--- editor_options: markdown: wrap: 72 --- # **phenomis**: An R package for post-processing and univariate analysis [](https://travis-ci.org/odisce/phenomis) ## Description This package provides methods to perform the statistical analysis of phenomics datasets (e.g. in proteomics and metabolomics). These methods include the reading of datasets (as 3 table *dataMatrix*, *sampleMetadata* and *variableMetadata* .tsv files) into a **`SummarizedExperiment`** or **`MultiAssayExperiment`** object (**`reading`**), quality control (**`inspecting`**) and transformation (**`transforming`**) of the dataMatrix, reduction of chemical redundancy (**`reducing`**), and univariate hypothesis testing (**`hypotesting`**). Multivariate analysis and feature selection can be further performed with the [**ropls**](http://bioconductor.org/packages/release/bioc/html/ropls.html) and [**biosigner**](http://bioconductor.org/packages/release/bioc/html/biosigner.html) packages, respectively. Finally, features can be annotated based on their m/z (and rt) values against public or local databases (**`annotating`**; based on the [**biodb**](https://github.com/pkrog/biodb) package). See the [**phenomis vignette**](vignettes/phenomis.Rmd) for a detailed example of the analysis of a metabolomics dataset. *Note that the `SummarizedExperiment` and `MultiAssayExperiment` formats are favored over the `ExpressionSet` and `MultiDataSet` formats, although the latter ones are still maintained.* ## Contributors Natacha Lenuzza, Alyssa Imbert, Pierrick Roger and Etienne A. Thevenot. ## Maintainer Etienne A. Thévenot ([etienne.thevenot\@cea.fr](mailto:etienne.thevenot@cea.fr)) Data Sciences team (Odiscé) Medicines and Healthcare Technologies Department CEA, INRAE, Université Paris Saclay, MetaboHUB 91191 Gif-sur-Yvette Cedex, France Web: <https://odisce.github.io/> ## Methods  ## Formats ### 3 tabular file format used for import/export Input (i.e. preprocessed) data consists of a 'samples times variables' matrix of intensities (**datMatrix** numeric matrix), in addition to sample and variable metadata (**sampleMetadata** and **variableMetadata** data frames). Theses 3 tables can be conveniently imported to/exported from R as tabular files:  ### `SummarizedExperiment` The following methods are available: | Methods | Description | Returned class | |--------------------------|----------------------------|-------------------| | **Constructors** | | | | **`SummarizedExperiment`** | Create a SummarizedExperiment object | `SummarizedExperiment` | | **`makeSummarizedExperimentFromExpressionSet`** | | `SummarizedExperiment` | | **Accessors** | | | | **`assayNames`** | Get or set the names of assay() elements | `character` | | **`assay`** | Get or set the ith (default first) assay element | `matrix` | | **`assays`** | Get the list of experimental data numeric matrices | `SimpleList` | | **`rowData`** | Get or set the row data (features) | `DataFrame` | | **`colData`** | Get or set the column data (samples) | `DataFrame` | | **`metadata`** | Get or set the experiment data | `list` | | **`dim`** | Get the dimensions (features of interest x samples) | `integer` | | **`dimnames`** | Get or set the dimension names | `list` of length 2 character/NULL | | **`rownames`** | Get the feature names | `character` | | **`colnames`** | Get the sample names | `character` | | **`as(, "SummarizedExperiment")`** | Convert an ExpressionSet to a SummarizedExperiment | `SummarizedExperiment` | ### `MultiAssayExperiment` The following methods are available ([Ramos et al., 2016](https://doi.org/10.1158/0008-5472.CAN-17-0344)): | Methods | Description | Returned class | |-----------------|--------------------------------------|-----------------| | **Constructors** | | | | **`MultiAssayExperiment`** | Create a MultiAssayExperiment object | `MultiAssayExperiment` | | **`ExperimentList`** | Create an ExperimentList from a List or list | `ExperimentList` | | **Accessors** | | | | **`colData`** | Get or set data that describe the patients/biological units | `DataFrame` | | **`experiments`** | Get or set the list of experimental data objects as original classes | `experimentList` | | **`assays`** | Get the list of experimental data numeric matrices | `SimpleList` | | **`assay`** | Get the first experimental data numeric matrix | `matrix`, matrix-like | | **`sampleMap`** | Get or set the map relating observations to subjects | `DataFrame` | | **`metadata`** | Get or set additional data descriptions | `list` | | **`rownames`** | Get row names for all experiments | `CharacterList` | | **`colnames`** | Get column names for all experiments | `CharacterList` | | **Subsetting** | | | | **`mae[i, j, k]`** | Get rows, columns, and/or experiments | `MultiAssayExperiment` | | **`mae[i, ,]`** | i: GRanges, character, integer, logical, List, list | `MultiAssayExperiment` | | **`mae[,j,]`** | j: character, integer, logical, List, list | `MultiAssayExperiment` | | **`mae[,,k]`** | k: character, integer, logical | `MultiAssayExperiment` | | **`mae[[i]]`** | Get or set object of arbitrary class from experiments | (Varies) | | **`mae[[i]]`** | Character, integer, logical | | | **`mae$column`** | Get or set colData column | `vector` (varies) | | **Management** | | | | **`complete.cases`** | Identify subjects with complete data in all experiments | `vector` (logical) | | **`duplicated`** | Identify subjects with replicate observations per experiment | `list` of `LogicalLists` | | **`mergeReplicates`** | Merge replicate observations within each experiment | `MultiAssayExperiment` | | **`intersectRows`** | Return features that are present for all experiments | `MultiAssayExperiment` | | **`intersectColumns`** | Return subjects with data available for all experiments | `MultiAssayExperiment` | | **`prepMultiAssay`** | Troubleshoot common problems when constructing main class | `list` | | **Reshaping** | | | | **`longFormat`** | Return a long and tidy DataFrame with optional colData columns | `DataFrame` | | **`wideFormat`** | Create a wide DataFrame, one row per subject | `DataFrame` | | **Combining** | | | | **`c`** | Concatenate an experiment | `MultiAssayExperiment` | | **Viewing** | | | | **`upsetSamples`** | Generalized Venn Diagram analog for sample membership | `upset` | ### **ExpressionSet** and **MultiDataSet** classes A legacy representation of ID-based datasets, supported for convenience and supplanted by `SummarizedExperiment` and `MultiAssayExperiment`. | Biobase methods | Description | |--------------------|----------------------------------------------------| | **exprs(eset)** | 'variable times samples' numeric matrix - dataMatrix | | **pData(eset)** | sample metadata data frame - sampleMetadata | | **fData(eset)** | variable metadata data frame - variableMetadata | | **sampleNames(eset)** | sample names | | **featureNames(eset)** | variable names | | **dims(eset)** | 2-element numeric vector of 'Features' and 'Samples' dimensions | | **varLabels(eset)** | Column names of the sampleMetadata, pData(eset) | | **fvarLabels(eset)** | Column names of the variableMetadata, fData(eset) | ### **MultiDataSet** class for multiple datasets | MultiDataSet methods | Description | Returned class | |------------------|-------------------------------------|------------------| | **as.list(mae)** | Get the list of data matrices | List | | **mds2mae** | Convert a MultiDataSet to a MultiAssayExperiment | MultiAssayExperiment | ## Installation The package can be installed from github with `devtools::install_github("odisce/phenomis")`. ## Tutorial See the [**phenomis vignette**](vignettes/phenomis.Rmd) for a detailed example of the analysis of a metabolomics dataset. ## Please cite - Imbert, A., Rompais, M., Selloum, M., Castelli, F., Mouton-Barbosa, E., Brandolini-Bunlon, M., Chu-Van, E., Joly, C., Hirschler, A., Roger, P., Burger, T., Leblanc, S., Sorg, T., Ouzia, S., Vandenbrouck, Y., Médigue, C., Junot, C., Ferro, M., Pujos-Guillot, E., de Peredo, A. G., Fenaille, F., Carapito, C., Herault, Y., & Thévenot, E. A. (2021). ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis. *Scientific Data*, 8(1). [doi:10.1038/s41597-021-01095-3](https://doi.org/10.1038/s41597-021-01095-3) - Thévenot, E.A., Roux, A., Xu, Y., Ezan, E., and Junot, C. (2015). Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. *Journal of Proteome Research* 14, 3322--3335. [doi:10.1021/acs.jproteome.5b00354](https://doi.org/10.1021/acs.jproteome.5b00354)