Normalization Methods on Single-Cell RNA-seq Data: An Empirical Survey

Lytal, Nicholas; Di, Ran; An, Lingling

doi:10.3389/fgene.2020.00041

Cited by 75 publications

(53 citation statements)

References 42 publications

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“…In COVID-19, neutrophil activation/degranulation is the largest gene signature in blood cells [ 89 ] predicting disease severity [ 90 ] and mortality [ 91 ]. An increasing number of reports have identified a high neutrophil to lymphocyte ratio to be associated with severe COVID-19 [ 92 , 93 ].…”

Section: Introductionmentioning

confidence: 99%

Innate immunology in COVID-19—a living review. Part II: dysregulated inflammation drives immunopathology

Prs.

Alrubayyi

Pring

et al. 2020

Oxford Open Immunology

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The current pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a global health crisis and will likely continue to impact public health for years. As the effectiveness of the innate immune response is crucial to patient outcome, huge efforts have been made to understand how dysregulated immune responses may contribute to disease progression. Here we have reviewed current knowledge of cellular innate immune responses to SARS-CoV-2 infection, highlighting areas for further investigation and suggesting potential strategies for intervention. We conclude that in severe COVID-19 initial innate responses, primarily type I interferon, are suppressed or sabotaged which results in an early interleukin (IL)-6, IL-10 and IL-1β-enhanced hyperinflammation. This inflammatory environment is driven by aberrant function of innate immune cells: monocytes, macrophages and natural killer cells dispersing viral pathogen-associated molecular patterns and damage-associated molecular patterns into tissues. This results in primarily neutrophil-driven pathology including fibrosis that causes acute respiratory distress syndrome. Activated leukocytes and neutrophil extracellular traps also promote immunothrombotic clots that embed into the lungs and kidneys of severe COVID-19 patients, are worsened by immobility in the intensive care unit and are perhaps responsible for the high mortality. Therefore, treatments that target inflammation and coagulation are promising strategies for reducing mortality in COVID-19.

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Section: Introductionmentioning

confidence: 99%

Innate immunology in COVID-19—a living review. Part II: dysregulated inflammation drives immunopathology

Prs.

Alrubayyi

Pring

et al. 2020

Oxford Open Immunology

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“…This has been extensively studied and addressed in a number of ways: normalization, regression of unwanted sources of variation, etc. [24, 25, 26, 27].…”

Section: Discussionmentioning

confidence: 99%

Triku: a feature selection method based on nearest neighbors for single-cell data

Ibáñez-Solé

Inza

et al. 2021

Preprint

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Feature selection is a relevant step in the analysis of single-cell RNA sequencing datasets. Triku is a feature selection method that favours genes defining the main cell populations. It does so by selecting genes expressed by groups of cells that are close in the nearest neighbor graph. Triku efficiently recovers cell populations present in artificial and biological benchmarking datasets, based on mutual information and silhouette coefficient measurements. Additionally, gene sets selected by triku are more likely to be related to relevant Gene Ontology terms, and contain fewer ribosomal and mitochondrial genes. Triku is available at https://gitlab.com/alexmascension/triku.

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“…Normalization using regularized negative binomial regression effectively eliminates technical differences due to different sequencing depth without inhibiting biological heterogeneity. A previous study [36] compared seven scRNA-seq data normalization methods with regard to reduction of noise or bias, and found that each of these methods was suitable to normalize specific types of data for further downstream analysis.…”

Section: Normalizationmentioning

confidence: 99%

What are the applications of single-cell RNA sequencing in cancer research: a systematic review

Xiong

Wang

et al. 2021

J Exp Clin Cancer Res

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Single-cell RNA sequencing (scRNA-seq) is a tool for studying gene expression at the single-cell level that has been widely used due to its unprecedented high resolution. In the present review, we outline the preparation process and sequencing platforms for the scRNA-seq analysis of solid tumor specimens and discuss the main steps and methods used during data analysis, including quality control, batch-effect correction, normalization, cell cycle phase assignment, clustering, cell trajectory and pseudo-time reconstruction, differential expression analysis and gene set enrichment analysis, as well as gene regulatory network inference. Traditional bulk RNA sequencing does not address the heterogeneity within and between tumors, and since the development of the first scRNA-seq technique, this approach has been widely used in cancer research to better understand cancer cell biology and pathogenetic mechanisms. ScRNA-seq has been of great significance for the development of targeted therapy and immunotherapy. In the second part of this review, we focus on the application of scRNA-seq in solid tumors, and summarize the findings and achievements in tumor research afforded by its use. ScRNA-seq holds promise for improving our understanding of the molecular characteristics of cancer, and potentially contributing to improved diagnosis, prognosis, and therapeutics.

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Normalization Methods on Single-Cell RNA-seq Data: An Empirical Survey

Cited by 75 publications

References 42 publications

Innate immunology in COVID-19—a living review. Part II: dysregulated inflammation drives immunopathology

Innate immunology in COVID-19—a living review. Part II: dysregulated inflammation drives immunopathology

Triku: a feature selection method based on nearest neighbors for single-cell data

What are the applications of single-cell RNA sequencing in cancer research: a systematic review

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