Statistical analyses were implemented at the R platform (https://www.r-project.org/ ). Permutational multivariate analysis of variance (PERMANOVA) was performed with the adonis function of the vegan package, and the adonis P-value was generated based on 1,000 permutations. The method of effect size analysis was referred as Wang et al. (2020) (link). The no-metric multidimensional scaling (NMDS) analysis was used as the ordination methods (metaMDS function in vegan package) for compositional data. The Procrustes coordinates analysis and significance were generated using the procuste and procuste.randtest functions in vegan package. The principal component analysis (PCA) was performed and visualized using the ade4 package. The Wilcoxon rank-sum test was used to measure statistical differences in diversity and taxonomic levels between two cohorts. P-values were corrected for multiple testing using the Benjamini–Hochberg procedure.
R platform
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R is an open-source software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, and is highly extensible.
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Lab products found in correlation
Stata 14, by StataCorp (2 mentions)
Human 1.0 st array, by Thermo Fisher Scientific (1 mentions)
Human exon 1.0 st microarray, by Thermo Fisher Scientific (1 mentions)
Agilent 6000 rna nano chips, by Agilent Technologies (1 mentions)
One color microarray based gene expression analysis protocol system, by Agilent Technologies (1 mentions)
Trizol method, by Thermo Fisher Scientific (1 mentions)
Agilent 2100 bioanalyzer, by Agilent Technologies (1 mentions)
Prism 7, by GraphPad (1 mentions)
27 protocols using r platform
1
Microbial Community Diversity Analysis
2
Differential Gene Expression Analysis
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On the R platform (version 4.1.0; https://www.r-project.org/ ), the raw data were read using the read.maimages function of the limma package (23 (link)). Next, the raw data were background-subtracted using the backgroundCorrect function and normalized using the normalizeBetweenArrays function of the limma package. Quality reports were then generated using the arrayQualityMetrics package (24 (link)). Data outliers were detected using the distance between the arrays, and one sample, GSM2267025, was considered an outlier and therefore removed. The normalized data were further processed using the modelmatrix, makeContrasts, lmFit, eBayes, and topTable functions of the limma R package to obtain differentially expressed genes (DEGs) between the ND and HFD groups and between the wild-type (WT) and ob/ob mice. Genes with a fold change >1.5 and P value <0.05 were defined as DEGs.
3
Survival Analysis of Bladder Cancer
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Change-point regression was applied following Muggeo’s approach [6 (link)]. The study population was divided into subgroups on the basis of time intervals between primary TURB and BCG onset, which were then compared using chi-square and Mann–Whitney tests. The recurrence-free survival (RFS) and progression-free survival (PFS) were estimated using the log-rank method, and Kaplan–Meier curves were plotted. Additionally, Cox regression analyses were performed for both RFS and PFS. Due to differences in baseline patient characteristics in both groups, we used a 1:1 propensity-score-matched analysis (PSM) adjusted for gender, smoking status, age, presence of MP in the primary specimen, tumor focality and size, incidence of concomitant CIS, and reTURB status [7 (link)]. Additionally, to reduce the bias of unweighted estimators and adjust for covariate imbalance between treatment groups without losing patients, we performed inverse-probability weighting (IPW) using the same variables as in PSM [8 ].
Patients without an event or who died before an event were censored on the last date of follow-up. Times to events were calculated by taking the date of primary resection as time zero. Statistical significance was considered at p < 0.05. Statistical analyses were performed using STATA 14 (Stata Corp., College Station, TX, USA) and the R platform (R project, Vienna, Austria).
Patients without an event or who died before an event were censored on the last date of follow-up. Times to events were calculated by taking the date of primary resection as time zero. Statistical significance was considered at p < 0.05. Statistical analyses were performed using STATA 14 (Stata Corp., College Station, TX, USA) and the R platform (R project, Vienna, Austria).
4
Epidemiological Analysis of HPV Prevalence
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All HPV data from 2011 to 2019 were entered into an Excel spreadsheet and then analyzed on the R platform (www.r-project.org ) (v3.2.0) and R packages, and the overall and type-specific prevalence of HPV were calculated. All genotypes from single and multiple infections were computed individually. These data were also stratified by age(< 20 years, 20–24 years, 25–29 years,30–34 years, 35–39 years,40–44 years, 45–49 years, 50–54 years, 55–59 years, 60–64 years, 65–69 years,70–74 years, 75–79, years 80–84 years, ≥85 years). The HPV prevalence rate was estimated by a proportion and summarized as a percentage. The secular trends for HPV subtypes and for HPV infection rates of different ages were calculated using student t-test. p < 0.05 was considered statistically significant.
5
Epigenetic Associations with Ancestry and Nutrition
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Statistical analyses were done on the R platform (http://www.r-project.org ) and JMP Statistics (JMP Pro 10.0.0). We applied linear regression to test association between methylation M-values and ancestry (self-reported race). Since maternal age and cellular heterogeneity are known to influence methylation values [17 (link)–19 (link)], both maternal age and estimated proportions of lymphocytes and granulocytes were used as covariates in the regression model. Birth weight only has limited influence on DNA methylation and this was not added as a factor in the regression model [38 (link)]. For association with maternal nutritional factors, the M-values were regressed on maternal plasma vitamin D or folate with race, maternal age, and estimated blood cell counts as covariates. P-values were adjusted for false discovery using the Benjamini and Hochberg method [48 (link)]. Enrichment in cis-meQTLS among CpG sites with population difference was evaluated using the hypergeometric test. Gene ontology and pathway enrichment analysis was done using DAVID 6.7 [49 (link)] (http://david.abcc.ncifcrf.gov ).
6
Differential Gene Expression in PAH
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We used the R platform (R-project.org) to screen DEGs between PAH patients and healthy controls in the different groups. Fold change (FC) was obtained by calculating the ratio of the expression of each gene between PAH and control. Logarithmic operations with 2, 5, or 10 as base numbers were used to make easier calculations and more scientific comparisons. Genes with |log2FC| > 1 were considered as DEGs, and statistical differences were defined by adjusted P value < .05. DEGs with log2FC < 0 were considered down-regulated, whereas those with log2FC > 0 were considered up-regulated. The visualization of DEGs was realized using volcano plots and heat maps using the pheatmap package in R language. Female- and male-specific DEGs were identified by comparing the DEGs between groups.
7
Integrated Disease Pathway Analysis
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The R platform (http://www.rproject.org/) was used for canonical correlation analysis of expression data. After the canonical variables were produced, the top correlated canonical variables (r > 0.95) were identified to analysis the coexpressed individual genes. Two thresholds were set up to isolate correlated integrated disease pathways with r values > 0.5 and standard deviations > 0.2. Web-based DAVID tool (http://david.abcc.ncifcrf.gov/) was used for functional annotations and enrichment analysis; we used Homo sapiens genome as background. The “KEGG_PATHWAY” was selected for disease pathway enrichment analysis. Other parameters were automatically generated from DAVID.
Functional annotations were generated, and enrichment analyses were performed for the metabolic pathway genes using the web-based DAVID tool (http://david.abcc.ncifcrf.gov /). For the pathway enrichment analyses, the “KEGG_PATHWAY” was selected. The pathways with a P value < 0.01 were considered significant.
Functional annotations were generated, and enrichment analyses were performed for the metabolic pathway genes using the web-based DAVID tool (
8
Comparative Microbiome Analysis Pipeline
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Statistical analyses were performed using the R platform (https://www.r-project.org ), with Phyloseq50 (link). and Vegan51 packages. For alpha and beta diversity analyses, all the samples were rarefied to the lowest number of reads with the “rarefy_even_depth” Phyloseq command.
The similarity in OTU profiles among different communities was investigated through unweighted UniFrac distances and ordinated by principal coordinate analysis (PCoA). The non-parametric Wilcoxon rank-sum test was used to determine the statistically significant compositional and diversity differences between the control and patient groups, with p-values adjusted by Bonferroni correction for multiple testing. To detect the OTUs that statistically decreased or increased in each patient group compared to the control, the Metastats tool52 as implemented in MOTHUR was applied. For that purpose, samples containing less than 7,000 reads were discarded. The remaining reads were normalized with the “rarefy_even_depth” Phyloseq command, and the 500 more frequent OTUs were selected. Furthermore, the results were filtered for q-values < 0.06.
The similarity in OTU profiles among different communities was investigated through unweighted UniFrac distances and ordinated by principal coordinate analysis (PCoA). The non-parametric Wilcoxon rank-sum test was used to determine the statistically significant compositional and diversity differences between the control and patient groups, with p-values adjusted by Bonferroni correction for multiple testing. To detect the OTUs that statistically decreased or increased in each patient group compared to the control, the Metastats tool52 as implemented in MOTHUR was applied. For that purpose, samples containing less than 7,000 reads were discarded. The remaining reads were normalized with the “rarefy_even_depth” Phyloseq command, and the 500 more frequent OTUs were selected. Furthermore, the results were filtered for q-values < 0.06.
9
Prognostic Biomarkers in Pulmonary Pleomorphic Carcinoma
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Univariate Cox regression analysis was performed to evaluate the correlation between the expression level of each gene and recurrence-free survival (RFS) of patients with pathological stages I and II PPC. Only genes with q-value < 0.05 were considered candidates in the correlation analysis. Recurrence-free survival (RFS) curves were generated using the Kaplan–Meier method and compared using the log-rank test. P < 0.05 indicates a significant difference. The log fold-change in the expression level of each gene between the epithelial and sarcomatoid components was evaluated using the Wald test, and differences with q < 0.05 indicate a significant difference. The correlation between the normalized count of each gene and the level of PD-L1 in each sample was calculated using Pearson’s correlation, and statistical significance was defined as q < 0.05. Furthermore, the correlations of the levels of PD-L1 with the levels of CD274 mRNA, which encodes PD-L1, and the TMB were evaluated using the Mann–Whitney test, and p < 0.05 indicates a significant difference. Statistical analyses were performed using the R platform (version 3.5.1; https://www.r-project.org/ ) and associated packages.
10
Secular Trends in HPV and TCT
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All HPV and TCT data collected from 2014 to 2019 were combined into an Excel spreadsheet and then analyzed and plotted using the R platform (www.r-project.org ) (v3.2.0) and R packages.
The secular trends for TCT and HPV-positive infection rates and their distribution in different age groups were analyzed during 2014–2019 using the Student’s t-test. Comparisons between TCT results and HPV infection subtypes were performed. P < 0.001 was considered statistically significant.
The secular trends for TCT and HPV-positive infection rates and their distribution in different age groups were analyzed during 2014–2019 using the Student’s t-test. Comparisons between TCT results and HPV infection subtypes were performed. P < 0.001 was considered statistically significant.
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