Testing the Equality of Location Parameters for Skewed Distributions Using S 1 with High Breakdown Robust Scale Estimators

Yahaya, Sharipah Soaad Syed; Othman, Abdul Rahim; Keselman, H. J.

doi:10.1007/978-3-0348-7958-3_28

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…We chose this criterion since it was widely used by most robust statistic researchers (e.g. Keselman et al, 2000;Othman et al, 2004;Syed Yahaya et al, 2004;Wilcox et al, 2000) to judge robustness. Nevertheless, for Guo and Luh (2000), if the empirical Type I error rate do not exceed the 0.075 level, it is considered robust.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Robust Alternative to the t - Test

Yusof¹,

Abdullah²,

Yahaya³

et al. 2012

MAS

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Abstractt-test is a classical test statistics for testing the equality of two groups. However, this test is very sensitive to non-normality as well as variance heterogeneity. To overcome these problems, robust method such as F t and S 1 tests statistics can be used. This study proposed the use of a robust estimator that is trimmed mean as the central tendency measure in F t test and median as the central tendency measure in S 1 test when comparing the equality of two groups. The performance of the S 1 test with MAD n was able to give the most convincing result than the other methods. The F t with MAD n showed comparable results with the conventional methods. This study has shown some improvement in the statistical solution of detecting differences between location parameters. These modified methods may serve as alternatives to some other robust statistical methods which are unable to handle either the problem of non-normality, variance heterogeneity or unbalanced design.

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Section: Simulation Resultsmentioning

confidence: 99%

A Robust Alternative to the t - Test

Yusof¹,

Abdullah²,

Yahaya³

et al. 2012

MAS

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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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Testing the Equality of Location Parameters for Skewed Distributions Using S 1 with High Breakdown Robust Scale Estimators

Cited by 8 publications

References 10 publications

A Robust Alternative to the t - Test

A Robust Alternative to the t - Test

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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