Robustness of S1 statistic with Hodges-Lehmann for skewed distributions

Ahad, Nor Aishah; Yahaya, Sharipah Soaad Syed; Yin, Lee Ping

doi:10.1063/1.4966092

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…The test results are shown in Table 3, the significance of each group of samples is less than 0.05, so they do not obey the normal distribution. Analysis of variance (ANOVA) is a commonly used parametric method for testing differences in means between more than two groups, but its limitation is the assumption of normality, which makes ANOVA very inefficient under the influence of non-normal data distribution [85]. The Kruskal-Wallis (K-W) test can be used when the sample data does not conform to a normal distribution and the data is continuous and independent [86].…”

Section: Text Sentiment-analysis Resultsmentioning

confidence: 99%

Analysis of Differences in User Groups and Post Sentiment of COVID-19 Vaccine Hesitators in Chinese Social-Media Platforms

Liu

Zheng

2023

Healthcare

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(1) Background: The COVID-19 epidemic is still global and no specific drug has been developed for COVID-19. Vaccination can both prevent infection and limit the spread of the epidemic. Eliminating hesitation to the COVID-19 vaccine and achieving early herd immunity is a common goal for all countries. However, efforts in this area have not been significant and there is still a long way to go to eliminate vaccine hesitancy. (2) Objective: This study aimed to uncover differences in the characteristics and sentiments of COVID-19 vaccine hesitators on Chinese social-media platforms and to achieve a classification of vaccine-hesitant groups. (3) Methods: COVID-19-vaccine-hesitation posts and user characteristics were collected on the Sina Microblog platform for posting times spanning one year, and posts were identified for hesitation types. Logistic regression was used to conduct user-group analysis. The differences in user characteristics between the various types of COVID-19 vaccine posts were analysed according to four user characteristics: gender, address type, degree of personal-information disclosure, and whether they followed health topics. Sentiment analysis was conducted using sentiment analysis tools to calculate the sentiment scores and sentiment polarity of various COVID-19 vaccine posts, and the K–W test was used to uncover the sentiment differences between various types of COVID-19-vaccine-hesitation posts. (4) Results: There are differences in the types of COVID-19-vaccine-hesitation posts posted by users with different characteristics, and different types of COVID-19-vaccine-hesitation posts differ in terms of sentiment. Differences in user attributes and user behaviors are found across the different COVID-19-vaccine-hesitation types. Ultimately, two COVID-19-vaccine-hesitant user groups were identified: Body-related and Non-bodily-related. Users who posted body-related vaccine-hesitation posts are more often female, disclose more personal information and follow health topics on social-media platforms. Users who posted non-bodily-related posts are more often male, disclose less personal information, and do not follow health topics. The average sentiment score for all COVID-19-vaccine-hesitant-type posts is less than 0.45, with negative-sentiment posts outweighing positive- and neutral-sentiment posts in each type, among which the “Individual rights” type is the most negative. (5) Conclusions: This paper complements the application of user groups in the field of vaccine hesitation, and the results of the analysis of group characteristics and post sentiment can help to provide an in-depth and comprehensive analysis of the concerns and needs of COVID-19 vaccine hesitators. This will help public-health agencies to implement more targeted strategies to eliminate vaccine hesitancy and improve their work related to the COVID-19 vaccine, with far-reaching implications for COVID-19-vaccine promotion and vaccination.

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Section: Text Sentiment-analysis Resultsmentioning

confidence: 99%

Analysis of Differences in User Groups and Post Sentiment of COVID-19 Vaccine Hesitators in Chinese Social-Media Platforms

Liu

Zheng

2023

Healthcare

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“…The S 1 statistic is a robust measure based on the difference of medians (Yahaya et al, 2004; Ahad et al, 2016; Babu et al, 1999; Hubert et al 2012), in this case the median of edge weights for a defined phenotypic group. S 1 statistics were computed using the NCI cancer research data commons cloud resource, the ISB-CGC, per tissue type.…”

Section: Methodsmentioning

confidence: 99%

Patient-specific cell communication networks associate with disease progression in cancer

Gibbs

Aguilar

Thórsson

et al. 2021

Preprint

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The maintenance and function of tissues in health and disease depends on cell-cell communication. This work shows how high-level features, representing cell-cell communication, can be defined and used to associate certain signaling 'axes' with clinical outcomes. Using cell-sorted gene expression data, we generated a scaffold of cell-cell interactions and define a probabilistic method for creating per-patient weighted graphs based on gene expression and cell deconvolution results. With this method, we generated over 9,000 graphs for TCGA patient samples, each representing likely channels of intercellular communication in the tumor microenvironment. It was shown that particular edges were strongly associated with disease severity and progression, in terms of survival time and tumor stage. Within individual tumor types, there are predominant cell types and the collection of associated edges were found to be predictive of clinical phenotypes. Additionally, genes associated with differentially weighted edges were enriched in Gene Ontology terms associated with tissue structure and immune response. Code, data, and notebooks are provided to enable the application of this method to any expression dataset (https://github.com/IlyaLab/Pan-Cancer-Cell-Cell-Comm-Net).

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“…A modified S 1 statistic, a robust measure of differences, can be used for comparing phenotypic groups (Babu et al, 1999;Yahaya et al, 2004;Hubert et al, 2012;Ahad et al, 2016). The modified forms of the S 1 statistic are shown to better control type 1 errors.…”

Section: Association Of Network Features and Survival-based Phenotypesmentioning

confidence: 99%

Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer

et al. 2021

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The maintenance and function of tissues in health and disease depends on cell–cell communication. This work shows how high-level features, representing cell–cell communication, can be defined and used to associate certain signaling “axes” with clinical outcomes. We generated a scaffold of cell–cell interactions and defined a probabilistic method for creating per-patient weighted graphs based on gene expression and cell deconvolution results. With this method, we generated over 9,000 graphs for The Cancer Genome Atlas (TCGA) patient samples, each representing likely channels of intercellular communication in the tumor microenvironment (TME). It was shown that cell–cell edges were strongly associated with disease severity and progression, in terms of survival time and tumor stage. Within individual tumor types, there are predominant cell types, and the collection of associated edges were found to be predictive of clinical phenotypes. Additionally, genes associated with differentially weighted edges were enriched in Gene Ontology terms associated with tissue structure and immune response. Code, data, and notebooks are provided to enable the application of this method to any expression dataset (https://github.com/IlyaLab/Pan-Cancer-Cell-Cell-Comm-Net).

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Robustness of S1 statistic with Hodges-Lehmann for skewed distributions

Cited by 3 publications

References 8 publications

Analysis of Differences in User Groups and Post Sentiment of COVID-19 Vaccine Hesitators in Chinese Social-Media Platforms

Analysis of Differences in User Groups and Post Sentiment of COVID-19 Vaccine Hesitators in Chinese Social-Media Platforms

Patient-specific cell communication networks associate with disease progression in cancer

Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer

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