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phenomis: An R package for post-processing and univariate analysis

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 and biosigner 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 package). See the phenomis vignette for a detailed example of the analysis of a metabolomics dataset.

Contributors

Natacha Lenuzza, Alyssa Imbert, Pierrick Roger and Etienne A. Thevenot.

Maintainer

Etienne A. Thévenot (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):

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

Installation

The package can be installed from github with devtools::install_github("odisce/phenomis").

Tutorial

See the phenomis vignette for a detailed example of the analysis of a metabolomics dataset.

Please cite