ClusterSignificance: a bioconductor package facilitating statistical analysis of class cluster separations in dimensionality reduced data

Serviss, Jason T.; Gådin, Jesper R.; Eriksson, Per; Folkersen, Lasse; Grandér, Dan

doi:10.1093/bioinformatics/btx393

Cited by 12 publications

(10 citation statements)

References 9 publications

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“…We used the Mlp method of the ClusterSignificance package in R (Serviss et al, 2017) to quantify the separation between grades 1 and 3 in the PCA projection of the SVCA signatures.…”

Section: Model Validation Using Simulated Datamentioning

confidence: 99%

Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis

et al. 2019

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“…We used the Mlp method of the ClusterSignificance package in R (Serviss et al, 2017) to quantify the separation between grades 1 and 3 in the PCA projection of the SVCA signatures.…”

Section: Model Validation Using Simulated Datamentioning

confidence: 99%

Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis

et al. 2019

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“…Cluster separations for PCA were scored and then we evaluated the probability that the separation seen was due to chance. This was performed using a permutation method within r package ‘ClusterSignificance’ [25]. Statistical analysis was conducted using in-house scripts implemented using the mixOmics package in R (r-project.org) [26].…”

Section: Methodsmentioning

confidence: 99%

A robust method for performing 1H-NMR lipidomics in embryonic zebrafish

et al. 2021

Preprint

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The embryonic zebrafish is an ideal system for lipid analyses with relevance to many areas of bioscience research, including biomarkers and therapeutics. Research in this area has been hampered by difficulties in extracting, identifying and quantifying lipids. We employed 1H-NMR at 700MHz to profile lipids in developing zebrafish embryos. The optimal method for lipidomics in embryonic zebrafish incorporated rapid lipid extraction using chloroform and an environment without oxygen depletion. Pools of 10 embryos gave the most acceptable signal-to-noise ratio, and the inclusion of chorions in the sample had no significant effect on lipid abundances. Embryos, bisected into cranial (head and yolk sac) and caudal (tail) regions, were compared by principal component analysis and analysis of variance. The lipid spectra (including lipid annotation) are available in the public repository MetaboLights (MTBLS2396)

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“…Figure 2 shows example snapshots of the output data. Analyses were conducted using open source packages in R, FactoMineR 4 for PCA, Adegenet for DAPC 2 and ClusterSignificance 5 to test the statistical significance of cluster separation. Median p-value (and confidence interval) for correct posterior classification of control and test following DAPC were calculated with the native Wilcox test in R.…”

Section: Methodsmentioning

confidence: 99%

Discriminant Analysis of Principle Component analyses of Physiological Data

Haidar

Ball

Barrett‐Jolley

2020

Preprint

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There are many situations in physiological and pharmacological analyses where multivariate data is collected. Frequently these are analysed with t-tests and multiple (Bonferroni) comparisons or ANOVA with post-hoc test. Increasingly, even with more powerful computers many variables and it seems that feature reduction would be a useful approach. The most commonly used method is principle component analyses, but in this report we compare this to a technique developed for genetic analyses, discriminant analysis of principle component (DAPC) analyses. A simple to use and well-maintained library exists for DAPC analyses, Adegenet2, and using this we find that DAPC detects differences between synthetic physiological datasets with significantly greater accuracy than traditional PCA.

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ClusterSignificance: a bioconductor package facilitating statistical analysis of class cluster separations in dimensionality reduced data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 12 publications

References 9 publications

Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis

Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis

A robust method for performing 1H-NMR lipidomics in embryonic zebrafish

Discriminant Analysis of Principle Component analyses of Physiological Data

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