| Title: | Tools for Omics Data Analysis |
|---|---|
| Description: | The POMA package offers a comprehensive toolkit designed for omics data analysis, streamlining the process from initial visualization to final statistical analysis. Its primary goal is to simplify and unify the various steps involved in omics data processing, making it more accessible and manageable within a single, intuitive R package. Emphasizing on reproducibility and user-friendliness, POMA leverages the standardized SummarizedExperiment class from Bioconductor, ensuring seamless integration and compatibility with a wide array of Bioconductor tools. This approach guarantees maximum flexibility and replicability, making POMA an essential asset for researchers handling omics datasets. See https://github.com/pcastellanoescuder/POMAShiny. Paper: Castellano-Escuder et al. (2021) <doi:10.1371/journal.pcbi.1009148> for more details. |
| Authors: | Pol Castellano-Escuder [aut, cre]
|
| Maintainer: | Pol Castellano-Escuder <[email protected]> |
| License: | GPL-3 |
| Version: | 1.17.5 |
| Built: | 2024-11-20 03:37:22 UTC |
| Source: | https://github.com/bioc/POMA |
Compute Box-Cox normalization.
box_cox_transformation(data)box_cox_transformation(data)
data |
A single variable. |
Compute correlation p-values.
cor_pmat(x, method)cor_pmat(x, method)
x |
A data matrix. |
method |
Character indicating which correlation coefficient has to be computed. Options are "pearson" (default), "kendall" and "spearman". |
Detect decimal variables.
detect_decimals(data)detect_decimals(data)
data |
A data matrix (samples in rows). |
Flatten Correlation Matrix
flattenCorrMatrix(cormat, pmat = NULL)flattenCorrMatrix(cormat, pmat = NULL)
cormat |
Output from |
pmat |
Output from |
Return function to interpolate a continuous POMA color palette
poma_pal_c(palette = "nature")poma_pal_c(palette = "nature")
palette |
Character name of palette in poma_palettes |
Return function to interpolate a discrete POMA color palette
poma_pal_d(palette = "nature")poma_pal_d(palette = "nature")
palette |
Character name of palette in poma_palettes |
PomaBatch performs batch correction on a SummarizedExperiment object given a batch factor variable.
PomaBatch(data, batch, mod = NULL)PomaBatch(data, batch, mod = NULL)
data |
A |
batch |
Character. The name of the column in |
mod |
Character vector. Indicates the names of |
A SummarizedExperiment object with batch-corrected data.
Pol Castellano-Escuder
Leek JT, Johnson WE, Parker HS, Fertig EJ, Jaffe AE, Zhang Y, Storey JD, Torres LC (2023). sva: Surrogate Variable Analysis. doi:10.18129/B9.bioc.sva https://doi.org/10.18129/B9.bioc.sva
# Output is a batch corrected SummarizedExperiment object data <- POMA::st000284 # Example SummarizedExperiment object included in POMA data %>% PomaBatch(batch = "gender")# Output is a batch corrected SummarizedExperiment object data <- POMA::st000284 # Example SummarizedExperiment object included in POMA data %>% PomaBatch(batch = "gender")
PomaBoxplots generates boxplots and violin plots for samples and features. This function can be used for data exploration (e.g., comparison between pre and post normalized datasets).
PomaBoxplots( data, x = "samples", violin = FALSE, outcome = NULL, feature_name = NULL, theme_params = list(legend_title = FALSE, axis_x_rotate = TRUE) )PomaBoxplots( data, x = "samples", violin = FALSE, outcome = NULL, feature_name = NULL, theme_params = list(legend_title = FALSE, axis_x_rotate = TRUE) )
data |
A |
x |
Character. Options are "samples" (to visualize sample boxplots) and "features" (to visualize feature boxplots). Default is "samples". |
violin |
Logical. Indicates if violin plots should be displayed instead of boxplots. Default is FALSE. |
outcome |
Character. Indicates the name of the |
feature_name |
Character vector. Indicates the feature/s to display. Default is NULL (all features will be displayed). |
theme_params |
List. Indicates |
A ggplot object.
Pol Castellano-Escuder
data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaNorm() # Sample boxplots data %>% PomaBoxplots(x = "samples", violin = FALSE, outcome = NULL, feature_name = NULL, theme_params = list(axistext = "y")) # If too many samples # Sample boxplots with covariate as outcome data %>% PomaBoxplots(x = "samples", violin = FALSE, outcome = "gender", # change outcome feature_name = NULL, theme_params = list(axistext = "y")) # If too many samples # Sample violin plots data %>% PomaBoxplots(x = "samples", violin = TRUE, outcome = NULL, feature_name = NULL, theme_params = list(axistext = "y")) # If too many samples # All feature boxplots data %>% PomaBoxplots(x = "features", theme_params = list(axis_x_rotate = TRUE)) # Specific feature boxplots data %>% PomaBoxplots(x = "features", feature_name = c("ornithine", "orotate")) # Specific feature violin plots data %>% PomaBoxplots(x = "features", violin = TRUE, feature_name = c("ornithine", "orotate"))data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaNorm() # Sample boxplots data %>% PomaBoxplots(x = "samples", violin = FALSE, outcome = NULL, feature_name = NULL, theme_params = list(axistext = "y")) # If too many samples # Sample boxplots with covariate as outcome data %>% PomaBoxplots(x = "samples", violin = FALSE, outcome = "gender", # change outcome feature_name = NULL, theme_params = list(axistext = "y")) # If too many samples # Sample violin plots data %>% PomaBoxplots(x = "samples", violin = TRUE, outcome = NULL, feature_name = NULL, theme_params = list(axistext = "y")) # If too many samples # All feature boxplots data %>% PomaBoxplots(x = "features", theme_params = list(axis_x_rotate = TRUE)) # Specific feature boxplots data %>% PomaBoxplots(x = "features", feature_name = c("ornithine", "orotate")) # Specific feature violin plots data %>% PomaBoxplots(x = "features", violin = TRUE, feature_name = c("ornithine", "orotate"))
PomaClust performs a k-means clustering and plots the results in a classical multidimensional scaling (MDS) plot.
PomaClust( data, method = "euclidean", k = NA, k_max = floor(min(dim(data))/2), show_clusters = TRUE, labels = FALSE )PomaClust( data, method = "euclidean", k = NA, k_max = floor(min(dim(data))/2), show_clusters = TRUE, labels = FALSE )
data |
A |
method |
Character. Indicates the distance method to perform MDS. Options are "euclidean", "maximum", "manhattan", "canberra" and "minkowski". See |
k |
Numeric. Indicates the number of clusters (default is |
k_max |
Numeric. Indicates the number of clusters among which the optimal |
show_clusters |
Logical. Indicates if clusters should be plotted or not. |
labels |
Logical. Indicates if sample names should be plotted or not. |
A list with results including plots and tables.
Pol Castellano-Escuder
## Output is a list with objects `mds_coordinates` (tibble), `mds_plot` (ggplot2 object), `optimal_clusters_number` (numeric value), `optimal_clusters_number` (numeric value), and `optimal_clusters_plot` (ggplot2 object) data <- POMA::st000284 # Example SummarizedExperiment object included in POMA data %>% PomaClust(method = "euclidean", k = NA, k_max = floor(min(dim(data))/2), show_clusters = TRUE, labels = FALSE)## Output is a list with objects `mds_coordinates` (tibble), `mds_plot` (ggplot2 object), `optimal_clusters_number` (numeric value), `optimal_clusters_number` (numeric value), and `optimal_clusters_plot` (ggplot2 object) data <- POMA::st000284 # Example SummarizedExperiment object included in POMA data %>% PomaClust(method = "euclidean", k = NA, k_max = floor(min(dim(data))/2), show_clusters = TRUE, labels = FALSE)
PomaCorr computes all pairwise correlations in the data and generates a correlation plot.
PomaCorr( data, method = "pearson", cluster = TRUE, corrplot_shape = "square", sig_level = 1 )PomaCorr( data, method = "pearson", cluster = TRUE, corrplot_shape = "square", sig_level = 1 )
data |
A |
method |
Character. Indicates which correlation coefficient has to be computed. Options are "pearson" (default), "kendall", and "spearman". |
cluster |
Logical. Indicates whether the correlation plot will be ordered using the |
corrplot_shape |
Character. Indicates the visualization method of the correlation plot to be used. Allowed values are "square" (default) and "circle". |
sig_level |
Numeric. Indicates the significance level. If the correlation p-value exceeds this threshold, the corresponding correlation coefficient is considered insignificant, and that pair will be hidden in the correlation plot. The default is 1, meaning all correlations are included in the plot. For datasets with more than 500 features, this threshold is ignored, and all pairwise correlations are displayed in the plot. |
A list with the results.
Pol Castellano-Escuder
## Output is a list with objects `correlations` (tibble) and `corrplot` (ggplot2 object) data <- POMA::st000284 # Example SummarizedExperiment object included in POMA data %>% PomaCorr(method = "pearson")## Output is a list with objects `correlations` (tibble) and `corrplot` (ggplot2 object) data <- POMA::st000284 # Example SummarizedExperiment object included in POMA data %>% PomaCorr(method = "pearson")
SummarizedExperiment ObjectPomaCreateObject creates a SummarizedExperiment object from data frames.
PomaCreateObject(metadata = NULL, features = NULL, factor_levels = 10)PomaCreateObject(metadata = NULL, features = NULL, factor_levels = 10)
metadata |
Data frame. Metadata variables structured in columns. Sample ID must be the first column. |
features |
Matrix of features. Each feature is a column. |
factor_levels |
Numeric. Integer variables with less levels than indicated by this parameter will be treated as factors. |
A SummarizedExperiment object.
Pol Castellano-Escuder
Morgan M, Obenchain V, Hester J, Pagès H (2021). SummarizedExperiment: SummarizedExperiment container. R package version 1.24.0, https://bioconductor.org/packages/SummarizedExperiment.
data(iris) # Create metadata: Data frame with sample names and a group factor metadata <- data.frame(sample_id = paste0("sample_", 1:150), group = iris$Species) # Create features: `p` column data frame with features features <- iris[, 1:4] # Create a `SummarizedExperiment` object with `POMA` object <- PomaCreateObject(metadata = metadata, features = features)data(iris) # Create metadata: Data frame with sample names and a group factor metadata <- data.frame(sample_id = paste0("sample_", 1:150), group = iris$Species) # Create features: `p` column data frame with features features <- iris[, 1:4] # Create a `SummarizedExperiment` object with `POMA` object <- PomaCreateObject(metadata = metadata, features = features)
PomaDensity generates a density plot for samples and features. This function can be used for data exploration (e.g., comparison between pre and post normalized datasets).
PomaDensity( data, x = "samples", outcome = NULL, feature_name = NULL, theme_params = list(legend_title = FALSE) )PomaDensity( data, x = "samples", outcome = NULL, feature_name = NULL, theme_params = list(legend_title = FALSE) )
data |
A |
x |
Character. Options are "samples" (to visualize sample density plots) and "features" (to visualize feature density plots). Default is "samples". |
outcome |
Character. Indicates the name of the |
feature_name |
Character vector. Indicates the feature/s to display. Default is NULL (all features will be displayed). |
theme_params |
List. Indicates |
A ggplot object.
Pol Castellano-Escuder
data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaNorm() # Sample density plots data %>% PomaDensity(x = "samples", outcome = NULL) # Sample density plots with covariate as outcome data %>% PomaDensity(x = "samples", outcome = "gender") # change outcome # All feature density plots data %>% PomaDensity(x = "features", theme_params = list(legend_position = "none")) # Specific feature density plots data %>% PomaDensity(x = "features", feature_name = c("ornithine", "orotate"))data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaNorm() # Sample density plots data %>% PomaDensity(x = "samples", outcome = NULL) # Sample density plots with covariate as outcome data %>% PomaDensity(x = "samples", outcome = "gender") # change outcome # All feature density plots data %>% PomaDensity(x = "features", theme_params = list(legend_position = "none")) # Specific feature density plots data %>% PomaDensity(x = "features", feature_name = c("ornithine", "orotate"))
PomaDESeq estimates variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.
PomaDESeq(data, contrast = NULL, outcome = NULL, covs = NULL, adjust = "fdr")PomaDESeq(data, contrast = NULL, outcome = NULL, covs = NULL, adjust = "fdr")
data |
A |
contrast |
Character. Indicates the comparison. For example, "Group1-Group2" or "control-intervention". |
outcome |
Character. Indicates the name of the |
covs |
Character vector. Indicates the names of |
adjust |
Character. Indicates the multiple comparisons correction method. Options are: "fdr", "holm", "hochberg", "hommel", "bonferroni", "BH" and "BY". |
A tibble with the results.
Pol Castellano-Escuder
Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biology 15(12):550 (2014)
#library("airway") #data("airway") #se <- airway # ## Classic DESeq2 #DESeq_results <- se %>% # PomaDESeq(contrast = NULL, # outcome = "dex", # covs = NULL, # adjust = "fdr") # #DESeq_results %>% # dplyr::slice(1:10) # ### Volcano plot #DESeq_results %>% # dplyr::select(feature, log2FC, pvalue) %>% # PomaVolcano(labels = TRUE) # ### Boxplot of top features #se %>% # PomaBoxplots(x = "features", # outcome = "cell", # factorial variable to group by (e.g., treatment, sex, etc) # feature_name = DESeq_results$feature[1:10]) # ### Heatmap of top features #se[rownames(se) %in% DESeq_results$feature[1:10]] %>% # PomaHeatmap(covs = c("cell", "dex"), # covariates to plot (e.g., treatment, sex, etc) # feature_names = TRUE) # ## DESeq2 with covariates #DESeq_results <- se %>% # PomaDESeq(contrast = NULL, # outcome = "dex", # covs = "cell", # adjust = "fdr") # #DESeq_results %>% # dplyr::slice(1:10) # ### Volcano plot #DESeq_results %>% # dplyr::select(feature, log2FC, adj_pvalue) %>% # PomaVolcano(labels = TRUE, y_label = "-log10 (Adjusted P-value)") # ### Boxplot of top features #se %>% # PomaBoxplots(x = "features", # outcome = "dex", # factorial variable to group by (e.g., treatment, sex, etc) # feature_name = DESeq_results$feature[1:10]) # ### Heatmap of top features #se[rownames(se) %in% DESeq_results$feature[1:10]] %>% # PomaHeatmap(covs = c("cell", "dex"), # covariates to plot (e.g., treatment, sex, etc) # feature_names = TRUE) # ## DESeq2 with covariates and batch #DESeq_results <- se %>% # PomaDESeq(contrast = NULL, # outcome = "dex", # covs = c("batch", "cell"), # adjust = "fdr") # #DESeq_results %>% # dplyr::slice(1:10) # ### Volcano plot #DESeq_results %>% # dplyr::select(feature, log2FC, adj_pvalue) %>% # PomaVolcano(labels = TRUE, y_label = "-log10 (Adjusted P-value)") # ### Boxplot of top features #se %>% # PomaBoxplots(x = "features", # outcome = "cell", # factorial variable to group by (e.g., treatment, sex, etc) # feature_name = DESeq_results$feature[1:10]) # ### Heatmap of top features #se[rownames(se) %in% DESeq_results$feature[1:10]] %>% # PomaHeatmap(covs = c("cell", "dex"), # covariates to plot (e.g., treatment, sex, etc) # feature_names = TRUE)#library("airway") #data("airway") #se <- airway # ## Classic DESeq2 #DESeq_results <- se %>% # PomaDESeq(contrast = NULL, # outcome = "dex", # covs = NULL, # adjust = "fdr") # #DESeq_results %>% # dplyr::slice(1:10) # ### Volcano plot #DESeq_results %>% # dplyr::select(feature, log2FC, pvalue) %>% # PomaVolcano(labels = TRUE) # ### Boxplot of top features #se %>% # PomaBoxplots(x = "features", # outcome = "cell", # factorial variable to group by (e.g., treatment, sex, etc) # feature_name = DESeq_results$feature[1:10]) # ### Heatmap of top features #se[rownames(se) %in% DESeq_results$feature[1:10]] %>% # PomaHeatmap(covs = c("cell", "dex"), # covariates to plot (e.g., treatment, sex, etc) # feature_names = TRUE) # ## DESeq2 with covariates #DESeq_results <- se %>% # PomaDESeq(contrast = NULL, # outcome = "dex", # covs = "cell", # adjust = "fdr") # #DESeq_results %>% # dplyr::slice(1:10) # ### Volcano plot #DESeq_results %>% # dplyr::select(feature, log2FC, adj_pvalue) %>% # PomaVolcano(labels = TRUE, y_label = "-log10 (Adjusted P-value)") # ### Boxplot of top features #se %>% # PomaBoxplots(x = "features", # outcome = "dex", # factorial variable to group by (e.g., treatment, sex, etc) # feature_name = DESeq_results$feature[1:10]) # ### Heatmap of top features #se[rownames(se) %in% DESeq_results$feature[1:10]] %>% # PomaHeatmap(covs = c("cell", "dex"), # covariates to plot (e.g., treatment, sex, etc) # feature_names = TRUE) # ## DESeq2 with covariates and batch #DESeq_results <- se %>% # PomaDESeq(contrast = NULL, # outcome = "dex", # covs = c("batch", "cell"), # adjust = "fdr") # #DESeq_results %>% # dplyr::slice(1:10) # ### Volcano plot #DESeq_results %>% # dplyr::select(feature, log2FC, adj_pvalue) %>% # PomaVolcano(labels = TRUE, y_label = "-log10 (Adjusted P-value)") # ### Boxplot of top features #se %>% # PomaBoxplots(x = "features", # outcome = "cell", # factorial variable to group by (e.g., treatment, sex, etc) # feature_name = DESeq_results$feature[1:10]) # ### Heatmap of top features #se[rownames(se) %in% DESeq_results$feature[1:10]] %>% # PomaHeatmap(covs = c("cell", "dex"), # covariates to plot (e.g., treatment, sex, etc) # feature_names = TRUE)
PomaEnrichment performs enrichment analysis on a set of query gene symbols using specified methods and gene set collections. It allows for the analysis of over-representation (ORA) or gene set enrichment (GSEA) in various model organisms.
PomaEnrichment( genes, method = "ora", organism = "Homo sapiens", collection = "C5", universe = NULL, rank = NULL, pval_cutoff = 0.05, fdr_cutoff = 0.1, min_size = 2, max_size = if (method == "gsea") { length(genes) - 1 } else { NULL }, max_genes = 10 )PomaEnrichment( genes, method = "ora", organism = "Homo sapiens", collection = "C5", universe = NULL, rank = NULL, pval_cutoff = 0.05, fdr_cutoff = 0.1, min_size = 2, max_size = if (method == "gsea") { length(genes) - 1 } else { NULL }, max_genes = 10 )
genes |
Character vector. Set of query gene symbols. |
method |
Character. Enrichment method. Options are: 'ora' (simple over-representation analysis based on hypergeometric test) and 'gsea' (gene set enrichment analysis on a ranked list of genes). |
organism |
Character. Indicates the model organism name. Default is 'Homo sapiens'. Other options are: 'Anolis carolinensis', 'Bos taurus', 'Caenorhabditis elegans', 'Canis lupus familiaris', 'Danio rerio', 'Drosophila melanogaster', 'Equus caballus', 'Felis catus', 'Gallus gallus', 'Macaca mulatta', 'Monodelphis domestica', 'Mus musculus', 'Ornithorhynchus anatinus', 'Pan troglodytes', 'Rattus norvegicus', 'Saccharomyces cerevisiae', 'Schizosaccharomyces pombe 972h-', 'Sus scrofa', 'Xenopus tropicalis'. See |
collection |
Character. Indicates the gene set collection. Default is 'C5' (Gene Ontology gene sets). Other options are: 'C1' (positional gene sets), 'C2' (curated gene sets), 'C3' (regulatory target gene sets), 'C4' (computational gene sets), 'C6' (oncogenic signature gene sets), 'C7' (immunologic signature gene sets), 'C8' (cell type signature gene sets), 'H' (Hallmark gene sets). See |
universe |
Character vector. A universe from which 'genes' were selected. |
rank |
Numeric vector. Ranking factor to sort genes for GSEA (e.g., logFC, -log10(p-value), etc). |
pval_cutoff |
Numeric. Raw p-value cutoff on enrichment tests to report. |
fdr_cutoff |
Numeric. Adjusted p-value cutoff on enrichment tests to report. |
min_size |
Numeric. Minimal size of a gene set to test. All pathways below the threshold are excluded. |
max_size |
Numeric. Maximal size of a gene set to test. All pathways above the threshold are excluded. |
max_genes |
Numeric. The number of genes to retain from the |
A tibble with the enriched gene sets.
Pol Castellano-Escuder
# Example genes genes <- c("BRCA1", "TP53", "EGFR", "MYC", "PTEN") # Perform ORA on Gene Ontology (C5) gene sets for Homo sapiens PomaEnrichment( genes = genes, method = "ora", organism = "Homo sapiens", collection = "C5", pval_cutoff = 0.05, fdr_cutoff = 0.1, min_size = 10, max_size = 500) # Example genes with ranking factors (e.g., logFC values) genes <- c("Actb", "Gapdh", "Cdkn1a", "Cd44", "Pten") rank <- c(2.5, -1.8, 3.1, -2.2, 1.7) # Perform GSEA on Hallmark (H) gene sets for Mus musculus PomaEnrichment( genes = genes, method = "gsea", organism = "Mus musculus", collection = "H", rank = rank, pval_cutoff = 0.05, fdr_cutoff = 0.25, min_size = 15, max_size = 500)# Example genes genes <- c("BRCA1", "TP53", "EGFR", "MYC", "PTEN") # Perform ORA on Gene Ontology (C5) gene sets for Homo sapiens PomaEnrichment( genes = genes, method = "ora", organism = "Homo sapiens", collection = "C5", pval_cutoff = 0.05, fdr_cutoff = 0.1, min_size = 10, max_size = 500) # Example genes with ranking factors (e.g., logFC values) genes <- c("Actb", "Gapdh", "Cdkn1a", "Cd44", "Pten") rank <- c(2.5, -1.8, 3.1, -2.2, 1.7) # Perform GSEA on Hallmark (H) gene sets for Mus musculus PomaEnrichment( genes = genes, method = "gsea", organism = "Mus musculus", collection = "H", rank = rank, pval_cutoff = 0.05, fdr_cutoff = 0.25, min_size = 15, max_size = 500)
PomaHeatmap generates a heatmap.
PomaHeatmap( data, covs = NULL, sample_names = TRUE, feature_names = FALSE, show_legend = TRUE )PomaHeatmap( data, covs = NULL, sample_names = TRUE, feature_names = FALSE, show_legend = TRUE )
data |
A |
covs |
Character vector. Indicates the names of |
sample_names |
Logical. Indicates if sample names should be displayed or not. Default is TRUE. |
feature_names |
Logical. Indicates if feature names should be displayed or not. Default is FALSE. |
show_legend |
Logical. Indicates if legend should be displayed or not. Default is TRUE. |
A heatmap plot.
Pol Castellano-Escuder
data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaNorm() # Basic heatmap data %>% PomaHeatmap() # Heatmap with one covariate data %>% PomaHeatmap(covs = "factors") # Heatmap with two covariates data %>% PomaHeatmap(covs = c("factors", "smoking_condition"))data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaNorm() # Basic heatmap data %>% PomaHeatmap() # Heatmap with one covariate data %>% PomaHeatmap(covs = "factors") # Heatmap with two covariates data %>% PomaHeatmap(covs = c("factors", "smoking_condition"))
PomaImpute performs missing value imputation on a dataset using various imputation methods.
PomaImpute( data, zeros_as_na = FALSE, remove_na = TRUE, cutoff = 20, group_by = NULL, method = "knn" )PomaImpute( data, zeros_as_na = FALSE, remove_na = TRUE, cutoff = 20, group_by = NULL, method = "knn" )
data |
A |
zeros_as_na |
Logical. Indicates if the zeros in the data are missing values. Default is FALSE. |
remove_na |
Logical. Indicates if features with a percentage of missing values over the |
cutoff |
Numeric. Percentage of missing values allowed in each feature. |
group_by |
Character. Indicates the name of the |
method |
Character. The imputation method to use. Options include "none" (no imputation, replace missing values by zeros), "half_min" (replace missing values with half of the minimum value), "median" (replace missing values with the median), "mean" (replace missing values with the mean), "min" (replace missing values with the minimum value), "knn" (replace missing values using k-nearest neighbors imputation), and "random_forest" (replace missing values using random forest imputation). |
A SummarizedExperiment object without missing values.
Pol Castellano-Escuder
Armitage, E. G., Godzien, J., Alonso‐Herranz, V., López‐Gonzálvez, Á., & Barbas, C. (2015). Missing value imputation strategies for metabolomics data. Electrophoresis, 36(24), 3050-3060.
# Output is a imputed SummarizedExperiment object data <- POMA::st000284 # Example SummarizedExperiment object included in POMA # No sample normalization data %>% PomaImpute(zeros_as_na = FALSE, remove_na = TRUE, cutoff = 20, group_by = NULL, method = "knn")# Output is a imputed SummarizedExperiment object data <- POMA::st000284 # Example SummarizedExperiment object included in POMA # No sample normalization data %>% PomaImpute(zeros_as_na = FALSE, remove_na = TRUE, cutoff = 20, group_by = NULL, method = "knn")
PomaLasso performs LASSO, Ridge, and Elasticnet regression for feature selection and prediction purposes for binary outcomes.
PomaLasso( data, alpha = 1, ntest = NULL, nfolds = 10, lambda = NULL, labels = FALSE )PomaLasso( data, alpha = 1, ntest = NULL, nfolds = 10, lambda = NULL, labels = FALSE )
data |
A |
alpha |
Numeric. Indicates the elasticnet mixing parameter. alpha = 1 is the LASSO penalty and alpha = 0 is the Ridge penalty. |
ntest |
Numeric. Indicates the percentage of observations that will be used as test set. Default is NULL (no test set). |
nfolds |
Numeric. Indicates number of folds for cross-validation (default is 10). Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is nfolds = 3. |
lambda |
Numeric. Indicates the user supplied lambda sequence. Typical usage is to have the program compute its own lambda sequence based on |
labels |
Logical. Indicates if feature names should be plotted in coefficient plot or not. Default is FALSE. |
A list with results.
Pol Castellano-Escuder
Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22. URL http://www.jstatsoft.org/v33/i01/.
data <- POMA::st000336 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `coefficients` (tibble), `coefficients_plot` (ggplot2 object), `cv_plot` (ggplot2 object), and `model` (cv.glmnet object) # LASSO data %>% PomaLasso(alpha = 1, ntest = NULL, nfolds = 10, lambda = NULL, labels = TRUE) # Elasticnet data %>% PomaLasso(alpha = 0.5, ntest = NULL, nfolds = 10, lambda = NULL, labels = TRUE) # Ridge Regression data %>% PomaLasso(alpha = 0, ntest = NULL, nfolds = 10, lambda = NULL, labels = FALSE)data <- POMA::st000336 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `coefficients` (tibble), `coefficients_plot` (ggplot2 object), `cv_plot` (ggplot2 object), and `model` (cv.glmnet object) # LASSO data %>% PomaLasso(alpha = 1, ntest = NULL, nfolds = 10, lambda = NULL, labels = TRUE) # Elasticnet data %>% PomaLasso(alpha = 0.5, ntest = NULL, nfolds = 10, lambda = NULL, labels = TRUE) # Ridge Regression data %>% PomaLasso(alpha = 0, ntest = NULL, nfolds = 10, lambda = NULL, labels = FALSE)
limma
PomaLimma uses the classical limma package to compute differential expression analysis.
PomaLimma( data, contrast = NULL, outcome = NULL, covs = NULL, adjust = "fdr", block = NULL, weights = FALSE )PomaLimma( data, contrast = NULL, outcome = NULL, covs = NULL, adjust = "fdr", block = NULL, weights = FALSE )
data |
A |
contrast |
Character. Indicates the comparison. For example, "Group1-Group2" or "control-intervention". |
outcome |
Character. Indicates the name of the |
covs |
Character vector. Indicates the names of |
adjust |
Character. Indicates the multiple comparisons correction method. Options are: "fdr", "holm", "hochberg", "hommel", "bonferroni", "BH" and "BY". |
block |
Character. Specifies the name of the |
weights |
Logical. Indicates whether the limma model should estimate the relative quality weights for each group. See |
A tibble with the results.
Pol Castellano-Escuder
Matthew E. Ritchie, Belinda Phipson, Di Wu, Yifang Hu, Charity W. Law, Wei Shi, Gordon K. Smyth, limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Research, Volume 43, Issue 7, 20 April 2015, Page e47, https://doi.org/10.1093/nar/gkv007
data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaNorm() # Basic limma limma_results <- data %>% PomaLimma(contrast = "Healthy-CRC", covs = NULL, adjust = "fdr", block = NULL) limma_results %>% dplyr::slice(1:10) ## Volcano plot limma_results %>% dplyr::select(feature, log2FC, pvalue) %>% PomaVolcano(labels = TRUE) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "gender", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = limma_results$feature[1:10]) ## Heatmap of top features data[rownames(data) %in% limma_results$feature[1:10]] %>% PomaHeatmap(covs = c("gender", "smoking_condition", "alcohol_consumption"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) # Basic limma on alternative outcome SummarizedExperiment::colData(data)$gender <- factor(ifelse(SummarizedExperiment::colData(data)$gender == 0, "male", "female")) data %>% PomaLimma(contrast = "male-female", outcome = "gender", covs = NULL, adjust = "fdr", block = NULL) limma_results %>% dplyr::slice(1:10) ## Volcano plot limma_results %>% dplyr::select(feature, log2FC, pvalue) %>% PomaVolcano(labels = TRUE) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "gender", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = limma_results$feature[1:10]) ## Heatmap of top features data[rownames(data) %in% limma_results$feature[1:10]] %>% PomaHeatmap(covs = c("gender", "smoking_condition", "alcohol_consumption"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) # limma with one covariate data %>% PomaLimma(contrast = "Healthy-CRC", covs = "gender", adjust = "fdr", block = NULL) limma_results %>% dplyr::slice(1:10) ## Volcano plot limma_results %>% dplyr::select(feature, log2FC, pvalue) %>% PomaVolcano(labels = TRUE) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "gender", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = limma_results$feature[1:10]) ## Heatmap of top features data[rownames(data) %in% limma_results$feature[1:10]] %>% PomaHeatmap(covs = c("gender", "smoking_condition", "alcohol_consumption"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) # limma with two covariates data %>% PomaLimma(contrast = "Healthy-CRC", covs = c("gender", "age_at_consent"), adjust = "fdr", block = NULL) limma_results %>% dplyr::slice(1:10) ## Volcano plot limma_results %>% dplyr::select(feature, log2FC, pvalue) %>% PomaVolcano(labels = TRUE) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "gender", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = limma_results$feature[1:10]) ## Heatmap of top features data[rownames(data) %in% limma_results$feature[1:10]] %>% PomaHeatmap(covs = c("gender", "smoking_condition", "alcohol_consumption"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) # limma with replicates # data %>% # PomaLimma(contrast = "Healthy-CRC", # covs = NULL, # adjust = "fdr", # block = "replicate")data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaNorm() # Basic limma limma_results <- data %>% PomaLimma(contrast = "Healthy-CRC", covs = NULL, adjust = "fdr", block = NULL) limma_results %>% dplyr::slice(1:10) ## Volcano plot limma_results %>% dplyr::select(feature, log2FC, pvalue) %>% PomaVolcano(labels = TRUE) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "gender", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = limma_results$feature[1:10]) ## Heatmap of top features data[rownames(data) %in% limma_results$feature[1:10]] %>% PomaHeatmap(covs = c("gender", "smoking_condition", "alcohol_consumption"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) # Basic limma on alternative outcome SummarizedExperiment::colData(data)$gender <- factor(ifelse(SummarizedExperiment::colData(data)$gender == 0, "male", "female")) data %>% PomaLimma(contrast = "male-female", outcome = "gender", covs = NULL, adjust = "fdr", block = NULL) limma_results %>% dplyr::slice(1:10) ## Volcano plot limma_results %>% dplyr::select(feature, log2FC, pvalue) %>% PomaVolcano(labels = TRUE) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "gender", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = limma_results$feature[1:10]) ## Heatmap of top features data[rownames(data) %in% limma_results$feature[1:10]] %>% PomaHeatmap(covs = c("gender", "smoking_condition", "alcohol_consumption"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) # limma with one covariate data %>% PomaLimma(contrast = "Healthy-CRC", covs = "gender", adjust = "fdr", block = NULL) limma_results %>% dplyr::slice(1:10) ## Volcano plot limma_results %>% dplyr::select(feature, log2FC, pvalue) %>% PomaVolcano(labels = TRUE) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "gender", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = limma_results$feature[1:10]) ## Heatmap of top features data[rownames(data) %in% limma_results$feature[1:10]] %>% PomaHeatmap(covs = c("gender", "smoking_condition", "alcohol_consumption"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) # limma with two covariates data %>% PomaLimma(contrast = "Healthy-CRC", covs = c("gender", "age_at_consent"), adjust = "fdr", block = NULL) limma_results %>% dplyr::slice(1:10) ## Volcano plot limma_results %>% dplyr::select(feature, log2FC, pvalue) %>% PomaVolcano(labels = TRUE) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "gender", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = limma_results$feature[1:10]) ## Heatmap of top features data[rownames(data) %in% limma_results$feature[1:10]] %>% PomaHeatmap(covs = c("gender", "smoking_condition", "alcohol_consumption"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) # limma with replicates # data %>% # PomaLimma(contrast = "Healthy-CRC", # covs = NULL, # adjust = "fdr", # block = "replicate")
PomaLM performs a linear model on a SummarizedExperiment object.
PomaLM(data, x = NULL, y = NULL, adjust = "fdr")PomaLM(data, x = NULL, y = NULL, adjust = "fdr")
data |
A |
x |
Character vector. Indicates the names of independent variables. If it's NULL (default), all features will be used. |
y |
Character. Indicates the name of |
adjust |
Character. Multiple comparisons correction method to adjust p-values. Available options are: "fdr" (false discovery rate), "holm", "hochberg", "hommel", "bonferroni", "BH" (Benjamini-Hochberg), and "BY" (Benjamini-Yekutieli). |
A list with results including plots and tables.
Pol Castellano-Escuder
data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `lm_table` (tibble) and `regression_plot` (ggplot2 object) # Perform linear model with all features data %>% PomaLM() # Perform linear model with two features data %>% PomaLM(x = c("x1_methyladenosine", "x2_deoxyuridine"))data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `lm_table` (tibble) and `regression_plot` (ggplot2 object) # Perform linear model with all features data %>% PomaLM() # Perform linear model with two features data %>% PomaLM(x = c("x1_methyladenosine", "x2_deoxyuridine"))
PomaLMM performs linear mixed models on a SummarizedExperiment object.
PomaLMM(data, x = NULL, y = NULL, adjust = "fdr", clean_plot = FALSE)PomaLMM(data, x = NULL, y = NULL, adjust = "fdr", clean_plot = FALSE)
data |
A |
x |
Character vector. Indicates the names of |
y |
Character vector. Indicates the names of dependent variables. If it's NULL (default), all features will be used. |
adjust |
Character. Multiple comparisons correction method to adjust p-values. Available options are: "fdr" (false discovery rate), "holm", "hochberg", "hommel", "bonferroni", "BH" (Benjamini-Hochberg), and "BY" (Benjamini-Yekutieli). |
clean_plot |
Logical. Indicates if remove intercept and linear mixed model residues boxplots from the plot. Defasult is FALSE. |
A list with results including plots and tables. Table values indicate the percentage variance explained per variable.
Pol Castellano-Escuder
data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `lm_table` (tibble) and `regression_plot` (ggplot2 object) ## Perform linear mixed model with all features #data %>% # PomaLMM() # ## Perform linear mixed model with two features #data %>% # PomaLMM(y = c("x1_methyladenosine", "x1_methylhistamine")) # ## Perform linear mixed model with one random effect #data %>% # PomaLMM(x = "smoking_condition") # ## Perform linear mixed model with two random effects and two features #data %>% # PomaLMM(x = c("smoking_condition", "gender"), # y = c("x1_methyladenosine", "x1_methylhistamine")) # ## Perform linear mixed model with no random effects and two features, therefore, a linear model will be fitted #data %>% # PomaLMM(x = "age_at_consent", # Numerical, i.e., fixed effect # y = c("x1_methyladenosine", "x1_methylhistamine")) # ## Perform linear mixed model with no random effects and all features, therefore, a linear model will be fitted #data %>% # PomaLMM(x = "age_at_consent") # Numerical i.e., fixed effectdata <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `lm_table` (tibble) and `regression_plot` (ggplot2 object) ## Perform linear mixed model with all features #data %>% # PomaLMM() # ## Perform linear mixed model with two features #data %>% # PomaLMM(y = c("x1_methyladenosine", "x1_methylhistamine")) # ## Perform linear mixed model with one random effect #data %>% # PomaLMM(x = "smoking_condition") # ## Perform linear mixed model with two random effects and two features #data %>% # PomaLMM(x = c("smoking_condition", "gender"), # y = c("x1_methyladenosine", "x1_methylhistamine")) # ## Perform linear mixed model with no random effects and two features, therefore, a linear model will be fitted #data %>% # PomaLMM(x = "age_at_consent", # Numerical, i.e., fixed effect # y = c("x1_methyladenosine", "x1_methylhistamine")) # ## Perform linear mixed model with no random effects and all features, therefore, a linear model will be fitted #data %>% # PomaLMM(x = "age_at_consent") # Numerical i.e., fixed effect
PomaNorm performs data normalization using various normalization methods.
PomaNorm(data, sample_norm = "none", method = "log_pareto")PomaNorm(data, sample_norm = "none", method = "log_pareto")
data |
A |
sample_norm |
Character. Sample normalization method. Options include "none" (default), "sum", or "quantile". Quantile is often used when >100 samples. |
method |
Character. The normalization method to use. Options include "none" (no normalization), "auto_scaling" (autoscaling, i.e., Z-score normalization), "level_scaling" (level scaling), "log_scaling" (log scaling), "log" (log transformation), "vast_scaling" (vast scaling), "log_pareto" (log Pareto scaling), "min_max" (min-max), and "box_cox" (Box-Cox transformation). |
A SummarizedExperiment object with normalized data.
Pol Castellano-Escuder
Van den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006). Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC genomics, 7(1), 142.
# Output is a normalized SummarizedExperiment object data <- POMA::st000284 # Example SummarizedExperiment object included in POMA # No sample normalization data %>% PomaNorm(sample_norm = "none", method = "log_pareto") # Sum sample normalization data %>% PomaNorm(sample_norm = "sum", method = "log_pareto")# Output is a normalized SummarizedExperiment object data <- POMA::st000284 # Example SummarizedExperiment object included in POMA # No sample normalization data %>% PomaNorm(sample_norm = "none", method = "log_pareto") # Sum sample normalization data %>% PomaNorm(sample_norm = "sum", method = "log_pareto")
PomaOddsRatio calculates the Odds Ratios for each feature from a logistic regression model using the binary outcome (group/type must be a binary factor) as a dependent variable.
PomaOddsRatio(data, feature_name = NULL, covs = NULL, show_ci = TRUE)PomaOddsRatio(data, feature_name = NULL, covs = NULL, show_ci = TRUE)
data |
A |
feature_name |
Character vector. Indicates the name/s of feature/s that will be used to fit the model. If it's NULL (default), all variables will be included in the model. |
covs |
Character vector. Indicates the names of |
show_ci |
Logical. Indicates if the 95% confidence intervals will be plotted. Default is |
A list with results including plots and tables.
Pol Castellano-Escuder
data <- POMA::st000336 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `odds_ratio_table` (tibble) and `odds_ratio_plot` (ggplot2 object) data %>% PomaOddsRatio(feature_name = c("glutamic_acid", "glutamine", "glycine", "histidine"), covs = NULL, show_ci = TRUE) # With covariates data %>% PomaOddsRatio(feature_name = c("glutamic_acid", "glutamine", "glycine", "histidine"), covs = "steroids", show_ci = TRUE)data <- POMA::st000336 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `odds_ratio_table` (tibble) and `odds_ratio_plot` (ggplot2 object) data %>% PomaOddsRatio(feature_name = c("glutamic_acid", "glutamine", "glycine", "histidine"), covs = NULL, show_ci = TRUE) # With covariates data %>% PomaOddsRatio(feature_name = c("glutamic_acid", "glutamine", "glycine", "histidine"), covs = "steroids", show_ci = TRUE)
PomaOutliers analyses and removes statistical outliers from the data.
PomaOutliers( data, method = "euclidean", type = "median", outcome = NULL, coef = 2, labels = FALSE )PomaOutliers( data, method = "euclidean", type = "median", outcome = NULL, coef = 2, labels = FALSE )
data |
A |
method |
Character. Indicates the distance measure method to perform MDS. |
type |
Character. Indicates the type of outlier analysis to perform. Options are "median" (default) and "centroid". See |
outcome |
Character. Indicates the name of the |
coef |
Numeric. Indicates the outlier coefficient. Lower values are more sensitive to outliers while higher values are less restrictive about outliers. |
labels |
Logical. Indicates if sample names should to be plotted. |
A list with the results.
Pol Castellano-Escuder
data <- POMA::st000336 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `polygon_plot` (ggplot2 object), `distance_boxplot` (ggplot2 object), `outliers` (tibble), and `data` (outlier-free SummarizedExperiment) outlier_results <- data %>% PomaOutliers(method = "euclidean", type = "median", outcome = NULL, coef = 2, labels = FALSE) outlier_results$data # cleaned SummarizedExperiment object ## Change oulier group factor outlier_results2 <- data %>% PomaOutliers(method = "euclidean", type = "median", outcome = "steroids", coef = 2, labels = FALSE) outlier_results2$data # cleaned SummarizedExperiment objectdata <- POMA::st000336 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `polygon_plot` (ggplot2 object), `distance_boxplot` (ggplot2 object), `outliers` (tibble), and `data` (outlier-free SummarizedExperiment) outlier_results <- data %>% PomaOutliers(method = "euclidean", type = "median", outcome = NULL, coef = 2, labels = FALSE) outlier_results$data # cleaned SummarizedExperiment object ## Change oulier group factor outlier_results2 <- data %>% PomaOutliers(method = "euclidean", type = "median", outcome = "steroids", coef = 2, labels = FALSE) outlier_results2$data # cleaned SummarizedExperiment object
PomaPCA performs a principal components analysis on the given SummarizedExperiment object.
PomaPCA( data, outcome = NULL, center = TRUE, scale = TRUE, ncomp = 4, labels = FALSE, ellipse = FALSE, load_length = 1 )PomaPCA( data, outcome = NULL, center = TRUE, scale = TRUE, ncomp = 4, labels = FALSE, ellipse = FALSE, load_length = 1 )
data |
A |
outcome |
Character. Indicates the name of the |
center |
Logical. Indicates whether the variables should be shifted to be zero centered. Default is TRUE. |
scale |
Logical. Indicates whether the variables should be scaled to have unit variance before the analysis takes place. Default is TRUE. |
ncomp |
Numeric. Number of components to be included in the results. Default is 4. |
labels |
Logical. Indicates if sample names should be displayed. |
ellipse |
Logical. Indicates whether a 95 percent confidence interval ellipse should be displayed in score plot and biplot. Default is FALSE. |
load_length |
Numeric. Indicates the length of biplot loading arrows. Value between 1 and 2. Default is 1. |
A list with results including plots and tables.
Pol Castellano-Escuder
data <- POMA::st000336 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `factors` (tibble wth principal components), `eigenvalues` (tibble), `loadings` (tibble), `factors_plot` (ggplot2 object with PCA plot), `eigenvalues_plot` (ggplot2 object with eigenvalues plot), `loadings_plot` (ggplot2 object), and `biplot` (ggplot2 object) # Default outcome (first factor variable in `colData`) data %>% PomaPCA(outcome = NULL, center = TRUE, scale = TRUE, labels = FALSE, ellipse = FALSE) # Alternative outcome data %>% PomaPCA(outcome = "steroids", center = TRUE, scale = TRUE, labels = FALSE, ellipse = FALSE)data <- POMA::st000336 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `factors` (tibble wth principal components), `eigenvalues` (tibble), `loadings` (tibble), `factors_plot` (ggplot2 object with PCA plot), `eigenvalues_plot` (ggplot2 object with eigenvalues plot), `loadings_plot` (ggplot2 object), and `biplot` (ggplot2 object) # Default outcome (first factor variable in `colData`) data %>% PomaPCA(outcome = NULL, center = TRUE, scale = TRUE, labels = FALSE, ellipse = FALSE) # Alternative outcome data %>% PomaPCA(outcome = "steroids", center = TRUE, scale = TRUE, labels = FALSE, ellipse = FALSE)
PomaPCR performs Principal Components Regression.
PomaPCR(data, center = TRUE, scale = TRUE, ncomp = 2, y = NULL, adjust = "fdr")PomaPCR(data, center = TRUE, scale = TRUE, ncomp = 2, y = NULL, adjust = "fdr")
data |
A |
center |
Logical. Indicates whether the variables should be shifted to be zero centered. Default is TRUE. |
scale |
Logical. Indicates whether the variables should be scaled to have unit variance before the analysis takes place. Default is TRUE. |
ncomp |
Numeric. Indicates the number of principal components used as predictors in the model. Default is 2. |
y |
Character. Indicates the name of |
adjust |
Character. Multiple comparisons correction method to adjust p-values. Available options are: "fdr" (false discovery rate), "holm", "hochberg", "hommel", "bonferroni", "BH" (Benjamini-Hochberg), and "BY" (Benjamini-Yekutieli). |
A tibble with the results.
Pol Castellano-Escuder
data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaNorm() # PCR with 2 components and the default outcome (1st column of `colData`) data %>% PomaPCR(center = TRUE, scale = TRUE, ncomp = 2, y = NULL, adjust = "fdr") # PCR with 2 components and alternative outcome data %>% PomaPCR(center = TRUE, scale = TRUE, ncomp = 2, y = "age_at_consent", adjust = "fdr") # PCR with 20 components and alternative outcome data %>% PomaPCR(center = TRUE, scale = TRUE, ncomp = 20, y = "age_at_consent", adjust = "fdr")data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaNorm() # PCR with 2 components and the default outcome (1st column of `colData`) data %>% PomaPCR(center = TRUE, scale = TRUE, ncomp = 2, y = NULL, adjust = "fdr") # PCR with 2 components and alternative outcome data %>% PomaPCR(center = TRUE, scale = TRUE, ncomp = 2, y = "age_at_consent", adjust = "fdr") # PCR with 20 components and alternative outcome data %>% PomaPCR(center = TRUE, scale = TRUE, ncomp = 20, y = "age_at_consent", adjust = "fdr")
PomaPLS performs Partial Least Squares (PLS) regression, Partial Least Squares Discriminant Analysis (PLS-DA) to classify samples, and Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) to classify samples (supervised analysis) and select variables.
PomaPLS( data, method = "pls", y = NULL, ncomp = 5, labels = FALSE, ellipse = TRUE, cross_validation = FALSE, validation = "Mfold", folds = 5, nrepeat = 10, vip = 1, num_features = 10, theme_params = list() )PomaPLS( data, method = "pls", y = NULL, ncomp = 5, labels = FALSE, ellipse = TRUE, cross_validation = FALSE, validation = "Mfold", folds = 5, nrepeat = 10, vip = 1, num_features = 10, theme_params = list() )
data |
A |
method |
Character. PLS method. Options include "pls", "plsda", and "splsda". |
y |
Character. Indicates the name of |
ncomp |
Numeric. Number of components in the model. Default is 5. |
labels |
Logical. Indicates if sample names should be displayed. |
ellipse |
Logical. Indicates whether a 95 percent confidence interval ellipse should be displayed. Default is TRUE. |
cross_validation |
Logical. Indicates if cross-validation should be performed for PLS-DA ("plsda") and sPLS-DA ("splsda") methods. Default is FALSE. |
validation |
Character. (Only for "plsda" and "splsda" methods). Indicates the cross-validation method. Options are "Mfold" and "loo" (Leave-One-Out). |
folds |
Numeric. (Only for "plsda" and "splsda" methods). Number of folds for "Mfold" cross-validation method (default is 5). If the validation method is "loo", this value is set to 1. |
nrepeat |
Numeric. (Only for "plsda" and "splsda" methods). Number of times the cross-validation process is repeated. |
vip |
Numeric. (Only for "plsda" method). Indicates the variable importance in the projection (VIP) cutoff. |
num_features |
Numeric. (Only for "splsda" method). Number of features to discriminate groups. |
theme_params |
List. Indicates |
A list with results including plots and tables.
Pol Castellano-Escuder
data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `factors` (tibble), `factors_plot` (ggplot2 object), `loadings` (tibble), and `loadings_plot` (ggplot2 object) # PLS data %>% PomaPLS(method = "pls", y = NULL, ncomp = 5, labels = FALSE, ellipse = FALSE) ## Output is a list with objects `factors` (tibble), `factors_plot` (ggplot2 object), `vip_values` (tibble), and `vip_plot` (ggplot2 object) # PLS-DA data %>% PomaPLS(method = "plsda", y = NULL, ncomp = 5, labels = FALSE, ellipse = TRUE, cross_validation = FALSE, vip = 1) # Alternative outcome (dependent variable) data %>% PomaPLS(method = "plsda", y = "gender", ncomp = 5, labels = FALSE, ellipse = TRUE, cross_validation = FALSE, vip = 1) ## Output is a list with objects `factors` (tibble), `factors_plot` (ggplot2 object), `vip_values` (tibble), `vip_plot` (ggplot2 object), `errors` (tibble), and `errors_plot` (ggplot2 object) # PLS-DA with Cross-Validation data %>% PomaPLS(method = "plsda", y = NULL, ncomp = 5, labels = FALSE, ellipse = TRUE, cross_validation = TRUE, validation = "Mfold", folds = 5, nrepeat = 10, vip = 1) ## Output is a list with objects `factors` (tibble), `factors_plot` (ggplot2 object), `selected_features` (tibble), and `selected_features_plot` (ggplot2 object) # sPLS-DA data %>% PomaPLS(method = "splsda", y = NULL, ncomp = 5, labels = FALSE, ellipse = TRUE, cross_validation = FALSE, num_features = 10) ## Output is a list with objects `factors` (tibble), `factors_plot` (ggplot2 object), `selected_features` (tibble), `selected_features_plot` (ggplot2 object), `errors` (tibble), `errors_plot` (ggplot2 object), `optimal_components` (numeric value), and `optimal_features` (vector with optimal features per component) # sPLS-DA with Cross-Validation data %>% PomaPLS(method = "splsda", y = NULL, ncomp = 3, labels = FALSE, ellipse = TRUE, cross_validation = TRUE, validation = "Mfold", folds = 5, nrepeat = 10, num_features = 10)data <- POMA::st000284 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `factors` (tibble), `factors_plot` (ggplot2 object), `loadings` (tibble), and `loadings_plot` (ggplot2 object) # PLS data %>% PomaPLS(method = "pls", y = NULL, ncomp = 5, labels = FALSE, ellipse = FALSE) ## Output is a list with objects `factors` (tibble), `factors_plot` (ggplot2 object), `vip_values` (tibble), and `vip_plot` (ggplot2 object) # PLS-DA data %>% PomaPLS(method = "plsda", y = NULL, ncomp = 5, labels = FALSE, ellipse = TRUE, cross_validation = FALSE, vip = 1) # Alternative outcome (dependent variable) data %>% PomaPLS(method = "plsda", y = "gender", ncomp = 5, labels = FALSE, ellipse = TRUE, cross_validation = FALSE, vip = 1) ## Output is a list with objects `factors` (tibble), `factors_plot` (ggplot2 object), `vip_values` (tibble), `vip_plot` (ggplot2 object), `errors` (tibble), and `errors_plot` (ggplot2 object) # PLS-DA with Cross-Validation data %>% PomaPLS(method = "plsda", y = NULL, ncomp = 5, labels = FALSE, ellipse = TRUE, cross_validation = TRUE, validation = "Mfold", folds = 5, nrepeat = 10, vip = 1) ## Output is a list with objects `factors` (tibble), `factors_plot` (ggplot2 object), `selected_features` (tibble), and `selected_features_plot` (ggplot2 object) # sPLS-DA data %>% PomaPLS(method = "splsda", y = NULL, ncomp = 5, labels = FALSE, ellipse = TRUE, cross_validation = FALSE, num_features = 10) ## Output is a list with objects `factors` (tibble), `factors_plot` (ggplot2 object), `selected_features` (tibble), `selected_features_plot` (ggplot2 object), `errors` (tibble), `errors_plot` (ggplot2 object), `optimal_components` (numeric value), and `optimal_features` (vector with optimal features per component) # sPLS-DA with Cross-Validation data %>% PomaPLS(method = "splsda", y = NULL, ncomp = 3, labels = FALSE, ellipse = TRUE, cross_validation = TRUE, validation = "Mfold", folds = 5, nrepeat = 10, num_features = 10)
PomaRandForest performs classification random forest. This method can be used both for prediction and variable selection.
PomaRandForest( data, ntest = NULL, ntree = 500, mtry = floor(sqrt(ncol(t(SummarizedExperiment::assay(data))))), nodesize = 1, nvar = 20 )PomaRandForest( data, ntest = NULL, ntree = 500, mtry = floor(sqrt(ncol(t(SummarizedExperiment::assay(data))))), nodesize = 1, nvar = 20 )
data |
A |
ntest |
Numeric. Indicates the percentage of observations that will be used as test set. Default is NULL (no test set). |
ntree |
Numeric. Indicates the number of trees to grow. |
mtry |
Numeric. Indicates the number of variables randomly sampled as candidates at each split. This value is set sqrt(p) (where p is number of variables in data) by default. |
nodesize |
Numeric. Indicates the minimum size of terminal nodes. Default is 1. |
nvar |
Numeric. Indicates the number of variables to show in the Gini Index plot. |
A list with results including plots and tables.
Pol Castellano-Escuder
A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18–22.
data <- POMA::st000336 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `MeanDecreaseGini` (tibble), `MeanDecreaseGini_plot` (ggplot2 object), `oob_error` (tibble), `error_tree` (ggplot2 object), and `model` (randomForest object) data %>% PomaRandForest(ntree = 500, mtry = floor(sqrt(ncol(t(SummarizedExperiment::assay(data))))), nodesize = 1, nvar = 20)data <- POMA::st000336 %>% # Example SummarizedExperiment object included in POMA PomaImpute() %>% PomaNorm() ## Output is a list with objects `MeanDecreaseGini` (tibble), `MeanDecreaseGini_plot` (ggplot2 object), `oob_error` (tibble), `error_tree` (ggplot2 object), and `model` (randomForest object) data %>% PomaRandForest(ntree = 500, mtry = floor(sqrt(ncol(t(SummarizedExperiment::assay(data))))), nodesize = 1, nvar = 20)
PomaRankProd performs the Rank Product (or Rank Sum) method to identify differentially expressed genes.
PomaRankProd(data, logged = TRUE, paired = NA, cutoff = 1, method = "pfp")PomaRankProd(data, logged = TRUE, paired = NA, cutoff = 1, method = "pfp")
data |
A |
logged |
Logical. Indicates if data should be log transformed first. |
paired |
Numeric. Indicates the number of random pairs generated in the function, if set to NA (default), the odd integer closer to the square of the number of replicates is used. |
cutoff |
Numeric. Indicates the pfp/pvalue threshold value used to select features. Default is 1 to include all features. |
method |
Character. Indicates the method to identify features. "pfp" uses percentage of false prediction, which is a default setting. "pval" uses p-values which is less stringent than pfp. |
A list with the results. Objects in the list are up_regulated (tibble) and down_regulated (tibble).
Pol Castellano-Escuder
Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P.(2004) Rank Products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letter, 57383-92
Hong, F., Breitling, R., McEntee, W.C., Wittner, B.S., Nemhauser, J.L., Chory, J. (2006). RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis Bioinformatics. 22(22):2825-2827
Del Carratore, F., Jankevics, A., Eisinga, R., Heskes, T., Hong, F. & Breitling, R. (2017). RankProd 2.0: a refactored Bioconductor package for detecting differentially expressed features in molecular profiling datasets. Bioinformatics. 33(17):2774-2775
data <- POMA::st000336 %>% # Example SummarizedExperiment object included in POMA PomaImpute() ## Output is a list with objects `up_regulated` (tibble with up regulated features) and `down_regulated` (tibble with down regulated features) ## Perform on no-scaled object to avoid negative values data %>% PomaRankProd(method = "pfp")data <- POMA::st000336 %>% # Example SummarizedExperiment object included in POMA PomaImpute() ## Output is a list with objects `up_regulated` (tibble with up regulated features) and `down_regulated` (tibble with down regulated features) ## Perform on no-scaled object to avoid negative values data %>% PomaRankProd(method = "pfp")
PomaUMAP performs a dimension reduction of the data using the Uniform Manifold Approximation and Projection (UMAP) method. See ?uwot::umap() for more.
PomaUMAP( data, n_neighbors = floor(sqrt(nrow(data))), n_components = 2, metric = "euclidean", pca = NULL, min_dist = 0.01, spread = 1, hdbscan_minpts = floor(nrow(data) * 0.05), show_clusters = TRUE, hide_noise = TRUE, labels = FALSE, outcome = NULL, theme_params = list(legend_title = TRUE, legend_position = "bottom") )PomaUMAP( data, n_neighbors = floor(sqrt(nrow(data))), n_components = 2, metric = "euclidean", pca = NULL, min_dist = 0.01, spread = 1, hdbscan_minpts = floor(nrow(data) * 0.05), show_clusters = TRUE, hide_noise = TRUE, labels = FALSE, outcome = NULL, theme_params = list(legend_title = TRUE, legend_position = "bottom") )
data |
A |
n_neighbors |
Numeric. Indicates the size of local neighborhood (sample points) used for manifold approximation. |
n_components |
Numeric. Indicates the dimension of the space to embed into. |
metric |
Character. Indicates the distance measure method to find nearest neighbors. Options are "euclidean", "cosine", "manhattan", "hamming" and "correlation". See |
pca |
If not NULL (default), reduce data to this number of columns using PCA before UMAP. |
min_dist |
Numeric. Indicates the effective minimum distance between embedded points. |
spread |
Numeric. Indicates the effective scale of embedded points. |
hdbscan_minpts |
Numeric. Indicates the minimum size of clusters. See |
show_clusters |
Logical. Indicates if clusters computed with HDBSCAN method should be plotted or not. |
hide_noise |
Logical. Specifies whether to hide Cluster 0 in the plot. In HDBSCAN, Cluster 0 is typically regarded as "noise." |
labels |
Logical. Indicates if sample names should be plotted or not. |
outcome |
Character. Has no effect on the analysis. Indicates the name of the |
theme_params |
List. Indicates |
A list with results including plots and tables.
Pol Castellano-Escuder
McInnes, L., Healy, J., & Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426.
Campello, R. J., Moulavi, D., & Sander, J. (2013, April). Density-based clustering based on hierarchical density estimates. In Pacific-Asia conference on knowledge discovery and data mining (pp. 160-172). Springer, Berlin, Heidelberg.
data <- POMA::st000284 # Example SummarizedExperiment object included in POMA ## Output is a list with objects `umap_embeddings` (tibble) and `umap_plot` (ggplot2 object) data %>% PomaNorm() %>% PomaUMAP(metric = "euclidean", pca = NULL, min_dist = 0.01, spread = 1, hdbscan_minpts = floor(nrow(data) * 0.05), show_clusters = TRUE, hide_noise = TRUE, labels = FALSE, outcome = NULL)data <- POMA::st000284 # Example SummarizedExperiment object included in POMA ## Output is a list with objects `umap_embeddings` (tibble) and `umap_plot` (ggplot2 object) data %>% PomaNorm() %>% PomaUMAP(metric = "euclidean", pca = NULL, min_dist = 0.01, spread = 1, hdbscan_minpts = floor(nrow(data) * 0.05), show_clusters = TRUE, hide_noise = TRUE, labels = FALSE, outcome = NULL)
PomaUnivariate performs parametric and non-parametric univariate statistical tests on a SummarizedExperiment object to compare groups or conditions. Available methods include T-test, ANOVA, ANCOVA, Mann Whitney U Test (Wilcoxon Rank Sum Test), and Kruskal-Wallis.
PomaUnivariate( data, method = "ttest", covs = NULL, error = NULL, paired = FALSE, var_equal = FALSE, adjust = "fdr", run_post_hoc = TRUE )PomaUnivariate( data, method = "ttest", covs = NULL, error = NULL, paired = FALSE, var_equal = FALSE, adjust = "fdr", run_post_hoc = TRUE )
data |
A |
method |
Character. The univariate statistical test to be performed. Available options include "ttest" (T-test), "anova" (analysis of variance), "mann" (Wilcoxon rank-sum test), and "kruskal" (Kruskal-Wallis test). |
covs |
Character vector. Indicates the names of |
error |
Character vector. Indicates the name of a |
paired |
Logical. Indicates if the data is paired or not. Default is FALSE. |
var_equal |
Logical. Indicates if the data variances are assumed to be equal or not. Default is FALSE. |
adjust |
Character. Multiple comparisons correction method to adjust p-values. Available options are: "fdr" (false discovery rate), "holm", "hochberg", "hommel", "bonferroni", "BH" (Benjamini-Hochberg), and "BY" (Benjamini-Yekutieli). |
run_post_hoc |
Logical. Indicates if computing post-hoc tests or not. Setting this parameter to FALSE can save time for large datasets. |
A tibble for "ttest" and "mann". A list for "anova" and "kruskal".
Pol Castellano-Escuder
# Two groups ## Output columns: feature, fold_change, diff_means, pvalue, adj_pvalue, mean_xxx (group 1) mean_yyy (group 2), sd_xxx (group 1), sd_yyy (group 2) data <- POMA::st000336 # Example SummarizedExperiment object included in POMA ## Perform T-test ttest_results <- st000336 %>% PomaImpute() %>% PomaUnivariate(method = "ttest", paired = FALSE, var_equal = FALSE, adjust = "fdr") ttest_results %>% dplyr::slice(1:10) ## Volcano plot ttest_results %>% dplyr::select(feature, fold_change, pvalue) %>% PomaVolcano(labels = TRUE) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "group", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = ttest_results$feature[1:10]) ## Heatmap of top features data[rownames(data) %in% ttest_results$feature[1:10]] %>% PomaHeatmap(covs = c("group"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) ## Perform Mann-Whitney U test mann_whitney_results <- st000336 %>% PomaImpute() %>% PomaUnivariate(method = "mann", paired = FALSE, var_equal = FALSE, adjust = "fdr") mann_whitney_results %>% dplyr::slice(1:10) ## Volcano plot mann_whitney_results %>% dplyr::select(feature, fold_change, pvalue) %>% PomaVolcano(labels = TRUE) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "group", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = mann_whitney_results$feature[1:10]) ## Heatmap of top features data[rownames(data) %in% mann_whitney_results$feature[1:10]] %>% PomaHeatmap(covs = c("group"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) # More than 2 groups ## Output is a list with objects `result` and `post_hoc_tests` data <- POMA::st000284 # Example SummarizedExperiment object included in POMA ## Perform Two-Way ANOVA anova_results <- data %>% PomaUnivariate(method = "anova", covs = c("gender"), error = NULL, adjust = "fdr", run_post_hoc = TRUE) anova_results$result %>% dplyr::slice(1:10) anova_results$post_hoc_tests %>% dplyr::slice(1:10) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "factors", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = anova_results$result$feature[1:10]) ## Boxplot of top significant pairwise features (after posthoc test) data %>% PomaBoxplots(x = "features", outcome = "factors", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = unique(anova_results$post_hoc_tests$feature)[1:10]) ## Heatmap of top features data[rownames(data) %in% anova_results$result$feature[1:10]] %>% PomaHeatmap(covs = c("factors"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) ## Perform Three-Way ANOVA data %>% PomaUnivariate(method = "anova", covs = c("gender", "smoking_condition")) ## Perform ANCOVA with one numeric covariate and one factor covariate data %>% PomaUnivariate(method = "anova", covs = c("age_at_consent", "smoking_condition")) # Perform Kruskal-Wallis test data %>% PomaUnivariate(method = "kruskal", adjust = "holm", run_post_hoc = TRUE)# Two groups ## Output columns: feature, fold_change, diff_means, pvalue, adj_pvalue, mean_xxx (group 1) mean_yyy (group 2), sd_xxx (group 1), sd_yyy (group 2) data <- POMA::st000336 # Example SummarizedExperiment object included in POMA ## Perform T-test ttest_results <- st000336 %>% PomaImpute() %>% PomaUnivariate(method = "ttest", paired = FALSE, var_equal = FALSE, adjust = "fdr") ttest_results %>% dplyr::slice(1:10) ## Volcano plot ttest_results %>% dplyr::select(feature, fold_change, pvalue) %>% PomaVolcano(labels = TRUE) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "group", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = ttest_results$feature[1:10]) ## Heatmap of top features data[rownames(data) %in% ttest_results$feature[1:10]] %>% PomaHeatmap(covs = c("group"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) ## Perform Mann-Whitney U test mann_whitney_results <- st000336 %>% PomaImpute() %>% PomaUnivariate(method = "mann", paired = FALSE, var_equal = FALSE, adjust = "fdr") mann_whitney_results %>% dplyr::slice(1:10) ## Volcano plot mann_whitney_results %>% dplyr::select(feature, fold_change, pvalue) %>% PomaVolcano(labels = TRUE) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "group", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = mann_whitney_results$feature[1:10]) ## Heatmap of top features data[rownames(data) %in% mann_whitney_results$feature[1:10]] %>% PomaHeatmap(covs = c("group"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) # More than 2 groups ## Output is a list with objects `result` and `post_hoc_tests` data <- POMA::st000284 # Example SummarizedExperiment object included in POMA ## Perform Two-Way ANOVA anova_results <- data %>% PomaUnivariate(method = "anova", covs = c("gender"), error = NULL, adjust = "fdr", run_post_hoc = TRUE) anova_results$result %>% dplyr::slice(1:10) anova_results$post_hoc_tests %>% dplyr::slice(1:10) ## Boxplot of top features data %>% PomaBoxplots(x = "features", outcome = "factors", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = anova_results$result$feature[1:10]) ## Boxplot of top significant pairwise features (after posthoc test) data %>% PomaBoxplots(x = "features", outcome = "factors", # factorial variable to group by (e.g., treatment, sex, etc) feature_name = unique(anova_results$post_hoc_tests$feature)[1:10]) ## Heatmap of top features data[rownames(data) %in% anova_results$result$feature[1:10]] %>% PomaHeatmap(covs = c("factors"), # covariates to plot (e.g., treatment, sex, etc) feature_names = TRUE) ## Perform Three-Way ANOVA data %>% PomaUnivariate(method = "anova", covs = c("gender", "smoking_condition")) ## Perform ANCOVA with one numeric covariate and one factor covariate data %>% PomaUnivariate(method = "anova", covs = c("age_at_consent", "smoking_condition")) # Perform Kruskal-Wallis test data %>% PomaUnivariate(method = "kruskal", adjust = "holm", run_post_hoc = TRUE)
PomaVolcano creates a volcano plot from a given dataset. This function is designed to visualize the statistical significance (p-value) against the magnitude of change (log2 fold change) for features.
PomaVolcano( data, pval_cutoff = 0.05, log2fc_cutoff = NULL, labels = FALSE, x_label = "log2 (Fold Change)", y_label = "-log10 (P-value)" )PomaVolcano( data, pval_cutoff = 0.05, log2fc_cutoff = NULL, labels = FALSE, x_label = "log2 (Fold Change)", y_label = "-log10 (P-value)" )
data |
A data frame with at least three columns: feature names, effect size (e.g., logFC), and statistical significance values. These should be the first three columns of the data, in this order. |
pval_cutoff |
Numeric. Specifies the p-value threshold for significance in the volcano plot. The default is set to 0.05. This parameter determines the horizontal line in the plot indicating the threshold for statistical significance. |
log2fc_cutoff |
Numeric. Specifies the log2 fold change cutoff for the volcano plot. If not provided, the cutoff is set to the 75th percentile of the absolute log2 fold changes in the data. This parameter determines the vertical lines in the plot indicating the magnitude of change threshold. |
labels |
Logical. Indicates whether to plot labels for significant features. |
x_label |
Character. Custom label for the x-axis. |
y_label |
Character. Custom label for the y-axis. |
A ggplot object representing the volcano plot.
Pol Castellano-Escuder
data <- POMA::st000336 # Example SummarizedExperiment object included in POMA # T-test results <- data %>% PomaImpute() %>% PomaUnivariate() %>% dplyr::select(feature, fold_change, pvalue) results %>% PomaVolcano(pval_cutoff = 0.05, log2fc_cutoff = NULL, labels = FALSE, x_label = "log2 (Fold Change)", y_label = "-log10 (P-value)") # Limma results <- data %>% PomaImpute() %>% PomaNorm() %>% PomaLimma(contrast = "DMD-Controls") %>% dplyr::select(feature, log2FC, pvalue) results %>% PomaVolcano(pval_cutoff = 0.05, log2fc_cutoff = NULL, labels = FALSE, x_label = "log2 (Fold Change)", y_label = "-log10 (P-value)")data <- POMA::st000336 # Example SummarizedExperiment object included in POMA # T-test results <- data %>% PomaImpute() %>% PomaUnivariate() %>% dplyr::select(feature, fold_change, pvalue) results %>% PomaVolcano(pval_cutoff = 0.05, log2fc_cutoff = NULL, labels = FALSE, x_label = "log2 (Fold Change)", y_label = "-log10 (P-value)") # Limma results <- data %>% PomaImpute() %>% PomaNorm() %>% PomaLimma(contrast = "DMD-Controls") %>% dplyr::select(feature, log2FC, pvalue) results %>% PomaVolcano(pval_cutoff = 0.05, log2fc_cutoff = NULL, labels = FALSE, x_label = "log2 (Fold Change)", y_label = "-log10 (P-value)")
Compute quantile normalization.
quantile_norm(data)quantile_norm(data)
data |
A data matrix (samples in rows). |
viridis "plasma" paletteColor scale constructor for continuous viridis "plasma" palette
scale_color_poma_c()scale_color_poma_c()
viridis "plasma" paletteColor scale constructor for discrete viridis "plasma" palette
scale_color_poma_d()scale_color_poma_d()
viridis "plasma" paletteFill scale constructor for continuous viridis "plasma" palette
scale_fill_poma_c()scale_fill_poma_c()
viridis "plasma" paletteFill scale constructor for discrete viridis "plasma" palette
scale_fill_poma_d()scale_fill_poma_d()
Colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world. Despite an expanding knowledge of its molecular pathogenesis during the past two decades, robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC are still lacking. In this study, we present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying biomarker candidates that could enable highly sensitive and specific CRC detection using human serum samples. In this targeted approach, 158 metabolites from 25 metabolic pathways of potential significance were monitored in 234 serum samples from three groups of patients (66 CRC patients, 76 polyp patients, and 92 healthy controls). Partial least squares-discriminant analysis (PLS-DA) models were established, which proved to be powerful for distinguishing CRC patients from both healthy controls and polyp patients. Receiver operating characteristic curves generated based on these PLS-DA models showed high sensitivities (0.96 and 0.89, respectively, for differentiating CRC patients from healthy controls or polyp patients); good specificities (0.80 and 0.88), and excellent areas under the curve (0.93 and 0.95) were also obtained. Monte Carlo cross validation (MCCV) was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach.
st000284st000284
A SummarizedExperiment object: 224 samples, 113 metabolites, 4 covariables and 3 groups (CRC, Healthy and Polyp).
113 serum metabolites.
Age at consent, Gender, Smoking Condition and Alcohol Consumption.
Colorectal Cancer Detection Using Targeted Serum Metabolic Profiling, J. Proteome. Res., 2014, 13, 4120-4130.
Duchenne Muscular Dystrophy (DMD) is an X-linked recessive form of muscular dystrophy that affects males via a mutation in the gene for the muscle protein, dystrophin. Progression of the disease results in severe muscle loss, ultimately leading to paralysis and death. Steroid therapy has been a commonly employed method for reducing the severity of symptoms. This study aims to quantify the urine levels of amino acids and organic acids in patients with DMD both with and without steroid treatment. Track the progression of DMD in patients who have provided multiple urine samples.
st000336st000336
A SummarizedExperiment object: 57 samples, 31 metabolites, 1 covariable and 2 groups (Controls and DMD).
31 urine metabolites.
Steroid status.
Compute sum normalization. Final unit is a percentage.
sum_norm(data)sum_norm(data)
data |
A data matrix (samples in rows). |
A ggplot theme which allow custom yet consistent styling of plots in the POMA package and web app.
theme_poma( base_size = 15, axistitle = "xy", axistext = "xy", legend_position = "bottom", legend_title = TRUE, axis_x_rotate = FALSE, margin = 2 )theme_poma( base_size = 15, axistitle = "xy", axistext = "xy", legend_position = "bottom", legend_title = TRUE, axis_x_rotate = FALSE, margin = 2 )
base_size |
(integer) Base point size |
axistitle |
(string) Axis titles. Options include "none" or any combination of "X", "Y", "x" and "y". |
axistext |
(string) Axis text labels for values or groups. Options include "none" or any combination of "X", "Y", "x" and "y". |
legend_position |
Character. Legend position. See |
legend_title |
Logical. Include legend title. |
axis_x_rotate |
Logical. Rotate x-axis 45 degrees. |
margin |
(numeric) Should a margin of x be added to the plot? Defaults to 0 (no margin by default). |
## Not run: library(ggplot2) ggplot(diamonds, aes(cut)) + geom_bar() + theme_poma() ## End(Not run)## Not run: library(ggplot2) ggplot(diamonds, aes(cut)) + geom_bar() + theme_poma() ## End(Not run)