Semi-supervised K-Way Spectral Clustering with Determination of Number of Clusters

Wacquet, Guillaume; Caillault, Émilie Poisson; Hébert, Pierre-Alexandre

doi:10.1007/978-3-642-35638-4_21

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…To implement spectral clustering, we used the following functions in R: computeGaussianSimilarity() (Σ = 1) to compute similarity matrix, and spectralClustering() ( K = 3, 4, and 5; other parameters: default; package RclusTool ( 115 ), version 0.91.3) to cluster.…”

Section: Methodsmentioning

confidence: 99%

Identifying novel regulators of placental development using time-series transcriptome data

Kaur

Kies

et al. 2022

Life Sci. Alliance

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The placenta serves as a connection between the mother and the fetus during pregnancy, providing the fetus with oxygen, nutrients, and growth hormones. However, the regulatory mechanisms and dynamic gene interaction networks underlying early placental development are understudied. Here, we generated RNA-sequencing data from mouse fetal placenta at embryonic days 7.5, 8.5, and 9.5 to identify genes with timepoint-specific expression, then inferred gene interaction networks to analyze highly connected network modules. We determined that timepoint-specific gene network modules were associated with distinct developmental processes, and with similar expression profiles to specific human placental cell populations. From each module, we identified hub genes and their direct neighboring genes, which were predicted to govern placental functions. We confirmed that four novel candidate regulators identified through our analyses regulate cell migration in the HTR-8/SVneo cell line. Overall, we predicted several novel regulators of placental development expressed in specific placental cell types using network analysis of bulk RNA-sequencing data. Our findings and analysis approaches will be valuable for future studies investigating the transcriptional landscape of early development.

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Section: Methodsmentioning

confidence: 99%

Identifying novel regulators of placental development using time-series transcriptome data

Kaur

Kies

et al. 2022

Life Sci. Alliance

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“…To implement spectral clustering, we utilized the following functions in R: computeGaussianSimilarity() (sigma = 1) to compute similarity matrix, and spectralClustering() (K = 3, other parameters: default; package RclusTool [112], version 0.91.3) to cluster.…”

Section: Cluster Analysismentioning

confidence: 99%

Identifying novel regulators of placental development using time series transcriptomic data and network analyses

Kaur

Kies

et al. 2022

Preprint

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The placenta serves as a connection between the mother and the fetus during pregnancy, and provides the fetus with oxygen, nutrients, and growth hormones. However, the regulatory mechanisms and dynamic gene interaction networks underlying early placental development are understudied. Here, we generated RNA sequencing (RNA-seq) data from mouse fetal placenta tissues at embryonic day (e) 7.5, e8.5 and e9.5 to identify genes with timepoint-specific expression, then inferred gene interaction networks to analyze highly connected network modules. We determined that timepoint-specific gene network modules associated with distinct developmental processes, and with similar expression profiles to specific human placental cell populations. From each module, we obtained hub genes and their direct neighboring genes, which were predicted to govern placental functions. We confirmed that four novel candidate regulators identified through our analyses regulate cell migration in the HTR-8/SVneo cell line. Upon conclusion of this study, we were able to predict several novel regulators of placental development using network analysis of bulk RNA-seq data. Our findings and analysis approaches will be valuable for future studies investigating the transcriptional landscape of early placental development.

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“…Figure 6 show 2006 year explains by 8 events, clearly separated in opposite of the mean pattern. Next, it will be important to consider this variability and to try to understand the 5 dominant yearly models computed from an unsupervised spectral clustering [13] that have no evident succession.…”

Section: ) Geometric Mean Biasmentioning

confidence: 99%

Towards Chl-a bloom understanding by EM-based unsupervised event detection

Caillault

Lefebvre

2017

OCEANS 2017 - Aberdeen

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Marine water quality monitoring and subsequent management require to know when a specific event like harmful algae bloom may occur and which environmental conditions and pressures lead to this event. So, event detection and its dynamic understanding are crucial to adapt strategy. An algorithm is proposed to identify curves mixture and their dynamics features-initiation, duration, peaks and ends of the event. The approach is fully unsupervised, it requires no tuning parameters and is based on Expectation Maximization process to estimate the most robust mixture according to fixed criteria. A complete framework is proposed to deal with a univariate time series with missing data. The approach is applied on Chlorophyll-a series collected weekly since 1989. Chlorophyll-a is a proxy of the phytoplankton biomass. The results are promising according to the phytoplankton composition knowledge, collected at lower frequency, and allowing to discuss about the annual variability of phytoplankton dynamics.

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Semi-supervised K-Way Spectral Clustering with Determination of Number of Clusters

Cited by 7 publications

References 6 publications

Identifying novel regulators of placental development using time-series transcriptome data

Identifying novel regulators of placental development using time-series transcriptome data

Identifying novel regulators of placental development using time series transcriptomic data and network analyses

Towards Chl-a bloom understanding by EM-based unsupervised event detection

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