PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients

Fujisawa, Kota; Shimo, Mamoru; Taguchi, Y-h.; Ikematsu, Shinya; Miyata, Ryota

doi:10.1038/s41598-021-95698-w

Cited by 18 publications

(15 citation statements)

References 86 publications

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“…Overall, the contributions of this study were as follows: first, we discovered novel pathways currently absent from the COVID-19 Disease Map; second, we demonstrated the use of a new methodology. While previous analysis or curation work found the canonical NF-κB pathway [42], the noncanonical pathways were not known to be involved in the COVID-19 Disease Map. The discovered pathways suggested the existence of unknown pathways in the map, upstream noncanonical NF-κB pathway, and a downstream pathway that may lead to MTOC formation subject to further experiments.…”

Section: Discussionmentioning

confidence: 86%

Gene Network Inference from Single-Cell Omics Data and Domain Knowledge for Constructing COVID-19-Specific ICAM1-Associated Pathways

Odaka

Magnin

Inoue

2022

Preprint

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ICAM-1 is critical for interactions between cells. Previous studies have suggested that ICAM-1 triggers cell-to-cell transmission of HIV-1 or HTLV-1. SARS-CoV-2 shares several features with these viruses in interactions between cells, and SARS-CoV-2 cell-to-cell transmission is associated with COVID-19 severity. However, ICAM-1 and its associated pathways are not identified as essential factors in interactions between cells in COVID-19. For example, the current COVID-19 Disease Map has no entry for those pathways. Therefore, discovering unknown ICAM-1 pathways will be indispensable for clarifying the mechanism of COVID-19. This study builds ICAM1-associated pathways by gene network inference from single-cell omics data and multiple knowledge bases. First, data analyses extracted coexpressed genes with significant differences in expression levels with spurious correlations removed. Second, knowledge bases validate models. Finally, mapping the models onto existing pathways identifies new ICAM1-associated pathways. These pathways indicate that (1) upstream pathways include proteins in noncanonical NF-kappaB pathway and that (2) downstream pathways contain integrins and cytoskeleton or motor proteins for cell transformation. In this way, data-driven and knowledge-based approaches are integrated into gene network inference for ICAM1-associated pathway construction. The results can contribute to repairing and completing the COVID-19 Disease Map, thereby improving our understanding of the mechanisms of COVID-19.

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Section: Discussionmentioning

confidence: 86%

Gene Network Inference from Single-Cell Omics Data and Domain Knowledge for Constructing COVID-19-Specific ICAM1-Associated Pathways

Odaka

Magnin

Inoue

2022

Preprint

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“…The greatest variations in the data are represented by the first PCs. The temporal changes in the first PCs are also used in finance to identify the states of different markets [ 17 , 18 ]. We identified the COVID-19 states of countries based on the changes in the correlations of each country contributing to PC1 between two monthly time windows such as Δ C 1 = C 1 ( T +1)− C 1 ( T ) where T is the time period.…”

Section: Methodsmentioning

confidence: 99%

“…The governments of various countries have locked down their cities and to ensure proper social distancing among people. Different models have been applied to the time series of COVID-19 confirmed, death and recovered cases to observe and predict the COVID dynamics in different countries [ 9 – 18 ]. To understand COVID-19 dynamics, the susceptible exposed infectious removed model and bi-furcation analysis have been performed [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…PCA is applied on data of 213 COVID-19 patients and identified three distinct groups of the COVID-19 patients [ 17 ]. Principal component analysis based unsupervised feature extraction is applied to RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects to identify genes associated with COVID-19 [ 18 ]. Autoregressive time series models based on two-piece scale mixture is applied on world time series of the confirmed and recovered cases of COVID-19 and forecast the confirmed and recovered cases of COVID-19 [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Application of principal component analysis on temporal evolution of COVID-19

2021

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The COVID-19 is one of the worst pandemics in modern history. We applied principal component analysis (PCA) to the daily time series of the COVID-19 death cases and confirmed cases for the top 25 countries from April of 2020 to February of 2021. We calculated the eigenvalues and eigenvectors of the cross-correlation matrix of the changes in daily accumulated data over monthly time windows. The largest eigenvalue describes the overall evolution dynamics of the COVID-19 and indicates that evolution was faster in April of 2020 than in any other period. By using the first two PC coefficients, we can identify the group dynamics of the COVID-19 evolution. We observed groups under critical states in the loading plot and found that American and European countries are represented by strong clusters in the loading plot. The first PC plays an important role and the correlations (C1) between the normalized logarithmic changes in deaths or confirmed cases and the first PCs may be used as indicators of different phases of the COVID-19. By varying C1 over time, we identified different phases of the COVID-19 in the analyzed countries over the target time period.

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“…We further show that the PCA-based methodology can be employed not only for sub-surface analysis but also surface defect analysis. Indeed, PCA serves as a simple yet powerful tool to simplify and analyze the trend behind voluminous data, and has already been utilized in various biological domains: healthcare 24 , medicine 25 , and cell 26 and virus 27 analysis.…”

Section: Introductionmentioning

confidence: 99%

PCA-Based Analysis Applied to Germanium-On-Nothing: Revealing Buried Sub-Surface Structures and Analyzing Defects From Nanoscale Surface Topography

Jeong

Kim

Lee

et al. 2021

Preprint

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Empty Space in Germanium (ESG) or Germanium-on-Nothing (GON) are unique self-assembled germanium structures with multiscale cavities of various morphologies. Due to their simple fabrication process and high-quality crystallinity after self-assembly, they can be applied in various fields including micro-/nanoelectronics, optoelectronics, and precision sensors, to name a few. In contrast to their simple fabrication, inspection is intrinsically difficult due to buried structures. Today, ultrasonic atomic force microscopy and interferometry are some prevalent non-destructive 3-D imaging methods that are used to inspect the underlying ESG structures. However, these non-destructive characterization methods suffer from low throughput due to slow measurement speed and limited measurable thickness. To overcome these limitations, this work proposes a new methodology to construct a principal-component-analysis based database that correlates surface images with empirically determined sub-surface structures by interpolating the surface topography from the database and determining the buried sub-surface structure. Since the acquisition rate of a single nanoscale surface micrograph is up to a few orders faster than a thorough 3D sub-surface analysis, the proposed methodology would provide an exploitable and decisive advantage over the currently prevalent methods. Also, an empirical destructive test essentially resolves the measurable thickness limitation. We also demonstrate the practicality of the proposed methodology by applying it to GON devices to selectively detect and quantitatively analyze surface defects. Compared to state-of-the-art deep learning-based defect detection schemes, our method is much effortlessly finetunable for specific applications. In terms of sub-surface analysis, this work proposes a fast, robust, and high-resolution methodology which could potentially replace the conventional exhaustive sub-surface inspection schemes.

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PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients

Cited by 18 publications

References 86 publications

Gene Network Inference from Single-Cell Omics Data and Domain Knowledge for Constructing COVID-19-Specific ICAM1-Associated Pathways

Gene Network Inference from Single-Cell Omics Data and Domain Knowledge for Constructing COVID-19-Specific ICAM1-Associated Pathways

Application of principal component analysis on temporal evolution of COVID-19

PCA-Based Analysis Applied to Germanium-On-Nothing: Revealing Buried Sub-Surface Structures and Analyzing Defects From Nanoscale Surface Topography

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