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Cancer Informatics
Published in Trevor F. Cox, Medical Statistics for Cancer Studies, 2022
The expression differences appear to be more or less constant over the mean log expression values. The figure was produced using the function limma::plotMD. The function limma::volcanoplot plots a “volcano plot”, as shown in Figure 10.9. Scaled p-values are plotted against log2 fold change, giving a visual summary, where we would be particularly looking at the p-values for large fold changes.
The Need of External Validation for Metabolomics Predictive Models
Published in Raquel Cumeras, Xavier Correig, Volatile organic compound analysis in biomedical diagnosis applications, 2018
Raquel Rodríguez-Pérez, Marta Padilla, Santiago Marco
Aside multiple hypothesis testing, the effect size (ES) of the parameter of interest is an important variable for data analysis. In metabolomics, the ES can be given by the strength of the differential abundance levels of the metabolites between the two condition groups, i.e., the difference between the means of both groups. The p-value by itself does not provide information about the ES of the metabolite under study. It may occur that a metabolite showing small ES is found statistically significant. Indeed, p-value (and also ES) depends on the sample size and it can be extremely unstable with a small number of samples (Halsey et al., 2015), as shown in Figure 8.5. Furthermore, an important effect may also be hidden due to a low number of studied subjects (Gardner and Altman, 1986). Some studies consider the ES along with the p-value to decide whether a metabolite is significant or not. For this, they establish one (or two) threshold for the estimated ES beyond which the metabolite is a candidate for significance. Then, such metabolite is found significant if it also has a small p-value. This means that two or three thresholds have to be pre-selected; one for p-value (usual 5%), and one or two for the ES. A ‘volcano plot’ representing the p-value (or equivalent quantity derived from multiple hypothesis testing) versus the ES, helps to visualize the regions of significant metabolites according to the three pre-selected thresholds (Patti et al., 2012, 2013) (Figure 8.6).
Analysis of DNA Microarrays in Clinical Trials
Published in Ding-Geng (Din) Chen, Karl E. Peace, Pinggao Zhang, Clinical Trial Data Analysis Using R and SAS, 2017
Ding-Geng (Din) Chen, Karl E. Peace, Pinggao Zhang
The p or B-values can reveal statistical significance, but say nothing about the size of an effect. The commonly used fold-change reveals the magnitude of the differential expression but not significance. A combined illustration to put both together is called a “volcano plot” Volcano Plots, as shown in Figure 12.4.
Monolith/Hydrogel composites as triamcinolone acetonide carriers for curing corneal neovascularization in mice by inhibiting the fibrinolytic system
Published in Drug Delivery, 2022
Cixin Huang, Xia Qi, Huilin Chen, Chao Wei, Xiaolin Qi, Hongwei Wang, Hua Gao
MS analysis was carried out in full-scan positive ion mode (m/z 375-1800) with the following conditions: mass resolution of first-stage MS, 60000; automatic gain control value, 3e6; the maximum injection time, 20 ms; collision energy, 35; MS/MS resolution, 45000; automatic gain control, 2e5; the maximum ion injection time, 100 ms; and the dynamic exclusion time, 30 s. Proteome Discover 2.4 (Thermo, USA) was used to screen the credible proteins according to the criteria of Score Sequest HT BBB 0. Unique Peptides ≥1. The volcano plot (gplot2 software package, (version 3.2.2)), cluster analysis map (P heatmap software package, (version 1.0.12)), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) bubble map (GGplot2 software package, (version 3.2.2)) were drawn by an analysis of differential protein expression using R software (version 4.2).
Comprehensive bioinformatics analysis reveals the hub genes and pathways associated with multiple myeloma
Published in Hematology, 2022
Shengli Zhao, Xiaoyi Mo, Zhenxing Wen, Lijuan Ren, Zhipeng Chen, Wei Lin, Qi Wang, Shaoxiong Min, Bailing Chen
First, the GSE125364 and GSE39754 datasets were standardized and integrated. The batch effect was removed to obtain the gene expression matrix of the samples. Principal component analysis (PCA) was used to visualize the spatial distribution of samples and evaluate their enrichment degree. Subsequently, the R package ‘limma (version 3.5.1)’ was used to identify DEGs between 215 MM samples and 9 healthy controls [15]. P-value was adjusted by the Benjamin and Hochberg method. The cut-off criteria were adjusted to P-value < 0.05 and |[log2FoldChange (log2FC)]| > 1. All genes were visualized by volcano plot using the R package ‘ggplot2 (version 3.3.2)’, and the top 100 significantly changed DEGs were shown by heatmap using the R package ‘pheatmap (version 0.7.7)’ [16].
Identification of critical chemical modifications by size exclusion chromatography of stressed antibody-target complexes with competitive binding
Published in mAbs, 2021
Rachel Liuqing Shi, Gang Xiao, Thomas M. Dillon, Arnold McAuley, Margaret S. Ricci, Pavel V. Bondarenko
The bound and unbound antibody species separated by SEC were subjected to peptide mapping LC-MS/MS analysis to identify the amino acid residues and their modifications (Supplemental Table S1). Sequence coverage of ~96% and 100% was found for the HC and LC of the antibody, respectively (Figure S1). Peptide mapping results including measured percentages of 420 modifications were summarized in the volcano plot, and a scatter-plot was used to identify changes in large data sets composed of replicate data (Figure 6). Volcano plot showed statistical significance (often defined as – log10 of p-value) and Fold Change on the y- and x-axes, respectively. As log2 Fold Change was used instead of Fold Change, Fold Change values ¼, ½, 1, 2, and 4 corresponded to log2 Fold Change values of −2, −1, 0, 1, and 2, respectively, symmetrical on x-axis.