Welcome to POMAShiny! 
POMAShiny is an user-friendly web-based workflow for pre-processing and statistical analysis of mass spectrometry data. This tool allows you to analyze your data:
Fast: Analyze and visualize your data easily in few steps
Friendly: POMAShiny provides users a very intuitive structure and a whole interactive analysis
Free: All POMAShiny options are completely open and free for all users
Upload Data
- Upload your data in the “Upload Data” tab
- Data must be a CSV (comma-separated-value) file
Target File
A .CSV with two mandatory columns + n optional covariates:
- Each row denotes a sample (the same as in the features file)
- First/Left-hand column must be sample IDs => red
- Second/Left-hand column must be sample group/factor (e.g. treatment) => green
- Covariates (optional): From the third column (included) users can also include several experiment covariates => purple
Features File
A .CSV with m columns:
- Each row denotes a sample and each column denotes a feature
- First row must contain the feature names
More Help and Instructions
Additional help and more detailed instructions are provided in the “Help” panel.
About POMAShiny
POMAShiny has been developed by Pol Castellano-Escuder, Raúl González-Domínguez, Cristina Andrés-Lacueva and Alex Sánchez-Pla at University of Barcelona, Spain.
The source code of POMAShiny is freely available on GitHub at https://github.com/pcastellanoescuder/POMAShiny.
We would appreciate reports of any issues with the app via the GitHub issue tracking at https://github.com/pcastellanoescuder/POMAShiny/issues.
Upload data panel
Target File
Features File
Prepared Data
Missing value imputation
Prepared Data
Imputed Data
Normalization
Not Normalized Data
Normalized Data
Outlier detection
Outliers
Volcano plot parameters
Boxplot parameters
Density plot parameters
Heatmap parameters
Univariate analysis
ANOVA/ANCOVA
Multivariate analysis
PCA
PLS-DA
sPLS-DA
Cluster analysis
Clusters
Limma parameters
Limma
Correlation parameters
Correlations
Regularization parameters
Lasso
Ridge
Elasticnet
Random forest parameters
Random Forest
Rank product parameters
Rank Products
Odds Ratio parameters
Odds Ratio
Help
- Upload Data Panel
- Pre-processing Panel
- EDA Panel
- Statistical Analysis Panel
Last update: septiembre 28, 2020
Upload Data Panel
In this panel users can upload their data to be analyzed in POMAShiny. Data format must be a CSV (comma-separated-value) file.
Target File
A .CSV with two mandatory columns + n optional covariates:
- Each row denotes a sample (the same as in the features file)
- First/Left-hand column must be sample IDs => red
- Second/Left-hand column must be sample group/factor (e.g. treatment) => green
- Covariates (optional): From the third column (included) users can also include several experiment covariates => purple
Once this file has been uploaded, users can select desired rows in the “Target File” panel table to create a subset of the whole uploaded data. If this selection is done, only selected rows are analyzed in POMAShiny, if not (default) all uploaded data are analyzed.
Features File
A .CSV with m columns:
- Each row denotes a sample and each column denotes a feature
- First row must contain the feature names
Exploratory report
After uploading the data and clicking the “Submit” button, POMAShiny allows users to generate an exploratory data analysis PDF report automatically by clicking the green button with the label “Exploratory report” in the top of the central panel. See a PDF report example here.
Example data
POMAShiny includes two example datasets that are both freely available at https://www.metabolomicsworkbench.org. The first example dataset consists of a targeted metabolomics three-group study and the second example dataset consists of a targeted metabolomics two-group study. These two datasets allow users to explore all available functionalities in POMAShiny. Both dataset documentations are available at https://github.com/pcastellanoescuder/POMA.
NOTE: Once target and features files are uploaded and the desired rows are selected in the target file (if necessary), users must have to click the “Submit” button to continue with the analysis.
Equivalent functions in POMA: POMA::PomaMSnSetClass() (format
data) and POMA::PomaEDA() (automatic PDF report).
Pre-processing Panel
Impute Values
Usually, mass spectrometry faces with a high number of missing values, most of them due to low signal intensity of peaks. Missing value imputation process in POMAShiny is divided in three sequential steps:
-
Distinguish between zeros and missing values. In case of the data have values of these two types users can distinguish or not between them. This option may be useful in experiments combining endogenous and exogenous features, as in this case the exogenous ones could be a real zero (absence) and the endogenous ones are unlikely to be real zeros.
-
Remove all features of the data that have more of a specific percentage (defined by user) of missing values in ALL study groups. By default this percentage is 20%.
-
Imputation. POMAShiny offers six different methods to impute missing values:
- replace missing values by zero
- replace missing values by half of the minimum positive value in the original data (in each column)
- replace missing values by the median of the column (feature)
- replace missing values by the mean of the column (feature)
- replace missing values by the minimum value in the column (feature)
- replace missing values using KNN algorithm (default)
Equivalent function in POMA: POMA::PomaImpute().
Normalization
It’s known that some factors can introduce variability in MS data. Even if the data have been generated under identical experimental conditions, this introduced variability can have a critical influence on the final statistical results, making normalization a key step in the workflow.
POMAShiny offers six different methods to normalize data:
- Autoscaling
- Level scaling
- Log scaling
- Log transformation
- Vast scaling
- Log pareto scaling (default)
Users can evaluate the normalization effects in the interactive boxplots located in the “Normalized Data” tab.
Equivalent functions in POMA: POMA::PomaNorm() (normalization) and
POMA::PomaBoxplots(group = "samples") (boxplots).
Outlier Detection
POMAShiny allows the analysis of outliers by different plots and tables as well as the possibility to remove statistical outliers from the analysis (default) using different modulable parameters.
The method implemented in POMAShiny is based on the euclidean distances (default but modulable) among observations and their distances to each group centroid in a two-dimensional space. Once this is computed, the classical univariate outlier detection formula _Q3 + 1.5*IQR_ (coefficient is modulable by the user) is used to detect multivariate group-dependant outliers using computed distance to each group centroid.
Select the method (distance), type and coefficient (the higher this value, the less sensitive the method is to outliers) to adapt the outlier detection method to your data. By switching the button “Show labels” all plots display automatically the sample IDs in the outlier detection plots.
- Distances Polygon Plot: Group centroids and sample coordinates in a two-dimensionality space
- Distances Boxplot: Boxplots of all computed distances to group centroid by group
NOTE: If the “Remove outliers” button is turned on (default), all detected outliers are excluded from the analysis automatically.
Equivalent functions in POMA: POMA::PomaOutliers(do = "analyze")
(analyze outliers) and POMA::PomaOutliers(do = "clean") (remove
outliers).
EDA Panel
POMAShiny offers several interactive and highly modulable plots designed to facilitate the exploratory data analysis (EDA) process, giving a wide range of visualization options.
Volcano Plot
In this tab, users can explore their data in an interactive volcano plot. This plot is based on the results of a standard T-test which users can define if data are paired or not and if the study group variances are equal or not. This option is only available for two-group studies.
POMAShiny interactive volcano plot gives users information about T-test significance and fold changes. log2 fold changes between groups are represented in the horizontal axis while -log10 T-test p-values are represented in the vertical axis.
Users can select if raw p-values or adjusted (FDR) p-values are displayed. Other parameters as p-value threshold, log2 fold change threshold or x-axis range are available in the parameters menu.
Equivalent function in POMA: POMA::PomaVolcano().
Boxplot
This tab provides a highly interactive boxplot that shows all data features by their different study groups. Each feature is represented by as many boxplots as there are groups in the study. Different visualization parameters are available in this tab:
- Features to plot: By default this box is empty because all features are plotted. However, by selecting some specific features in this box, only these features are plotted
- Show points: By turning on this button, points corresponding to each sample are shown in each feature boxplots. If your data contain many features, this option can slow down the interactive display
- Split boxes: By default study group boxplots are overlapped in each feature. By turning on this button, study group boxplots are splitted in each feature. Only recommended if features selected to show are not too many (maximum 10 features)
Equivalent functions in POMA:
POMA::PomaBoxplots(group = "features") (all features) and
POMA::PomaBoxplots(group = "features", feature_name = c("XXX", "YYY", "ZZZ"))
(only features XXX, YYY and ZZZ).
Density Plot
POMAShiny provides an interactive density plot to explore all study group distributions (default). However, by turning off the “Plot groups” button, POMAShiny plots the feature distributions indicated by user instead study group distributions.
Equivalent functions in POMA: POMA::PomaDensity() (study groups)
and
POMA::PomaDensity(group = "features", feature_name = c("XXX", "YYY", "ZZZ"))
(only features XXX, YYY and ZZZ).
Heatmap
In this panel POMAShiny offers a classical heatmap as well as a hierarchical clustering with a color stripe that corresponds to each sample group label. Users can display or not sample IDs (not recommended if n is too large) and feature names (not recommended for too many features).
Equivalent function in POMA: POMA::PomaHeatmap().
Statistical Analysis Panel
Univariate Analysis
Univariate analysis is the simplest form of data analysis where the data being analyzed contains only one variable. Since it’s a single variable it doesn’t deal with causes or relationships.
T-test
T-test is a parametric statistical hypothesis test in which the test statistic follows a Student’s t-distribution under the null hypothesis. This analysis is used when you are comparing two groups. This test assumes the normal distribution of features. T-test results can be visualized in the volcano plot provided at EDA panel.
-
Equal Variance (or pooled) T-test: The equal variance T-test is used when the variance of the two tested groups is similar.
-
Unequal Variance T-test: The unequal variance T-test is used when the variance of the two tested groups is different (default). This test is also called Welch’s T-test.
-
Paired T Test: The paired T-test is performed when samples consist of matched pairs of similar units or when there are cases of repeated measures. This method can also applies on cases where the samples are related in some manner or have matching characteristics (default is that groups are not paired).
Equivalent function in POMA: POMA::Univariate(method = "ttest").
ANOVA
The analysis of variance (ANOVA) tests the hypothesis that the averages of two or more groups are the same. The ANOVA evaluates the importance of one or more factors when comparing the means of the response variable in the different levels of the factors. The null hypothesis states that all the means of the groups are the same while the alternative hypothesis states that at least one is different. ANOVA is a parametric method that assumes the normal distribution of features.
If one or more covariates have been included in the target file, an analysis of covariance (ANCOVA) is performed automatically and the results are available at the “ANCOVA Results” tab. The ANCOVA is a general linear model which mix ANOVA and regression. ANCOVA evaluates whether the means of the groups are equal while statistically controlling the effects of other continuous variables that are not of primary interest (as group or treatment), known as covariates.
Equivalent functions in POMA: POMA::Univariate(method = "anova")
(ANOVA) and POMA::Univariate(method = "anova", covariates = TRUE)
(ANCOVA).
Mann-Whitney U Test
Mann-Whitney U test is the non-parametric alternative test to the independent sample T-test. It’s a non-parametric test that is used to compare two group means that come from the same population, and used to test whether two sample means are equal or not. Usually, the Mann-Whitney U test is used when the assumptions of the T-test are not met. When the study groups are paired, this test becomes a Wilcoxon signed-rank test.
Equivalent function in POMA: POMA::Univariate(method = "mann").
Kruskal Wallis Test
Kruskal-Wallis test is a non-parametric alternative to ANOVA. It is an extension of the Mann-Whitney U test for 3 or more groups. Kruskal-Wallis test does not assume normality in the data, as opposed to the traditional ANOVA.
Equivalent function in POMA: POMA::Univariate(method = "kruskal").
Multivariate Analysis
Unlike univariate methods, multivariate methods are focused in the study of more than one feature at a time. These type of approaches have been widely used because their informativeness. Since being more complex than conventional univariate statistics, these methods can provide information about the structure of the data and different internal relationships that would not be observed with univariate statistics. However, the interpretation of these type of analysis can be more complex.
PCA (principal component analysis)
PCA is one of the most used methods for data dimension reduction. POMAShiny allows users to compute a PCA controlling different parameters:
- Number of components: This number indicates the number of components that are calculated
- Scale and Center: By default these parameters are disabled. If the data have been normalized
- Show ellipses: By turning on this button, the ellipses computed assuming a multivariate normal distribution are drawn in a score plot and biplot
Equivalent function in POMA:
POMA::PomaMultivariate(method = "pca").
PLS-DA (partial least squares discriminant analysis)
PLS-DA is a supervised method that uses the multiple linear regression method to find the direction of maximum covariance between the data and the sample group. POMAShiny allows users to compute a PLS-DA controlling different parameters:
- Number of components: This number indicates the number of components that are calculated
- VIP cutoff: This value indicates the variable importance in the projection (VIP) cutoff. Features shown in the VIP plot tab are based on this value. Only features with a VIP higher than this value are shown in the plot. This is a reactive option, it means that users doesn’t have to recalculate a PLS-DA to change this value, it can be changed and the VIP plot are updated automatically without doing anything more
- Show ellipses: By turning on this button (default), the ellipses computed assuming a multivariate normal distribution are drawn in a score plot
- Validation type: Internal validation to use, options are “Mfold” (default) or “Leave One Out”
- Number of folds: Number of folds for Mfold validation method (default is 5). If the validation method is loo, this value will become to 1
- Number of iterations for validation process: Number of iterations for the validation method selected
Equivalent function in POMA:
POMA::PomaMultivariate(method = "plsda").
sPLS-DA (sparse partial least squares discriminant analysis)
Often, sPLS-DA method is used to classify samples (supervised analysis) and to select features. POMAShiny allows users to compute a sPLS-DA controlling different parameters:
- Number of components: This number indicates the number of components that are calculated
- Number of features: The number of features to keep in the model
- Show ellipses: By turning on this button (default), the ellipses computed assuming a multivariate normal distribution are drawn in a score plot
- Validation type: Internal validation to use, options are “Mfold” (default) or “Leave One Out”
- Number of folds: Number of folds for Mfold validation method (default is 5). If the validation method is loo, this value will become to 1
- Number of iterations for validation process: Number of iterations for the validation method selected
Equivalent function in POMA:
POMA::PomaMultivariate(method = "splsda").
Cluster Analysis
Cluster analysis is also a multivariate method like the previous ones. However, it’s in a separate section only to make the app and the analysis more understandable. POMAShiny allows users to compute easily a cluster analysis using the k-means algorithm.
k-means
k-means is an unsupervised method that aims to assign each sample of the study to one of k clusters based on the sample means and the nearest cluster centroid. POMAShiny calclulates the optimum number of clusters (k) using the elbow method. Different parameters are available for the calculation and visualization of clusters.
- Method: Distance used to calculate multi dimensional scaling (MDS)
- Number of clusters among which the optimal one will be selected: The total number of clusters to compute to select the optimal one. In POMAShiny, this value is selected using the “elbow” method
- Number of clusters (k): Number of clusters that are calculated. By default this value is the optimal number of clusters computed by POMAShiny. However, by changing this value, the k selected by user is used
- Show clusters: k different clusters are projected in the MDS plot. By turning off this button a simple MDS plot are displayed
- Show labels: By turning on this button sample IDs are shown in the
plot. If this option is enabled, an additional button is displayed:
- Show group: By turning on this option, sample IDs are replaced by group labels
Equivalent function in POMA: POMA::PomaClust().
MDS (multi dimensional scaling)
Since k-means is a multivariate method, POMAShiny uses the two first dimensions of a classical MDS to project the computed clusters. By turning off the “Show clusters” button, users visualize a plain MDS plot. Labeling options are the same for both scenarios.
Both two first dimensions of MDS and calculated k clusters are available in the “Cluster Table” tab.
Equivalent function in POMA:
POMA::PomaClust(show_clusters = FALSE).
Limma
Limma (Linear Models for Microarray Data) was created for the statistical analysis of gene expression experiments as microarrays. However, over the last years this method has been user in many other omics such as metabolomics or proteomics.
Limma performs a single-sample T-test using an empirical Bayes method to borrow information between all features. This method “leverages the highly parallel nature of features to borrow strength between the feature-wise models, allowing for different levels of variability between features and between samples, and making statistical conclusions more reliable when the number of samples is small”. See Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research, 43(7), e47-e47..
POMAShiny allows users to compute limma models and the possibility to adjust these models by different covariates (if they have been included in the target file). The POMAShiny limma results are displayed in an interactive volcano plot at Limma’s “Volcano Plot” tab.
Equivalent functions in POMA: POMA::PomaLimma() (without
covariates) and POMA::PomaLimma(covariates = TRUE) (with covariates).
Correlation Analysis
POMAShiny provides different options to conduct an accurate and interactive correlation analysis. In the “Correlation parameters” menu users can select the correlation method (pearson, spearman or kendall) to be used for all available visualizations explained below.
Pairwise Correlation Scatterplot
POMAShiny provides a highly modulable and interactive scatterplot of pairwise correlation between features. Here, users can select two desired features and explore them in a very comfortable way, being able to remove some points of the plot by clicking over, drawing a smoth line based on a linear model, showing the sample IDs instead of points or exploring and comparing pairwise correlations within each study group (factor).
Pairwise Correlation Table
A table with all pairwise correlations in the data. This table can be sorted by all different columns and it can be downloaded in different formats.
Equivalent function in POMA: POMA::PomaCorr()
Correlogram
A global correlation plot or correlogram is provided in this tab. This plot allows users to visualize all correlations in the data at once. Users can control the label size by clicking in the upper left corner menu.
Equivalent function in POMA: POMA::PomaCorr()
Correlation Network
POMAShiny offers a correlation network visualization, where correlations between features can be observed in a very clear way. Only pairs of features with a correlation absolute value over “Correlation Cutoff” indicated in the upper left corner menu are shown.
Equivalent function in POMA: POMA::PomaCorr()
Gaussian Graphical Models
POMAShiny provides an alternative method for correlation network
visualization. Estimation of gaussian graphical models through glasso
R package is provided in this tab. Users can define the regularizarion
parameter to estimate an sparse inverse correlation matrix using lasso
in the upper left corner menu.
Equivalent function in POMA: PomaCorr(corr_type = "glasso")
Regularization
POMAShiny offers three different wide used regularization methods both for feature selection and prediction model creation purposes. These methods are lasso, ridge regression and elasticnet. All of these methods are based on penalized logistic regression and are therefore only available for two-group studies. If the purpose is not predictive, users can set the test parameter to zero. Otherwise, if the purpose is to build a predictive model, users can select the proportion (%) of samples that are used as a test set.
- Test partition (%): Percentage of observations that are used as test set. These samples are used only to perform an external validation. If this value is set to zero, all samples are used to create the model and no external validation is computed
- Internal CV folds: Number of folds for CV (default is 10). This value can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable 3
- Elasticnet Mixing Parameter (only for elasticnet): This value corresponds to the alpha (or penalty) parameter
Equivalent functions in POMA: POMA::PomaLasso(alpha = 1) (lasso),
POMA::PomaLasso(alpha = 0) (ridge) and POMA::PomaLasso(alpha = 0.5)
(elasticnet; alpha range from >0 to <1).
Random Forest
Random forest is a widely used machine learning algorithm in different omics for the purpose of creating prediction models. POMAShiny provides a classification random forest algorithm designed to create prediction models to discriminate between two or more study groups. Different parameters are available in this panel:
- Test partition (%): Percentage of observations that are used as test set. Default is 20% of observations. These samples are used only to perform an external validation
- Number of trees: Number of trees to grow
- Number of variables randomly sampled as candidates at each split
- Node Size: Minimum size of terminal nodes
- Number of Selected Features: Number of features that are shown in the Gini plot
Equivalent function in POMA: POMA::PomaRandForest()
Rank Products
The rank product is a biologically motivated test for the detection of differential expressed/concentrated features in high throughput experiments. It’s a non-parametric statistical method based on the ranks of fold changes. Over the last years this methodology has become popular in many omics fields such as transcriptomics, metabolomics and proteomics.
Rank product test in POMAShiny skips the normalization and outlier detection steps to avoid possible negative values generated in the normalization process. Consequently, this method is based on the imputed data and all samples will be used to perform the analysis. If the user wants to remove some detected outliers from this test, it’s possible to select all samples except the detected outliers in the Upload Data panel -> Target File tab table and repeat the imputation step.
In the Rank Products panel, users can select if their data is paired or not and the possibility to apply a log2 transformarion over each feature (default). Also the method to perform the test (percentage of false prediction or p-value) and the cutoff can be modulated by users. For more details see Del Carratore, F., Jankevics, A., Eisinga, R., Heskes, T., Hong, F., Breitling, and R. (2017) RankProd 2.0: a refactored Bioconductor package for detecting differentially expressed features in molecular profiling datasets. Bioinformatics, 33, 2774–2775.
Equivalent function in POMA: POMA::PomaRankProd().
Odds Ratio
In the Odds Ratio panel users can calculate the odds ratios (OR) ans its confidence intervals based on a logistic regression model. Consequently, this panel allows only two group datasets. By default, all OR are computed. However, users can select specific features in “Select model features” box.
To include confidence intervals in the plot switch on the “Show CI” button and to include ALL covariates uploaded in the target file in the logistic regression model and in the plot, switch on the “Include covariates” button.
Equivalent function in POMA: POMA::PomaOddsRatio().
POMA R/Bioconductor Package
POMAShiny is whole based on POMA R/Bioconductor package. POMA introduces a structured, reproducible and easy-to-use workflow for the visualization, pre-processing, exploratory and statistical analysis of mass spectrometry data. The main aim of POMA is to enable a flexible data cleaning and statistical analysis processes in one comprehensible and user-friendly R package. This package uses the standardized MSnbase data structures, to achieve the maximum flexibility and reproducibility and makes POMA compatible with pre-existing Bioconductor packages. To get an overview of the package, see the POMA workflow vignette at https://pcastellanoescuder.github.io/POMA/articles/POMA-demo.html.
- POMA website: https://pcastellanoescuder.github.io/POMA/
- POMA source code: https://github.com/pcastellanoescuder/POMA
- POMA Bioconductor page: https://bioconductor.org/packages/devel/bioc/html/POMA.html
Citation
Castellano-Escuder P, González-Domínguez R, Andrés-Lacueva C, Sánchez-Pla A (2020). POMA: User-friendly Workflow for Pre-processing and Statistical Analysis of Mass Spectrometry Data. R package version 0.99.40, https://github.com/pcastellanoescuder/POMA.
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- Additional Terms.
“Additional permissions” are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions.
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All other non-permissive additional terms are considered “further restrictions” within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying.
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Additional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way.
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Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10.
- Acceptance Not Required for Having Copies.
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- Patents.
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In the following three paragraphs, a “patent license” is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To “grant” such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party.
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Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.
- No Surrender of Others’ Freedom.
If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.
- Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.
- Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.
Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.
If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy’s public statement of acceptance of a version permanently authorizes you to choose that version for the Program.
Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.
- Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
- Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
- Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands show w' and show c' should show the appropriate
parts of the General Public License. Of course, your program’s commands
might be different; for a GUI interface, you would use an “about box”.
You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see https://www.gnu.org/licenses/.
The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read https://www.gnu.org/licenses/why-not-lgpl.html.
Code of Conduct
Please note that the POMAShiny is an open source project released with the Contributor Code of Conduct attached below. By contributing to this project, you agree to abide by its terms.
Contributor Covenant Code of Conduct
Our Pledge
We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.
Our Standards
Examples of behavior that contributes to a positive environment for our community include:
- Demonstrating empathy and kindness toward other people
- Being respectful of differing opinions, viewpoints, and experiences
- Giving and gracefully accepting constructive feedback
- Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience
- Focusing on what is best not just for us as individuals, but for the overall community
Examples of unacceptable behavior include:
- The use of sexualized language or imagery, and sexual attention or advances of any kind
- Trolling, insulting or derogatory comments, and personal or political attacks
- Public or private harassment
- Publishing others’ private information, such as a physical or email address, without their explicit permission
- Other conduct which could reasonably be considered inappropriate in a professional setting
Enforcement Responsibilities
Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful.
Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate.
Scope
This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.
Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at [INSERT CONTACT METHOD]. All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the reporter of any incident.
Enforcement Guidelines
Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:
1. Correction
Community Impact: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community.
Consequence: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.
2. Warning
Community Impact: A violation through a single incident or series of actions.
Consequence: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.
3. Temporary Ban
Community Impact: A serious violation of community standards, including sustained inappropriate behavior.
Consequence: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.
4. Permanent Ban
Community Impact: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.
Consequence: A permanent ban from any sort of public interaction within the community.
Attribution
This Code of Conduct is adapted from the Contributor Covenant, version 2.0, available at https://www.contributor-covenant.org/version/2/0/ code_of_conduct.html.
Community Impact Guidelines were inspired by Mozilla’s code of conduct enforcement ladder.
For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https:// www.contributor-covenant.org/translations.
Contact
Report a bug:
For any issue related to the web app, we encourage users to use the GitHub issue chanel. You can access it at the top of this page or at the link: https://github.com/pcastellanoescuder/POMAShiny/issues
Other questions:
For any other questions, please contact:
- Pol Castellano Escuder (Author, Maintainer): polcaes@gmail.com
- Raúl González Domínguez (Author): raul.gonzalez@ub.edu
- Cristina Andrés Lacueva (Author): candres@ub.edu
- Alex Sánchez Pla (Author): asanchez@ub.edu