECTIVE To evaluate the association of convalescent plasma treatment with 30-day mortality in hospitalized adults with hematologic cancers and COVID-19 from a multi-institutional cohort.
DESIGN, SETTING, AND PARTICIPANTSThis retrospective cohort study using data from the COVID-19 and Cancer Consortium registry with propensity score matching evaluated patients with hematologic cancers who were hospitalized for COVID-19. Data were collected between
Polydatin(PD) shows anti-allergic inflammatory effect, and this study investigated its underlying mechanisms in in vitro and in vivo models. IgE-mediated passive cutaneous anaphylaxis (PCA) and passive systemic anaphylaxis (PSA) models were used to confirm PD effect in vivo. Various signaling pathway proteins in mast cell were examined. RT-PCR, ELISA and western blotting were applied when appropriate. Activity of Lyn and Fyn kinases in vitro was measured using the Kinase Enzyme System. PD dose-dependently reduced the pigmentation of Evans blue in the PCA model and decreased the concentration of serum histamine in PSA model, and attenuated the degranulation of mast cells without generating cytotoxicity. PD decreased pro-inflammatory cytokine expression (TNF-α, IL-4, IL-1β, and IL-8). PD directly inhibited activity of Lyn and Syk kinases and down-regulated downstream signaling pathway including MAPK, PI3K/AKT and NF-kB. In addition, PD also targets Nrf2/HO-1 pathway to inhibit mast cell-derived allergic inflammatory reactions. In conclusion, the study demonstrates that PD is a possible therapeutic candidate for allergic inflammatory diseases. It directly inhibited activity of Lyn and Syk kinases and down-regulates the signaling pathway of MAPK, PI3K/AKT and NF-κB, and up-regulates the signaling pathway of Nrf2/HO-1 to inhibit the degranulation of mast cells.
A pressing challenge in single-cell transcriptomics is to benchmark experimental protocols and computational methods. A solution is to use computational simulators, but existing simulators cannot simultaneously achieve three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill this gap, we propose scDesign2, a transparent simulator that achieves all three goals and generates high-fidelity synthetic data for multiple single-cell gene expression count-based technologies. In particular, scDesign2 is advantageous in its transparent use of probabilistic models and its ability to capture gene correlations via copulas.
In the burgeoning field of single-cell transcriptomics, a pressing challenge is to benchmark various experimental protocols and numerous computational methods in an unbiased manner. Although dozens of simulators have been developed for single-cell RNA-seq (scRNA-seq) data, they lack the capacity to simultaneously achieve all the three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill in this gap, here we propose scDesign2, an interpretable simulator that achieves all the three goals and generates high-fidelity synthetic data for multiple scRNA-seq protocols and other single-cell gene expression count-based technologies. Compared with existing simulators, scDesign2 is advantageous in its transparent use of probabilistic models and is unique in its ability to capture gene correlations via copula. We verify that scDesign2 generates more realistic synthetic data for four scRNA-seq protocols (10x Genomics, CEL-Seq2, Fluidigm C1, and Smart-Seq2) and two single-cell spatial transcriptomics protocols (MERFISH and pciSeq) than existing simulators do. Under two typical computational tasks, cell clustering and rare cell type detection, we demonstrate that scDesign2 provides informative guidance on deciding the optimal sequencing depth and cell number in single-cell RNA-seq experimental design, and that scDesign2 can effectively benchmark computational methods under varying sequencing depths and cell numbers. With these advantages, scDesign2 is a powerful tool for single-cell researchers to design experiments, develop computational methods, and choose appropriate methods for specific data analysis needs.
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