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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.

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)

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

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 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

• 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