| 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: | 1.15.0 |
| Built: | 2024-07-22 05:05:59 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)flattenCorrMatrix(cormat, pmat)
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
data("st000284") st000284 %>% PomaImpute(method = "knn") %>% PomaBatch(batch = "gender")data("st000284") st000284 %>% PomaImpute(method = "knn") %>% 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, feature_name = NULL, theme_params = list(legend_title = FALSE, axis_x_rotate = TRUE) )PomaBoxplots( data, x = "samples", violin = FALSE, 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. |
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("st000284") # Sample boxplots st000284 %>% PomaNorm() %>% PomaBoxplots(theme_params = list(axistext = "y")) # Sample violin plots st000284 %>% PomaNorm() %>% PomaBoxplots(violin = TRUE, theme_params = list(axistext = "y")) # All feature boxplots st000284 %>% PomaNorm() %>% PomaBoxplots(x = "features", theme_params = list(axis_x_rotate = TRUE)) # Specific feature boxplots st000284 %>% PomaNorm() %>% PomaBoxplots(x = "features", feature_name = c("ornithine", "orotate")) # Specific feature violin plots st000284 %>% PomaNorm() %>% PomaBoxplots(x = "features", violin = TRUE, feature_name = c("ornithine", "orotate"))data("st000284") # Sample boxplots st000284 %>% PomaNorm() %>% PomaBoxplots(theme_params = list(axistext = "y")) # Sample violin plots st000284 %>% PomaNorm() %>% PomaBoxplots(violin = TRUE, theme_params = list(axistext = "y")) # All feature boxplots st000284 %>% PomaNorm() %>% PomaBoxplots(x = "features", theme_params = list(axis_x_rotate = TRUE)) # Specific feature boxplots st000284 %>% PomaNorm() %>% PomaBoxplots(x = "features", feature_name = c("ornithine", "orotate")) # Specific feature violin plots st000284 %>% PomaNorm() %>% 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
data("st000284") PomaClust(st000284)data("st000284") PomaClust(st000284)
PomaCorr computes all pairwise correlations in the data.
PomaCorr(data, method = "pearson", label_size = 8, theme_params = list())PomaCorr(data, method = "pearson", label_size = 8, theme_params = list())
data |
A |
method |
Character. Indicates which correlation coefficient has to be computed. Options are "pearson" (default), "kendall" and "spearman". |
label_size |
Numeric. Indicates plot label size. |
theme_params |
List. Indicating |
A list with the results.
Pol Castellano-Escuder
Jerome Friedman, Trevor Hastie and Rob Tibshirani (2019). glasso: Graphical Lasso: Estimation of Gaussian Graphical Models. R package version 1.11. https://CRAN.R-project.org/package=glasso
data("st000284") # Pearson correlation PomaCorr(st000284)$correlations ## Gaussian graphical model # library(ggraph) # PomaCorr(st000284, corr_type = "glasso")data("st000284") # Pearson correlation PomaCorr(st000284)$correlations ## Gaussian graphical model # library(ggraph) # PomaCorr(st000284, corr_type = "glasso")
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 |
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 more 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(ID = 1:150, Group = iris$Species) # Create features: `p` column data frame with features features <- iris[, 1:4] # Create a `SummarizedExperiment` object with `POMA` object <- PomaCreateObject(metadata = metadata, features = features)data(iris) # Create metadata: Data frame with sample names and a group factor metadata <- data.frame(ID = 1:150, Group = iris$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", feature_name = NULL, theme_params = list(legend_title = FALSE) )PomaDensity( data, x = "samples", 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". |
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("st000284") # Sample density plots st000284 %>% PomaNorm() %>% PomaDensity(theme_params = list(axistext = "y")) # All feature density plots st000284 %>% PomaNorm() %>% PomaDensity(x = "features", theme_params = list(legend_position = "none")) # Specific feature density plots st000284 %>% PomaNorm() %>% PomaDensity(x = "features", feature_name = c("ornithine", "orotate"))data("st000284") # Sample density plots st000284 %>% PomaNorm() %>% PomaDensity(theme_params = list(axistext = "y")) # All feature density plots st000284 %>% PomaNorm() %>% PomaDensity(x = "features", theme_params = list(legend_position = "none")) # Specific feature density plots st000284 %>% PomaNorm() %>% 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, adjust = "fdr")PomaDESeq(data, adjust = "fdr")
data |
A |
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
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)
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("st000284") # Basic heatmap st000284 %>% PomaNorm() %>% PomaHeatmap() # Heatmap with one covariate st000284 %>% PomaNorm() %>% PomaHeatmap(covs = "factors") # Heatmap with two covariates st000284 %>% PomaNorm() %>% PomaHeatmap(covs = c("factors", "smoking_condition"))data("st000284") # Basic heatmap st000284 %>% PomaNorm() %>% PomaHeatmap() # Heatmap with one covariate st000284 %>% PomaNorm() %>% PomaHeatmap(covs = "factors") # Heatmap with two covariates st000284 %>% PomaNorm() %>% 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 = TRUE, method = "knn" )PomaImpute( data, zeros_as_na = FALSE, remove_na = TRUE, cutoff = 20, group_by = TRUE, 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 |
Logical. If |
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.
data("st000336") PomaImpute(st000336, method = "knn")data("st000336") PomaImpute(st000336, 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("st000336") # lasso st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaLasso() # elasticnet st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaLasso(alpha = 0.5) # ridge st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaLasso(alpha = 0)data("st000336") # lasso st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaLasso() # elasticnet st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaLasso(alpha = 0.5) # ridge st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaLasso(alpha = 0)
limma
PomaLimma uses the classical limma package to compute differential expression analysis.
PomaLimma(data, contrast = NULL, covs = NULL, adjust = "fdr", weights = FALSE)PomaLimma(data, contrast = NULL, covs = NULL, adjust = "fdr", weights = FALSE)
data |
A |
contrast |
Character. Indicates the comparison. For example, "Group1-Group2" or "control-intervention". |
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". |
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("st000284") st000284 %>% PomaNorm() %>% PomaLimma(contrast = "Healthy-CRC", adjust = "fdr")data("st000284") st000284 %>% PomaNorm() %>% PomaLimma(contrast = "Healthy-CRC", adjust = "fdr")
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("st000284") # Perform linear model with all features st000284 %>% PomaLM() # Perform linear model with two features st000284 %>% PomaLM(x = c("x1_methyladenosine", "x2_deoxyuridine"))data("st000284") # Perform linear model with all features st000284 %>% PomaLM() # Perform linear model with two features st000284 %>% 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("st000284") # Perform linear mixed model with all features st000284 %>% PomaLMM() # Perform linear mixed model with two features st000284 %>% PomaLMM(y = c("x1_methyladenosine", "x1_methylhistamine")) # Perform linear mixed model with one random effect st000284 %>% PomaLMM(x = "smoking_condition") # Perform linear mixed model with two random effects and two features st000284 %>% 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 st000284 %>% 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 st000284 %>% PomaLMM(x = "age_at_consent") # Numerical i.e., fixed effectdata("st000284") # Perform linear mixed model with all features st000284 %>% PomaLMM() # Perform linear mixed model with two features st000284 %>% PomaLMM(y = c("x1_methyladenosine", "x1_methylhistamine")) # Perform linear mixed model with one random effect st000284 %>% PomaLMM(x = "smoking_condition") # Perform linear mixed model with two random effects and two features st000284 %>% 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 st000284 %>% 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 st000284 %>% 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". |
method |
Character. The normalization method to use. Options include "none" (no normalization), "auto_scaling" (autoscaling normalization, i.e., Z-score normalization), "level_scaling" (level scaling normalization), "log_scaling" (log scaling normalization), "log_transform" (log transformation normalization), "vast_scaling" (vast scaling normalization), "log_pareto" (log Pareto scaling normalization), "min_max" (min-max normalization), 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.
data("st000284") PomaNorm(st000284, method = "log_pareto")data("st000284") PomaNorm(st000284, 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("st000336") st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaOddsRatio(feature_name = c("glutamic_acid", "glutamine", "glycine", "histidine"))data("st000336") st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaOddsRatio(feature_name = c("glutamic_acid", "glutamine", "glycine", "histidine"))
PomaOutliers analyses and removes statistical outliers from the data.
PomaOutliers( data, method = "euclidean", type = "median", coef = 2, labels = FALSE )PomaOutliers( data, method = "euclidean", type = "median", 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 |
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("st000336") # clean outliers st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaOutliers()data("st000336") # clean outliers st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaOutliers()
PomaPCA performs a principal components analysis on the given SummarizedExperiment object.
PomaPCA( data, center = TRUE, scale = TRUE, ncomp = 4, labels = FALSE, ellipse = FALSE, load_length = 1 )PomaPCA( data, center = TRUE, scale = TRUE, ncomp = 4, labels = FALSE, ellipse = FALSE, load_length = 1 )
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. 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("st000336") st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaPCA()data("st000336") st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaPCA()
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("st000284") # PCR with 2 components st000284 %>% PomaPCR(y = "age_at_consent") # PCR with 20 components st000284 %>% PomaPCR(ncomp = 20)data("st000284") # PCR with 2 components st000284 %>% PomaPCR(y = "age_at_consent") # PCR with 20 components st000284 %>% PomaPCR(ncomp = 20)
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("st000284") # PLS st000284 %>% PomaNorm() %>% PomaPLS(method = "pls") data("st000336") # PLSDA st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaPLS(method = "plsda") # PLSDA with Cross-Validation st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaPLS(method = "plsda", cross_validation = TRUE) # sPLSDA st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaPLS(method = "splsda") # sPLSDA with Cross-Validation st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaPLS(method = "splsda", ncomp = 3, cross_validation = TRUE)data("st000284") # PLS st000284 %>% PomaNorm() %>% PomaPLS(method = "pls") data("st000336") # PLSDA st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaPLS(method = "plsda") # PLSDA with Cross-Validation st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaPLS(method = "plsda", cross_validation = TRUE) # sPLSDA st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaPLS(method = "splsda") # sPLSDA with Cross-Validation st000336 %>% PomaImpute() %>% PomaNorm() %>% PomaPLS(method = "splsda", ncomp = 3, cross_validation = TRUE)
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("st000336") st000336 %>% PomaImpute() %>% PomaRandForest()data("st000336") st000336 %>% PomaImpute() %>% PomaRandForest()
PomaRankProd performs the Rank Product (or Rank Sum) method to identify differentially expressed genes.
PomaRankProd(data, logged = TRUE, paired = NA, cutoff = 0.05, method = "pfp")PomaRankProd(data, logged = TRUE, paired = NA, cutoff = 0.05, 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. |
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 results including plots and tables.
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("st000336") st000336 %>% PomaImpute() %>% PomaRankProd()data("st000336") st000336 %>% PomaImpute() %>% PomaRankProd()
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, 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, 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. |
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("st000284") st000284 %>% PomaNorm() %>% PomaUMAP()data("st000284") st000284 %>% PomaNorm() %>% PomaUMAP()
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 list with the results.
Pol Castellano-Escuder
data("st000336") # Perform T-test st000336 %>% PomaImpute() %>% PomaUnivariate(method = "ttest") # Perform Mann-Whitney U test st000336 %>% PomaImpute() %>% PomaUnivariate(method = "mann", adjust = "fdr") data("st000284") # Perform Two-Way ANOVA st000284 %>% PomaUnivariate(method = "anova", covs = c("gender")) # Perform Three-Way ANOVA st000284 %>% PomaUnivariate(method = "anova", covs = c("gender", "smoking_condition")) # Perform ANCOVA with one numeric covariate and one factor covariate # st000284 %>% # PomaUnivariate(method = "anova", covs = c("age_at_consent", "smoking_condition")) # Perform Kruskal-Wallis test st000284 %>% PomaUnivariate(method = "kruskal", adjust = "holm")data("st000336") # Perform T-test st000336 %>% PomaImpute() %>% PomaUnivariate(method = "ttest") # Perform Mann-Whitney U test st000336 %>% PomaImpute() %>% PomaUnivariate(method = "mann", adjust = "fdr") data("st000284") # Perform Two-Way ANOVA st000284 %>% PomaUnivariate(method = "anova", covs = c("gender")) # Perform Three-Way ANOVA st000284 %>% PomaUnivariate(method = "anova", covs = c("gender", "smoking_condition")) # Perform ANCOVA with one numeric covariate and one factor covariate # st000284 %>% # PomaUnivariate(method = "anova", covs = c("age_at_consent", "smoking_condition")) # Perform Kruskal-Wallis test st000284 %>% PomaUnivariate(method = "kruskal", adjust = "holm")
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, statistical significance values, and magnitude of change values. These should be the first three columns of the data, in this exact 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
st000336 %>% PomaImpute() %>% PomaUnivariate() %>% magrittr::extract2("result") %>% dplyr::select(feature, fold_change, pvalue) %>% PomaVolcano()st000336 %>% PomaImpute() %>% PomaUnivariate() %>% magrittr::extract2("result") %>% dplyr::select(feature, fold_change, pvalue) %>% PomaVolcano()
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)