Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data.

Variations of cell-type proportions within tissues could be informative of biological aging and disease risk. Single-cell RNA-sequencing offers the opportunity to detect such differential abundance patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because such patterns are often of small effect size. Here we present a differential abundance testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold. Using simulated and real single-cell and single-nucleus RNA-Seq datasets, we benchmark ELVAR against an analogous algorithm that uses Louvain for clustering, as well as local neighborhood-based methods, demonstrating that ELVAR improves the sensitivity to detect cell-type composition shifts in relation to aging, precancerous states and Covid-19 phenotypes. In effect, leveraging cell attribute information when inferring cell communities can denoise single-cell data, avoid the need for batch correction and help retrieve more robust cell states for subsequent differential abundance testing. ELVAR is available as an open-source R-package.

Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin.

To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individuals by molecular signatures in the form of machine-learning models with genes as predictor variables. Early diagnosis of patients by molecular signatures could also contribute to better treatments. An approach that has rarely been considered for machine-learning models in the context of transcriptomics is data augmentation. For other data types it has been shown that augmentation can improve classification accuracy and prevent overfitting. Here, we compare three strategies for data augmentation of DNA microarray and RNA-seq data from two selected studies on respiratory diseases of viral origin. The first study involves samples of patients with either viral or bacterial origin of the respiratory disease, the second study involves patients with either SARS-CoV-2 or another respiratory virus as disease origin. Specifically, we reanalyze these public datasets to study whether patient classification by transcriptomic signatures can be improved when adding artificial data for training of the machine-learning models. Our comparison reveals that augmentation of transcriptomic data can improve the classification accuracy and that fewer genes are necessary as explanatory variables in the final models. We also report genes from our signatures that overlap with signatures presented in the original publications of our example data. Due to strict selection criteria, the molecular role of these genes in the context of respiratory infectious diseases is underlined.

SLAMF7 engagement superactivates macrophages in acute and chronic inflammation.

Macrophages regulate protective immune responses to infectious microbes, but aberrant macrophage activation frequently drives pathological inflammation. To identify regulators of vigorous macrophage activation, we analyzed RNA-seq data from synovial macrophages and identified SLAMF7 as a receptor associated with a superactivated macrophage state in rheumatoid arthritis. We implicated IFN-γ as a key regulator of SLAMF7 expression and engaging SLAMF7 drove a strong wave of inflammatory cytokine expression. Induction of TNF-α after SLAMF7 engagement amplified inflammation through an autocrine signaling loop. We observed SLAMF7-induced gene programs not only in macrophages from rheumatoid arthritis patients but also in gut macrophages from patients with active Crohn's disease and in lung macrophages from patients with severe COVID-19. This suggests a central role for SLAMF7 in macrophage superactivation with broad implications in human disease pathology.

Competitive Endogenous RNA Network Activates Host Immune Response in SARS-CoV-2-, panH1N1 (A/California/07/2009)-, and H7N9 (A/Shanghai/1/2013)-Infected Cells.

The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still ongoing, as is research on the molecular mechanisms underlying cellular infection by coronaviruses, with the hope of developing therapeutic agents against this pandemic. Other important respiratory viruses such as 2009 pandemic H1N1 and H7N9 avian influenza virus (AIV), influenza A viruses, are also responsible for a possible outbreak due to their respiratory susceptibility. However, the interaction of these viruses with host cells and the regulation of post-transcriptional genes remains unclear. In this study, we detected and analyzed the comparative transcriptome profiling of SARS-CoV-2, panH1N1 (A/California/07/2009), and H7N9 (A/Shanghai/1/2013) infected cells. The results showed that the commonly upregulated genes among the three groups were mainly involved in autophagy, pertussis, and tuberculosis, which indicated that autophagy plays an important role in viral pathogenicity. There are three groups of commonly downregulated genes involved in metabolic pathways. Notably, unlike panH1N1 and H7N9, SARS-CoV-2 infection can inhibit the m-TOR pathway and activate the p53 signaling pathway, which may be responsible for unique autophagy induction and cell apoptosis. Particularly, upregulated expression of IRF1 was found in SARS-CoV-2, panH1N1, and H7N9 infection. Further analysis showed SARS-CoV-2, panH1N1, and H7N9 infection-induced upregulation of lncRNA-34087.27 could serve as a competitive endogenous RNA to stabilize IRF1 mRNA by competitively binding with miR-302b-3p. This study provides new insights into the molecular mechanisms of influenza A virus and SARS-CoV-2 infection.

System-wide transcriptome damage and tissue identity loss in COVID-19 patients.

The molecular mechanisms underlying the clinical manifestations of coronavirus disease 2019 (COVID-19), and what distinguishes them from common seasonal influenza virus and other lung injury states such as acute respiratory distress syndrome, remain poorly understood. To address these challenges, we combine transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues to define body-wide transcriptome changes in response to COVID-19. We then match these data with spatial protein and expression profiling across 357 tissue sections from 16 representative patient lung samples and identify tissue-compartment-specific damage wrought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, evident as a function of varying viral loads during the clinical course of infection and tissue-type-specific expression states. Overall, our findings reveal a systemic disruption of canonical cellular and transcriptional pathways across all tissues, which can inform subsequent studies to combat the mortality of COVID-19 and to better understand the molecular dynamics of lethal SARS-CoV-2 and other respiratory infections.

Viral infection and transmission in a large, well-traced outbreak caused by the SARS-CoV-2 Delta variant.

The SARS-CoV-2 Delta variant has spread rapidly worldwide. To provide data on its virological profile, we here report the first local transmission of Delta in mainland China. All 167 infections could be traced back to the first index case. Daily sequential PCR testing of quarantined individuals indicated that the viral loads of Delta infections, when they first become PCR-positive, were on average ~1000 times greater compared to lineage A/B infections during the first epidemic wave in China in early 2020, suggesting potentially faster viral replication and greater infectiousness of Delta during early infection. The estimated transmission bottleneck size of the Delta variant was generally narrow, with 1-3 virions in 29 donor-recipient transmission pairs. However, the transmission of minor iSNVs resulted in at least 3 of the 34 substitutions that were identified in the outbreak, highlighting the contribution of intra-host variants to population-level viral diversity during rapid spread.

Cardiovascular Risk After SARS-CoV-2 Infection Is Mediated by IL18/IL18R1/HIF-1 Signaling Pathway Axis.

Objectives: Currently, cardiovascular risk associated with COVID-19 has been brought to people's attention, but the mechanism is not clear. The aim of this study is to elucidate the mechanisms based on multiple omics data. Methodology: Weighted gene co-expression network analysis (WGCNA) was used to identify key pathways. Combination analysis with aneurysm and atherosclerosis related pathways, hypoxia induced factor-1 (HIF-1) signaling were identified as key pathways of the increased cardiovascular risk associated with COVID-19. ScMLnet algorithm based on scRNA-seq was used to explore the regulation of HIF-1 pathway by intercellular communication. Proteomic analysis was used to detect the regulatory mechanisms between IL18 and HIF-1 signaling pathway. Pseudo time locus analysis was used to study the regulation of HIF1 signaling pathway in macrophages and vascular smooth muscle cells (VSMC) phenotypic transformation. The Virtual Inference of protein-activity by Enriched Regulon (VIPER) analysis was used to study the activity of regulatory proteins. Epigenetic analysis based on methylation revealed epigenetic changes in PBMC after SARS-CoV-2 infection. Potential therapeutic compounds were explored by using Cmap algorithm. Results: HIF-1 signaling pathway is a common key pathway for aneurysms, atherosclerosis and SARS-CoV-2 infection. Intercellular communication analysis showed that macrophage-derived interleukin-18 (IL-18) activates the HIF-1 signaling pathway through IL18R1. Proteomic analysis showed that IL18/IL18R1 promote NF-κB entry into the nucleus, and activated the HIF-1 signaling pathway. Macrophage-derived IL18 promoted the M1 polarization of macrophages and the syntactic phenotype transformation of VSMCs. MAP2K1 mediates the functional regulation of HIF-1 signaling pathway in various cell types. Epigenetic changes in PBMC after COVID-19 infection are characterized by activation of the type I interferon pathway. MEK inhibitors are the promising compounds for the treatment of HIF-1 overactivation. Conclusions: The IL18/IL18R1/HIF1A axis is expected to be an therapeutic target for cardiovascular protection after SARS-CoV-2 infection. MEK inhibitors may be an choice for cardiovascular protection after SARS-COV-2 infection.

Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19.

Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100Ahi/HLA-DRlo classical monocytes and activated LAG-3hi T cells are hallmarks of progressive disease and highlights the abnormal MHC-II/LAG-3 interaction on myeloid and T cells, respectively. We also find skewed T cell receptor repertories in expanded effector CD8+ clones, unmutated IGHG+ B cell clones, and mutated B cell clones with stable somatic hypermutation frequency over time. In conclusion, our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19.

Systematic identification of NF90 target RNAs by iCLIP analysis.

RNA-binding proteins (RBPs) interact with and determine the fate of many cellular RNAs directing numerous essential roles in cellular physiology. Nuclear Factor 90 (NF90) is an RBP encoded by the interleukin enhancer-binding factor 3 (ILF3) gene that has been found to influence RNA metabolism at several levels, including pre-RNA splicing, mRNA turnover, and translation. To systematically identify the RNAs that interact with NF90, we carried out iCLIP (individual-nucleotide resolution UV crosslinking and immunoprecipitation) analysis in the human embryonic fibroblast cell line HEK-293. Interestingly, many of the identified RNAs encoded proteins involved in the response to viral infection and RNA metabolism. We validated a subset of targets and investigated the impact of NF90 on their expression levels. Two of the top targets, IRF3 and IRF9 mRNAs, encode the proteins IRF3 and IRF9, crucial regulators of the interferon pathway involved in the SARS-CoV-2 immune response. Our results support a role for NF90 in modulating key genes implicated in the immune response and offer insight into the immunological response to the SARS-CoV-2 infection.

Elevated temperature inhibits SARS-CoV-2 replication in respiratory epithelium independently of IFN-mediated innate immune defenses.

The pandemic spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiological agent of Coronavirus Disease 2019 (COVID-19), represents an ongoing international health crisis. A key symptom of SARS-CoV-2 infection is the onset of fever, with a hyperthermic temperature range of 38 to 41°C. Fever is an evolutionarily conserved host response to microbial infection that can influence the outcome of viral pathogenicity and regulation of host innate and adaptive immune responses. However, it remains to be determined what effect elevated temperature has on SARS-CoV-2 replication. Utilizing a three-dimensional (3D) air-liquid interface (ALI) model that closely mimics the natural tissue physiology of SARS-CoV-2 infection in the respiratory airway, we identify tissue temperature to play an important role in the regulation of SARS-CoV-2 infection. Respiratory tissue incubated at 40°C remained permissive to SARS-CoV-2 entry but refractory to viral transcription, leading to significantly reduced levels of viral RNA replication and apical shedding of infectious virus. We identify tissue temperature to play an important role in the differential regulation of epithelial host responses to SARS-CoV-2 infection that impact upon multiple pathways, including intracellular immune regulation, without disruption to general transcription or epithelium integrity. We present the first evidence that febrile temperatures associated with COVID-19 inhibit SARS-CoV-2 replication in respiratory epithelia. Our data identify an important role for tissue temperature in the epithelial restriction of SARS-CoV-2 independently of canonical interferon (IFN)-mediated antiviral immune defenses.

Role of Human Antigen R (HuR) in the Regulation of Pulmonary ACE2 Expression.

Patients with COPD may be at an increased risk for severe illness from COVID-19 because of ACE2 upregulation, the entry receptor for SARS-CoV-2. Chronic exposure to cigarette smoke, the main risk factor for COPD, increases pulmonary ACE2. How ACE2 expression is controlled is not known but may involve HuR, an RNA binding protein that increases protein expression by stabilizing mRNA. We hypothesized that HuR would increase ACE2 protein expression. We analyzed scRNA-seq data to profile ELAVL1 expression in distinct respiratory cell populations in COVID-19 and COPD patients. HuR expression and cellular localization was evaluated in COPD lung tissue by multiplex immunohistochemistry and in human lung cells by imaging flow cytometry. The regulation of ACE2 expression was evaluated using siRNA-mediated knockdown of HuR. There is a significant positive correlation between ELAVL1 and ACE2 in COPD cells. HuR cytoplasmic localization is higher in smoker and COPD lung tissue; there were also higher levels of cleaved HuR (CP-1). HuR binds to ACE2 mRNA but knockdown of HuR does not change ACE2 protein levels in primary human lung fibroblasts (HLFs). Our work is the first to investigate the association between ACE2 and HuR. Further investigation is needed to understand the mechanistic underpinning behind the regulation of ACE2 expression.

Recent advances in methods for the diagnosis of Corona Virus Disease 2019.

Since the beginning of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic, it has been clear that effective methods for the diagnosis of Corona Virus Disease 2019 (COVID-19) are the key tools to control its epidemic. The current gold standard for diagnosing COVID-19 is the real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR), which is a sensitive and specific method to detect SARS-CoV-2. Other RNA-based methods include RNA sequencing (RNA-seq), droplet digital reverse transcription-polymerase chain reaction (ddRT-PCR), reverse transcription loop-mediated isothermal amplification (RT-LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR). The serological testing of antibodies (IgM and IgG), nanoparticle-based lateral-flow assay, and enzyme-linked immunosorbent assay (ELISA) can be used to enhance the detection sensitivity and accuracy. Because antibodies are usually detected a week after the onset of symptoms, these tests are used to assess the overall infection rate in the community. Sine the fact that healthcare varies from country to country across the world, different types of diagnosing COVID-19 imaging technologies including chest computed tomography (CT), chest radiography, and lung ultrasound are used in different degrees. Besides, the pooling test is an important public health tool to reduce cost and increase testing capacity in low-risk area, while artificial intelligence (AI) may aid to increase the diagnostic efficiency of imaging-based methods. Finally, depending on the type of samples and stages of the disease, a combination of information on patient demographics and histories, clinical symptoms, results of molecular and serological diagnostic tests, and imaging information is highly recommended to achieve adequate diagnosis of patients with COVID-19.

YAP1 nuclear efflux and transcriptional reprograming follow membrane diminution upon VSV-G-induced cell fusion.

Cells in many tissues, such as bone, muscle, and placenta, fuse into syncytia to acquire new functions and transcriptional programs. While it is known that fused cells are specialized, it is unclear whether cell-fusion itself contributes to programmatic-changes that generate the new cellular state. Here, we address this by employing a fusogen-mediated, cell-fusion system to create syncytia from undifferentiated cells. RNA-Seq analysis reveals VSV-G-induced cell fusion precedes transcriptional changes. To gain mechanistic insights, we measure the plasma membrane surface area after cell-fusion and observe it diminishes through increases in endocytosis. Consequently, glucose transporters internalize, and cytoplasmic glucose and ATP transiently decrease. This reduced energetic state activates AMPK, which inhibits YAP1, causing transcriptional-reprogramming and cell-cycle arrest. Impairing either endocytosis or AMPK activity prevents YAP1 inhibition and cell-cycle arrest after fusion. Together, these data demonstrate plasma membrane diminishment upon cell-fusion causes transient nutrient stress that may promote transcriptional-reprogramming independent from extrinsic cues.

Inhibition of SARS-CoV-2 coronavirus proliferation by designer antisense-circRNAs.

Circular RNAs (circRNAs) are noncoding RNAs that exist in all eukaryotes investigated and are derived from back-splicing of certain pre-mRNA exons. Here, we report the application of artificial circRNAs designed to act as antisense-RNAs. We systematically tested a series of antisense-circRNAs targeted to the SARS-CoV-2 genome RNA, in particular its structurally conserved 5'-untranslated region. Functional assays with both reporter transfections as well as with SARS-CoV-2 infections revealed that specific segments of the SARS-CoV-2 5'-untranslated region can be efficiently accessed by specific antisense-circRNAs, resulting in up to 90% reduction of virus proliferation in cell culture, and with a durability of at least 48 h. Presenting the antisense sequence within a circRNA clearly proved more efficient than in the corresponding linear configuration and is superior to modified antisense oligonucleotides. The activity of the antisense-circRNA is surprisingly robust towards point mutations in the target sequence. This strategy opens up novel applications for designer circRNAs and promising therapeutic strategies in molecular medicine.

WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition.

The low capture rate of expressed RNAs from single-cell sequencing technology is one of the major obstacles to downstream functional genomics analyses. Recently, a number of imputation methods have emerged for single-cell transcriptome data, however, recovering missing values in very sparse expression matrices remains a substantial challenge. Here, we propose a new algorithm, WEDGE (WEighted Decomposition of Gene Expression), to impute gene expression matrices by using a biased low-rank matrix decomposition method. WEDGE successfully recovered expression matrices, reproduced the cell-wise and gene-wise correlations and improved the clustering of cells, performing impressively for applications with sparse datasets. Overall, this study shows a potent approach for imputing sparse expression matrix data, and our WEDGE algorithm should help many researchers to more profitably explore the biological meanings embedded in their single-cell RNA sequencing datasets. The source code of WEDGE has been released at https://github.com/QuKunLab/WEDGE.

Viral S protein histochemistry reveals few potential SARS-CoV-2 entry sites in human ocular tissues.

Despite the reported low expression of the primary SARS-CoV-2 receptor ACE2 in distinct ocular tissues, some clinical evidence suggests that SARS-CoV-2 can infect the eye. In this study, we explored potential entry sites for SARS-CoV-2 by viral S protein histochemistry on various ocular tissues and compared the staining patterns with RNA and protein expression of TMPRSS2 and ACE2. Potential viral entry sites were investigated by histochemistry using tagged recombinant viral S protein on 52 ocular tissue samples including specimens of the cornea, conjunctiva, lid margin, lacrimal gland tissue, retina, choroid, and RPE. In addition, ACE2 and TMPRSS2 immunohistochemistry were performed on the same ocular tissue, each with distinct antibodies binding to different epitopes. Lung tissue samples were used as positive controls. Finally, bulk RNA sequencing (RNA-Seq) was used to determine the expression of ACE2 and its auxiliary factors in the tissues mentioned above. S protein histochemistry revealed a positive staining in lung tissue but absent staining in the cornea, the conjunctiva, eye lid samples, the lacrimal glands, the retina and the optic nerve which was supported by hardly any immunoreactivity for ACE2 and TMPRSS2 and scarce ACE2 and TMPRSS2 RNA expression. Negligible staining with antibodies targeting ACE2 or TMPRSS2 was seen in the main and accessory lacrimal glands. In contrast, ocular staining (S protein, ACE2, TMPRSS2) was distinctly present in pigmented cells of the RPE and choroid, as well as in the ciliary body and the iris stroma. S protein histochemistry revealed hardly any SARS-CoV-2 entry sites in all ocular tissues examined. Similarly, no significant ACE2 or TMPRSS2 expression was found in extra- and intraocular tissue. While this study suggest a rather low risk of ocular infection with SARS-CoV-2, it should be noted, that potential viral entry sites may increase in response to inflammation or in certain disease states.

AGO CLIP-based imputation of potent siRNA sequences targeting SARS-CoV-2 with antifibrotic miRNA-like activity.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is associated with fatal pulmonary fibrosis. Small interfering RNAs (siRNAs) can be developed to induce RNA interference against SARS-CoV-2, and their susceptible target sites can be inferred by Argonaute crosslinking immunoprecipitation sequencing (AGO CLIP). Here, by reanalysing AGO CLIP data in RNA viruses, we delineated putative AGO binding in the conserved non-structural protein 12 (nsp12) region encoding RNA-dependent RNA polymerase (RdRP) in SARS-CoV-2. We utilised the inferred AGO binding to optimise the local RNA folding parameter to calculate target accessibility and predict all potent siRNA target sites in the SARS-CoV-2 genome, avoiding sequence variants. siRNAs loaded onto AGO also repressed seed (positions 2-8)-matched transcripts by acting as microRNAs (miRNAs). To utilise this, we further screened 13 potential siRNAs whose seed sequences were matched to known antifibrotic miRNAs and confirmed their miRNA-like activity. A miR-27-mimicking siRNA designed to target the nsp12 region (27/RdRP) was validated to silence a synthesised nsp12 RNA mimic in lung cell lines and function as an antifibrotic miR-27 in regulating target transcriptomes related to TGF-ß signalling. siRNA sequences with an antifibrotic miRNA-like activity that could synergistically treat COVID-19 are available online ( http://clip.korea.ac.kr/covid19 ).

RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data.

The transcriptomic diversity of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Unsupervised clustering of SC transcriptomes, which is the default technique for defining cell types, is prone to group cells by technical, rather than biological, variation. Compared to de-novo (unsupervised) clustering, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects and data quality artifacts. Here, we present RCA2, the first algorithm to combine reference projection (batch effect robustness) with graph-based clustering (scalability). In addition, RCA2 provides a user-friendly framework incorporating multiple commonly used downstream analysis modules. RCA2 also provides new reference panels for human and mouse and supports generation of custom panels. Furthermore, RCA2 facilitates cell type-specific QC, which is essential for accurate clustering of data from heterogeneous tissues. We demonstrate the advantages of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Scalable supervised clustering methods such as RCA2 will facilitate unified analysis of cohort-scale SC datasets.

African Americans and European Americans exhibit distinct gene expression patterns across tissues and tumors associated with immunologic functions and environmental exposures.

The COVID-19 pandemic has affected African American populations disproportionately with respect to prevalence, and mortality. Expression profiles represent snapshots of combined genetic, socio-environmental (including socioeconomic and environmental factors), and physiological effects on the molecular phenotype. As such, they have potential to improve biological understanding of differences among populations, and provide therapeutic biomarkers and environmental mitigation strategies. Here, we undertook a large-scale assessment of patterns of gene expression between African Americans and European Americans, mining RNA-Seq data from 25 non-diseased and diseased (tumor) tissue-types. We observed the widespread enrichment of pathways implicated in COVID-19 and integral to inflammation and reactive oxygen stress. Chemokine CCL3L3 expression is up-regulated in African Americans. GSTM1, encoding a glutathione S-transferase that metabolizes reactive oxygen species and xenobiotics, is upregulated. The little-studied F8A2 gene is up to 40-fold more highly expressed in African Americans; F8A2 encodes HAP40 protein, which mediates endosome movement, potentially altering the cellular response to SARS-CoV-2. African American expression signatures, superimposed on single cell-RNA reference data, reveal increased number or activity of esophageal glandular cells and lung ACE2-positive basal keratinocytes. Our findings establish basal prognostic signatures that can be used to refine approaches to minimize risk of severe infection and improve precision treatment of COVID-19 for African Americans. To enable dissection of causes of divergent molecular phenotypes, we advocate routine inclusion of metadata on genomic and socio-environmental factors for human RNA-sequencing studies.

NDRindex: a method for the quality assessment of single-cell RNA-Seq preprocessing data.

BACKGROUND: Single-cell RNA sequencing can be used to fairly determine cell types, which is beneficial to the medical field, especially the many recent studies on COVID-19. Generally, single-cell RNA data analysis pipelines include data normalization, size reduction, and unsupervised clustering. However, different normalization and size reduction methods will significantly affect the results of clustering and cell type enrichment analysis. Choices of preprocessing paths is crucial in scRNA-Seq data mining, because a proper preprocessing path can extract more important information from complex raw data and lead to more accurate clustering results. RESULTS: We proposed a method called NDRindex (Normalization and Dimensionality Reduction index) to evaluate data quality of outcomes of normalization and dimensionality reduction methods. The method includes a function to calculate the degree of data aggregation, which is the key to measuring data quality before clustering. For the five single-cell RNA sequence datasets we tested, the results proved the efficacy and accuracy of our index. CONCLUSIONS: This method we introduce focuses on filling the blanks in the selection of preprocessing paths, and the result proves its effectiveness and accuracy. Our research provides useful indicators for the evaluation of RNA-Seq data.