R/allGenerics.R
In SciDoPhenIA/phenomis: Analysis of single and multiple phenomic data sets
#### annotating ####
#' MS annotation
#'
#' Annotation with chemical and biological databases
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param database.c character: database to be used for annotation
#' @param param.ls list: parameters for database query; see the example below for
#' the name and related database of each of them
#' @param report.c Character: File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including the appended rowData
#' data frame(s)
#' @rdname annotating
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' # see the (default) parameters (e.g. for ChEBI query)
#' annotating_parameters("chebi")
#' # mz annotation with ChEBI
#' sacurine.se <- annotating(sacurine.se, database.c = "chebi", param.ls = list(query.type = "mz", query.col = "mass_to_charge", ms.mode = "neg", prefix = "chebiMZ."))
#' # mz annotation with local database
#' msdbDF <- read.table(system.file("extdata/local_ms_db.tsv", package = "phenomis"),
#' header = TRUE, sep = "\t", stringsAsFactors = FALSE)
#' sacurine.se <- annotating(sacurine.se, database.c = "local.ms",
#' param.ls = list(query.type = "mz", query.col = "mass_to_charge", ms.mode = "neg",
#' mz.tol = 5, mz.tol.unit = "ppm", local.ms.db = msdbDF, prefix = "localMS."))
#' rowData(sacurine.se)[!is.na(rowData(sacurine.se)[, "localMS.accession"]), ]
#' # annotation from ChEBI identifiers
#' sacurine.se <- annotating(sacurine.se, database.c = "chebi",
#' param.ls = list(query.type = "chebi.id", query.col = "database_identifier",
#' prefix = "chebiID."))
#' head(rowData(sacurine.se))
setGeneric("annotating",
function(x,
database.c = c("chebi", "local.ms")[1],
param.ls = list(query.type = c("mz", "chebi.id", "kegg.id")[1],
query.col = "mz",
ms.mode = "pos",
mz.tol = 10,
mz.tol.unit = "ppm",
fields = c("chebi.id", "name", "formula", "molecular.mass", "monoisotopic.mass"),
fieldsLimit = 1,
max.results = 3,
local.ms.db = data.frame(),
organism = "hsa",
prefix = paste0(database.c, "."),
sep = "|"),
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("annotating"))
#### clustering ####
#' clustering
#'
#' Hierarchical clustering of both samples and variables
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param dissym.c character: [hclust]
#' @param correl.c character: correlation coefficient (in case
#' '1-cor' or '1-abs(cor)' are selected as dissymilarity)
#' @param agglo.c character: agglomeration method
#' @param clusters.vi tupple of integers: number of sample and variable clusters, respectively
#' @param cex.vn tupple of numerics [Plot parameter]; size of the sample and variable labels
#' @param palette.c character [Plot parameter]: color palette
#' @param scale_plot.l logical [Plot parameter]: scaling (mean-centering and unit
#' variance scaling) to enhance contrast (for plotting only)
#' @param title.c character [Plot parameter]: Graphic the subtitle
#' @param figure.c character: File name with '.pdf' extension for the figure;
#' if 'interactive' (default), figures will be displayed interactively; if 'none',
#' no figure will be generated
#' @param report.c character: File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including columns indicating
#' the clusters in rowData and colData if 'clusters.vi' has been specified
#' @rdname clustering
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' sacurine.se <- correcting(sacurine.se)
#' sacurine.se <- sacurine.se[, pData(sacurine.se)[, "sampleType"] != "pool"]
#' sacurine.se <- transforming(sacurine.se)
#' sacurine.se <- sacurine.se[, sampleNames(sacurine.se) != "HU_neg_096_b2"]
#' sacurine.se <- clustering(sacurine.se)
#' utils::head(fData(sacurine.se))
#' # MultiDataSet
#' prometis.mset <- reading(system.file("extdata/prometis/", package="phenomis"))
#' prometis.mset <- clustering(prometis.mset, clusters.vi = c(3, 3))
setGeneric("clustering",
function(x,
dissym.c = c("euclidean",
"maximum",
"manhattan",
"canberra",
"binary",
"minkowski",
"1-cor",
"1-abs(cor)")[7],
correl.c = c("pearson",
"kendall",
"spearman")[1],
agglo.c = c("ward.D",
"ward.D2",
"single",
"complete",
"average",
"mcquitty",
"median",
"centroid")[2],
clusters.vi = c(2, 2),
cex.vn = c(1, 1),
palette.c = c("blueOrangeRed",
"redBlackGreen")[1],
scale_plot.l = TRUE,
title.c = NA,
figure.c = c("none", "interactive", "myfile.pdf")[2],
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("clustering"))
#### correcting ####
#' correcting
#'
#' Signal drift and batch effect correction
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param method.vc character of length 1 or the total number of datasets: method(s) to be used for each dataset (either 'serrf' or 'loess'); for the 'serrf' approach, the seed is internally set to 123 for reproducibility; in case the parameter is of length 1 and x contains multiple datasets, the same method will be used for all datasets
#' @param reference.vc character of length 1 or the total number of datasets: sample type to be used as reference for
#' the correction (as indicated in the 'sampleType' column from the
#' colData(x); e.g. 'pool' [default]); should be set to 'pool' for the 'serrf' method; in case the parameter is of length 1 and x contains multiple datasets, the same reference sample type will be used for all datasets
#' @param loess_span.vn character of length 1 or the total number of datasets: smoothing parameter for the loess regression;
#' between 0 and 1; (default set to 1); in case the parameter is of length 1 and x contains multiple datasets, the same span value will be used for all datasets
#' @param serrf_corvar.vi character of length 1 or the total number of datasets: number of correlated features for the random forest regression; (default set to 10); in case the parameter is of length 1 and x contains multiple datasets, the same value will be used for all datasets
#' @param sample_intensity.c Character: metric to be used when displaying the sample intensities
#' @param title.c Character: Graphic title: if NA [default] the
#' 'title' slot from the experimentData will be used (metadata)
#' @param col_batch.c Character: name of the column from colData(x) containing
#' the batch information (encoded as characters)
#' @param col_injectionOrder.c Character: name of the column from colData(x)
#' containing the injection order information (encoded as numerics)
#' @param col_sampleType.c Character: name of the column from colData(x)
#' containing the sample type information (encoded as characters)
#' @param figure.c Character: File name with '.pdf' extension for the figure;
#' if 'interactive' (default), figures will be displayed interactively; if 'none',
#' no figure will be generated
#' @param report.c Character: File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including the corrected
#' intensities in the assay matrix (matrices)
#' @rdname correcting
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' sacurine.se <- correcting(sacurine.se)
#' # MultiDataSet (to be done)
setGeneric("correcting",
function(x,
method.vc = c("loess", "serrf")[1],
reference.vc = c("pool", "sample")[1],
loess_span.vn = 1,
serrf_corvar.vi = 10,
sample_intensity.c = c("median", "mean", "sum")[2],
title.c = NA,
col_batch.c = "batch",
col_injectionOrder.c = "injectionOrder",
col_sampleType.c = "sampleType",
figure.c = c("none", "interactive", "myfile.pdf")[2],
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("correcting"))
#### filtering ####
#' Filtering of the features (or samples) with a high proportion of NAs or a low variance
#'
#' Filtering of the features (or samples) with a high proportion of NAs or a low variance
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param class.c character(1): name of the column of the sample metadata giving
#' the classification groups: the filtering will be applied on each class
#' (default: "" meaning that there are no specific classes to consider)
#' @param max_na_prop.n numeric(1): maximum proportion of NAs for a feature (or
#' sample) to be kept [default: 0.2] (in case 'class.c' is provided, the maximum
#' proportion of NAs for a feature must be achieved in at least one sample class)
#' @param min_variance.n numeric(1): minimum variance for a feature (or sample) to be kept
#' [default: .Machine$double.eps] (in case 'class.c' is provided, the minimum variance
#' for a feature must be achieved in all sample classes)
#' @param dims.vc Vector of one or two characters: dimension(s) to which the
#' filtering should be applied; either 'features', 'samples', or c('features', 'samples')
#' @param report.c character(1): File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including the filtered data and metadata
#' @rdname filtering
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' assay.mn <- assay(sacurine.se)
#' ropls::view(assay.mn)
#' filtering(sacurine.se)
#' assay.mn[assay.mn < 1e5] <- NA
#' ropls::view(assay.mn)
#' assay(sacurine.se) <- assay.mn
#' filtering(sacurine.se)
#' filtering(sacurine.se, class.c = "gender")
#' filtering(sacurine.se, class.c = "sampleType")
#' # MultiDataSet
#' prometis.mset <- reading(system.file("extdata/prometis", package="phenomis"))
#' filtering(prometis.mset)
#' for (set.c in names(prometis.mset)) {
#' eset <- prometis.mset[[set.c]]
#' exprs.mn <- Biobase::exprs(eset)
#' exprs.mn[exprs.mn < quantile(c(exprs.mn), 0.2)] <- NA
#' Biobase::exprs(eset) <- exprs.mn
#' prometis.mset <- MultiDataSet::add_eset(prometis.mset, eset, dataset.type = set.c,
#' GRanges = NA, overwrite = TRUE, warnings = FALSE)
#' }
#' filtering(prometis.mset)
setGeneric("filtering",
function(x,
class.c = "",
max_na_prop.n = 0.2,
min_variance.n = .Machine$double.eps,
dims.vc = c("features", "samples"),
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("filtering"))
#### hypotesting ####
#' Univariate hypothesis testing
#'
#' Univariate hypothesis testing
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param test.c Character: One of the 9 available hypothesis tests can be selected
#' (either 'ttest', 'limma', 'wilcoxon', 'anova', 'kruskal', 'pearson', 'spearman',
#' 'limma2ways', 'limma2waysInter', 'anova2ways', 'anova2waysInter')
#' @param factor_names.vc (Vector of) character(s): Factor(s) of interest (up to two),
#' i.e. name(s) of a column from the pData(x)
#' @param factor_levels.ls List: for each factor of interest (up to two), the levels
#' of the factor can be specified (i.e. re-ordered) by including a character vector
#' with those levels in the list; by default (no specification), the two vectors
#' are set to "default".
#' @param adjust.c Character: Name of the method for correction of multiple testing
#' (the p.adjust function is used)
#' @param adjust_thresh.n Numeric: Threshold for (corrected) p-values
#' @param signif_maxprint.i Interger: Maximum number of significant feature to display
#' on the screen (by default, 'NA', all significant features are displayed)
#' @param title.c Character: Title of the graphics
#' @param display_signif.l Character: In case of two sample tests (or
#' correlation test), should individual boxplots (or scatterplots) of significant
#' features be shown?
#' @param prefix.c Character: prefix to be added to the supplementary columns from
#' the variableMetadata to prevent overwriting of pre-existing columns with identical
#' names [default: ""]
#' @param figure.c Character: File name with '.pdf' extension for the figure (for
#' venn diagrams, e.g. in the 'anova2ways' test, the extension will be internally
#' changed to '.tiff' for compatibility with the VennDiagram package); if
#' 'interactive' (default), figures will be displayed interactively; if 'none',
#' no figure will be generated
#' @param report.c Character: File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including the difference in
#' means/medians or correlations and the adjusted p-values in feature metadata
#' @rdname hypotesting
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' sacurine.se <- correcting(sacurine.se, figure.c = 'none')
#' sacurine.se <- sacurine.se[, colData(sacurine.se)[, "sampleType"] != "pool"]
#' sacurine.se <- transforming(sacurine.se)
#' sacurine.se <- sacurine.se[, colnames(sacurine.se) != "HU_neg_096_b2"]
#' # Student's T test
#' sacurine.se <- hypotesting(sacurine.se, "ttest", "gender")
#' # Pearson correlation test
#' sacurine.se <- hypotesting(sacurine.se, "pearson", "age")
#' # ANOVA
#' colData(sacurine.se)[, "ageGroup"] <- vapply(colData(sacurine.se)[, "age"],
#' function(x) {
#' if (x < 35) {
#' return("thirty")
#' } else if (x < 50) {
#' return("fourty")
#' } else {
#' return("fifty")}},
#' FUN.VALUE = character(1))
#' sacurine.se <- hypotesting(sacurine.se, "anova", "ageGroup")
#' prometis.mset <- reading(system.file("extdata/prometis", package="phenomis"))
#' prometis.mset <- hypotesting(prometis.mset, "limma", "gene")
setGeneric("hypotesting",
function(x,
test.c = c("ttest", "limma", "wilcoxon",
"anova", "kruskal",
"pearson", "spearman",
"limma2ways", "limma2waysInter",
"anova2ways", "anova2waysInter")[2],
factor_names.vc,
factor_levels.ls = list(factor1Vc = "default", factor2Vc = "default"),
adjust.c = c("holm", "hochberg", "hommel", "bonferroni", "BH",
"BY", "fdr", "none")[5],
adjust_thresh.n = 0.05,
signif_maxprint.i = NA,
title.c = NA,
display_signif.l = FALSE,
prefix.c = "",
figure.c = c("none", "interactive", "interactive_plotly", "myfile.pdf")[2],
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("hypotesting"))
#### inspecting ####
#' Inspecting
#'
#' Provides numerical metrics and graphical overview of an ExpressionSet or MultiDataSet instance
#' Please note that all variables with a proportion of missing values > 'max_na_prop.n'
#' or a variance of 0 will be filtered out at the beginning of the method and
#' therefore in the output SummarizedExperiments(s)
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param pool_as_pool1.l Logical: should pool be included (as pool1) in the correlation
#' with the dilution factor?
#' @param pool_cv.n Numeric: threshold for the coefficient of variation of the pools
#' @param loess_span.n Numeric: span parameter used in the loess trend estimation
#' @param sample_intensity.c Character: function to be used to display the global
#' sample intensity; default: 'mean'
#' @param title.c Character: MultiAssayExperiment: title of the barplot showing the number
#' of samples and variables in each dataset; ExpressionSet: title of the multipanel
#' graphic displaying the metrics (if NA -default- the title slot from the experimentData
#' will be used)
#' @param plot_dims.l (MultiAssayExperiment) Logical: should an overview of the number of samples and
#' variables in all datasets be barplotted?
#' @param col_batch.c Character: name of the column from colData(x) containing
#' the batch information (encoded as characters)
#' @param col_injectionOrder.c Character: name of the column from colData(x)
#' containing the injection order information (encoded as numerics)
#' @param col_sampleType.c Character: name of the column from colData(x)
#' containing the sample type information (encoded as characters)
#' @param figure.c Character: File name with '.pdf' extension for the figure;
#' if 'interactive' (default), figures will be displayed interactively; if 'none',
#' no figure will be generated
#' @param report.c Character: File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including the computed
#' in rowData and colData sample and variable metrics
#' @rdname inspecting
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' sacurine.se <- inspecting(sacurine.se)
#' sacurine.se <- correcting(sacurine.se)
#' sacurine.se <- inspecting(sacurine.se)
#' sacurine.se <- transforming(sacurine.se)
#' sacurine.se <- inspecting(sacurine.se)
#' # MultiAssayExperiment
#' prometis.mae <- reading(system.file("extdata/prometis", package = "phenomis"))
#'\dontrun{
#' prometis.mae <- inspecting(prometis.mae)
#'}
setGeneric("inspecting",
function(x,
pool_as_pool1.l = FALSE,
pool_cv.n = 0.3,
loess_span.n = 1,
sample_intensity.c = c("median", "mean", "sum")[2],
title.c = NA,
plot_dims.l = TRUE,
col_batch.c = "batch",
col_injectionOrder.c = "injectionOrder",
col_sampleType.c = "sampleType",
figure.c = c("none", "interactive", "myfile.pdf")[2],
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("inspecting"))
#### normalizing ####
#' Normalization of the dataMatrix
#'
#' Normalization of the dataMatrix intensities
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param method.vc character of length 1 or the total number of datasets: method(s) to be used for each dataset (default is 'pqn'); in case the parameter is of length 1 and x contains multiple datasets, the same method will be used for all datasets
#' @param report.c character(1): File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including the (list of) matrix
#' with normalized intensities
#' @rdname normalizing
#' @export
#' @examples
#' sacurine.se <- reading(file.path(system.file(package = "phenomis"),
#' "extdata/W4M00001_Sacurine-statistics"))
#' sacurine.se <- sacurine.se[, colnames(sacurine.se) != 'HU_neg_096_b2']
#' sacurine.se <- transforming(sacurine.se, method.vc = "log10")
#' norm.se <- normalizing(sacurine.se, method.vc = "pqn")
#' # MultiDataSet
setGeneric("normalizing",
function(x,
method.vc = "pqn",
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("normalizing"))
#### reducing ####
#' Grouping chemically redundant MS1 features
#'
#' This method groups chemically redundant features from a peak table, based on
#' 1) correlation of sample profiles, 2) retention time window, 3) referenced
#' m/z differences. The initial algorithm is named 'Analytic Correlation Filtration'
#' (Monnerie et al., 2019; DOI:10.3390/metabo9110250) and is available in Perl and
#' on the Workflow4Metabolomics platform.
#' Here, the algorithm described in the paper was implemented in R as follows:
#' An adjacency matrix of all pairs of features is built, containing a 1 when the
#' features have a (Pearson) correlation above the (0.9) threshold, a retention time
#' difference between the (6) seconds threshold, and an m/z difference belonging to
#' referenced adducts, isotopes and fragments m/z difference, and containing a 0 otherwise.
#' The connex components of this adjacency matrix are extracted ('igraph' package).
#' Within each component, the features are ranked by decreasing average intensity in samples;
#' all features except the first one are flagged as 'redundant'.
#' Note: the algorithm relies on the 'mzdiff_db.tsv' file referencing the known adducts,
#' isotopes, and fragments.
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}:
#' the dataset(s) must contain the dataMatrix and the variableMetadata (with the
#' 'mz' and 'rt' columns)
#' @param cor_method.c character(1): correlation method (default: 'pearson')
#' @param cor_threshold.n numeric(1): correlation threshold (default: 0.9)
#' @param rt_tol.n numeric(1): retention time width in seconds (default: 6 s)
#' @param rt_colname.c character(1): column name for the retention time in the fData
#' (default: 'rt')
#' @param mzdiff_tol.n numeric(1): tolerance in Da for the matching of m/z differences
#' and referenced adducts, isotopes, and fragments (default: 0.005 Da)
#' @param mz_colname.c character(1): column name for the m/z in the fData (default: 'mz')
#' @param return_adjacency.l logical(1): should the adjacency matrix be returned
#' (in addition to the updated ExpressionSet)?
#' @param report.c character(1): File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return updated \code{SummarizedExperiment} or \code{MultiAssayExperiment}: the summarized experiment(s)
#' now include(s) 5 new columns in the rowData: 'redund_samp_mean', 'redund_is',
#' 'redund_group', redund_iso_add_frag', 'redund_repres' and 'redund_relative'
#' containing, respectively, the redundant features (coded by 1; i.e. features with
#' a relative annotation distinct from '' and 'M'), the connected components,
#' the m/z diff. chemical annotations, the representative ion of each group, and
#' the annotations relative to this representative ion within each group
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00002_Sacurine-comprehensive",
#' package = "phenomis"),
#' report.c = "none")
#' sacurine.se <- reducing(sacurine.se,
#' rt_tol.n = 6)
#' table(rowData(sacurine.se)[, "redund_group"])
setGeneric("reducing",
function(x,
cor_method.c = "pearson",
cor_threshold.n = 0.9,
rt_tol.n = 6,
rt_colname.c = "rt",
mzdiff_tol.n = 0.005,
mz_colname.c = "mz",
return_adjacency.l = FALSE,
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("reducing"))
#### transforming ####
#' Transformation of the dataMatrix
#'
#' Transformation of the dataMatrix intensities
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment} (\code{ExpressionSet} and \code{MultiDataSet} are deprecated)
#' @param method.vc character(): type of transformation (either 'log2', 'log10', or 'sqrt'); in case of a MultiAssayExperiment, distinct methods may be provided for each dataset
#' @param report.c Character: File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} (or \code{MultiAssayExperiment}) including the (list of) matrix
#' with transformed intensities
#' @rdname transforming
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' sacurine.se <- correcting(sacurine.se)
#' sacurine.se <- sacurine.se[, colData(sacurine.se)[, "sampleType"] != "pool"]
#' sacurine.se <- transforming(sacurine.se)
#' # MultiDataSet
#' prometis.mae <- reading(system.file("extdata/prometis", package = "phenomis"))
#' prometis.mset <- transforming(prometis.mset)
setGeneric("transforming",
function(x,
method.vc = c("log2", "log10", "sqrt")[1],
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("transforming"))
#### writing ####
#' Exporting a SummarizedExperiment (or MultiAssayExperiment) instance into (subfolders with)
#' the 3 tabulated files 'dataMatrix.tsv', sampleMetadata.tsv', 'variableMetadata.tsv'
#'
#' Note that the \code{dataMatrix} is transposed before export (e.g., the samples
#' are written column wise in the 'dataMatrix.tsv' exported file).
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param dir.c character(1): directory where each dataset should be written
#' @param prefix.c character(1): prefix to be used (followed by '_') in the
#' 'dataMatrix.tsv', 'sampleMetadata.tsv', and 'variableMetadata.tsv' file names
#' @param files.ls list: alternatively to the dir.c argument, the full names of the files can be provided as a list
#' @param overwrite.l logical(1): should existing files be overwritten?
#' @param report.c character(1): File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return No object returned.
#' @rdname writing
#' @export
#' @examples
#' metabo.se <- reading(system.file("extdata/prometis/metabolomics", package="phenomis"))
#'\dontrun{
#' writing(metabo.se, dir.c = file.path(getwd(), "metabolomics"))
#'}
#'# MultiAssayExperiment
#' prometis.mae <- reading(system.file("extdata/prometis",package="phenomis"))
#'\dontrun{
#' writing(prometis.mae, dir.c = file.path(getwd(), "prometis"))
#' # alternatively
#' writing(prometis.mae,
#' dir.c = NA,
#' files.ls = list(metabolomics = list(dataMatrix.tsvC = file.path(getwd(), "met_dataMatrix.tsv"),
#' sampleMetadata.tsvC = file.path(getwd(), "met_sampleMetadata.tsv"),
#' variableMetadata.tsvC = file.path(getwd(), "met_variableMetadata.tsv")),
#' proteomics = list(dataMatrix.tsvC = file.path(getwd(), "pro_dataMatrix.tsv"),
#' sampleMetadata.tsvC = file.path(getwd(), "pro_sampleMetadata.tsv"),
#' variableMetadata.tsvC = file.path(getwd(), "pro_variableMetadata.tsv"))))
#'}
setGeneric("writing",
function(x,
dir.c,
prefix.c = "",
files.ls = NULL,
overwrite.l = FALSE,
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("writing"))
SciDoPhenIA/phenomis documentation built on June 9, 2022, 11:54 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#### annotating ####
#' MS annotation
#'
#' Annotation with chemical and biological databases
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param database.c character: database to be used for annotation
#' @param param.ls list: parameters for database query; see the example below for
#' the name and related database of each of them
#' @param report.c Character: File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including the appended rowData
#' data frame(s)
#' @rdname annotating
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' # see the (default) parameters (e.g. for ChEBI query)
#' annotating_parameters("chebi")
#' # mz annotation with ChEBI
#' sacurine.se <- annotating(sacurine.se, database.c = "chebi", param.ls = list(query.type = "mz", query.col = "mass_to_charge", ms.mode = "neg", prefix = "chebiMZ."))
#' # mz annotation with local database
#' msdbDF <- read.table(system.file("extdata/local_ms_db.tsv", package = "phenomis"),
#' header = TRUE, sep = "\t", stringsAsFactors = FALSE)
#' sacurine.se <- annotating(sacurine.se, database.c = "local.ms",
#' param.ls = list(query.type = "mz", query.col = "mass_to_charge", ms.mode = "neg",
#' mz.tol = 5, mz.tol.unit = "ppm", local.ms.db = msdbDF, prefix = "localMS."))
#' rowData(sacurine.se)[!is.na(rowData(sacurine.se)[, "localMS.accession"]), ]
#' # annotation from ChEBI identifiers
#' sacurine.se <- annotating(sacurine.se, database.c = "chebi",
#' param.ls = list(query.type = "chebi.id", query.col = "database_identifier",
#' prefix = "chebiID."))
#' head(rowData(sacurine.se))
setGeneric("annotating",
function(x,
database.c = c("chebi", "local.ms")[1],
param.ls = list(query.type = c("mz", "chebi.id", "kegg.id")[1],
query.col = "mz",
ms.mode = "pos",
mz.tol = 10,
mz.tol.unit = "ppm",
fields = c("chebi.id", "name", "formula", "molecular.mass", "monoisotopic.mass"),
fieldsLimit = 1,
max.results = 3,
local.ms.db = data.frame(),
organism = "hsa",
prefix = paste0(database.c, "."),
sep = "|"),
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("annotating"))
#### clustering ####
#' clustering
#'
#' Hierarchical clustering of both samples and variables
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param dissym.c character: [hclust]
#' @param correl.c character: correlation coefficient (in case
#' '1-cor' or '1-abs(cor)' are selected as dissymilarity)
#' @param agglo.c character: agglomeration method
#' @param clusters.vi tupple of integers: number of sample and variable clusters, respectively
#' @param cex.vn tupple of numerics [Plot parameter]; size of the sample and variable labels
#' @param palette.c character [Plot parameter]: color palette
#' @param scale_plot.l logical [Plot parameter]: scaling (mean-centering and unit
#' variance scaling) to enhance contrast (for plotting only)
#' @param title.c character [Plot parameter]: Graphic the subtitle
#' @param figure.c character: File name with '.pdf' extension for the figure;
#' if 'interactive' (default), figures will be displayed interactively; if 'none',
#' no figure will be generated
#' @param report.c character: File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including columns indicating
#' the clusters in rowData and colData if 'clusters.vi' has been specified
#' @rdname clustering
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' sacurine.se <- correcting(sacurine.se)
#' sacurine.se <- sacurine.se[, pData(sacurine.se)[, "sampleType"] != "pool"]
#' sacurine.se <- transforming(sacurine.se)
#' sacurine.se <- sacurine.se[, sampleNames(sacurine.se) != "HU_neg_096_b2"]
#' sacurine.se <- clustering(sacurine.se)
#' utils::head(fData(sacurine.se))
#' # MultiDataSet
#' prometis.mset <- reading(system.file("extdata/prometis/", package="phenomis"))
#' prometis.mset <- clustering(prometis.mset, clusters.vi = c(3, 3))
setGeneric("clustering",
function(x,
dissym.c = c("euclidean",
"maximum",
"manhattan",
"canberra",
"binary",
"minkowski",
"1-cor",
"1-abs(cor)")[7],
correl.c = c("pearson",
"kendall",
"spearman")[1],
agglo.c = c("ward.D",
"ward.D2",
"single",
"complete",
"average",
"mcquitty",
"median",
"centroid")[2],
clusters.vi = c(2, 2),
cex.vn = c(1, 1),
palette.c = c("blueOrangeRed",
"redBlackGreen")[1],
scale_plot.l = TRUE,
title.c = NA,
figure.c = c("none", "interactive", "myfile.pdf")[2],
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("clustering"))
#### correcting ####
#' correcting
#'
#' Signal drift and batch effect correction
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param method.vc character of length 1 or the total number of datasets: method(s) to be used for each dataset (either 'serrf' or 'loess'); for the 'serrf' approach, the seed is internally set to 123 for reproducibility; in case the parameter is of length 1 and x contains multiple datasets, the same method will be used for all datasets
#' @param reference.vc character of length 1 or the total number of datasets: sample type to be used as reference for
#' the correction (as indicated in the 'sampleType' column from the
#' colData(x); e.g. 'pool' [default]); should be set to 'pool' for the 'serrf' method; in case the parameter is of length 1 and x contains multiple datasets, the same reference sample type will be used for all datasets
#' @param loess_span.vn character of length 1 or the total number of datasets: smoothing parameter for the loess regression;
#' between 0 and 1; (default set to 1); in case the parameter is of length 1 and x contains multiple datasets, the same span value will be used for all datasets
#' @param serrf_corvar.vi character of length 1 or the total number of datasets: number of correlated features for the random forest regression; (default set to 10); in case the parameter is of length 1 and x contains multiple datasets, the same value will be used for all datasets
#' @param sample_intensity.c Character: metric to be used when displaying the sample intensities
#' @param title.c Character: Graphic title: if NA [default] the
#' 'title' slot from the experimentData will be used (metadata)
#' @param col_batch.c Character: name of the column from colData(x) containing
#' the batch information (encoded as characters)
#' @param col_injectionOrder.c Character: name of the column from colData(x)
#' containing the injection order information (encoded as numerics)
#' @param col_sampleType.c Character: name of the column from colData(x)
#' containing the sample type information (encoded as characters)
#' @param figure.c Character: File name with '.pdf' extension for the figure;
#' if 'interactive' (default), figures will be displayed interactively; if 'none',
#' no figure will be generated
#' @param report.c Character: File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including the corrected
#' intensities in the assay matrix (matrices)
#' @rdname correcting
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' sacurine.se <- correcting(sacurine.se)
#' # MultiDataSet (to be done)
setGeneric("correcting",
function(x,
method.vc = c("loess", "serrf")[1],
reference.vc = c("pool", "sample")[1],
loess_span.vn = 1,
serrf_corvar.vi = 10,
sample_intensity.c = c("median", "mean", "sum")[2],
title.c = NA,
col_batch.c = "batch",
col_injectionOrder.c = "injectionOrder",
col_sampleType.c = "sampleType",
figure.c = c("none", "interactive", "myfile.pdf")[2],
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("correcting"))
#### filtering ####
#' Filtering of the features (or samples) with a high proportion of NAs or a low variance
#'
#' Filtering of the features (or samples) with a high proportion of NAs or a low variance
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param class.c character(1): name of the column of the sample metadata giving
#' the classification groups: the filtering will be applied on each class
#' (default: "" meaning that there are no specific classes to consider)
#' @param max_na_prop.n numeric(1): maximum proportion of NAs for a feature (or
#' sample) to be kept [default: 0.2] (in case 'class.c' is provided, the maximum
#' proportion of NAs for a feature must be achieved in at least one sample class)
#' @param min_variance.n numeric(1): minimum variance for a feature (or sample) to be kept
#' [default: .Machine$double.eps] (in case 'class.c' is provided, the minimum variance
#' for a feature must be achieved in all sample classes)
#' @param dims.vc Vector of one or two characters: dimension(s) to which the
#' filtering should be applied; either 'features', 'samples', or c('features', 'samples')
#' @param report.c character(1): File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including the filtered data and metadata
#' @rdname filtering
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' assay.mn <- assay(sacurine.se)
#' ropls::view(assay.mn)
#' filtering(sacurine.se)
#' assay.mn[assay.mn < 1e5] <- NA
#' ropls::view(assay.mn)
#' assay(sacurine.se) <- assay.mn
#' filtering(sacurine.se)
#' filtering(sacurine.se, class.c = "gender")
#' filtering(sacurine.se, class.c = "sampleType")
#' # MultiDataSet
#' prometis.mset <- reading(system.file("extdata/prometis", package="phenomis"))
#' filtering(prometis.mset)
#' for (set.c in names(prometis.mset)) {
#' eset <- prometis.mset[[set.c]]
#' exprs.mn <- Biobase::exprs(eset)
#' exprs.mn[exprs.mn < quantile(c(exprs.mn), 0.2)] <- NA
#' Biobase::exprs(eset) <- exprs.mn
#' prometis.mset <- MultiDataSet::add_eset(prometis.mset, eset, dataset.type = set.c,
#' GRanges = NA, overwrite = TRUE, warnings = FALSE)
#' }
#' filtering(prometis.mset)
setGeneric("filtering",
function(x,
class.c = "",
max_na_prop.n = 0.2,
min_variance.n = .Machine$double.eps,
dims.vc = c("features", "samples"),
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("filtering"))
#### hypotesting ####
#' Univariate hypothesis testing
#'
#' Univariate hypothesis testing
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param test.c Character: One of the 9 available hypothesis tests can be selected
#' (either 'ttest', 'limma', 'wilcoxon', 'anova', 'kruskal', 'pearson', 'spearman',
#' 'limma2ways', 'limma2waysInter', 'anova2ways', 'anova2waysInter')
#' @param factor_names.vc (Vector of) character(s): Factor(s) of interest (up to two),
#' i.e. name(s) of a column from the pData(x)
#' @param factor_levels.ls List: for each factor of interest (up to two), the levels
#' of the factor can be specified (i.e. re-ordered) by including a character vector
#' with those levels in the list; by default (no specification), the two vectors
#' are set to "default".
#' @param adjust.c Character: Name of the method for correction of multiple testing
#' (the p.adjust function is used)
#' @param adjust_thresh.n Numeric: Threshold for (corrected) p-values
#' @param signif_maxprint.i Interger: Maximum number of significant feature to display
#' on the screen (by default, 'NA', all significant features are displayed)
#' @param title.c Character: Title of the graphics
#' @param display_signif.l Character: In case of two sample tests (or
#' correlation test), should individual boxplots (or scatterplots) of significant
#' features be shown?
#' @param prefix.c Character: prefix to be added to the supplementary columns from
#' the variableMetadata to prevent overwriting of pre-existing columns with identical
#' names [default: ""]
#' @param figure.c Character: File name with '.pdf' extension for the figure (for
#' venn diagrams, e.g. in the 'anova2ways' test, the extension will be internally
#' changed to '.tiff' for compatibility with the VennDiagram package); if
#' 'interactive' (default), figures will be displayed interactively; if 'none',
#' no figure will be generated
#' @param report.c Character: File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including the difference in
#' means/medians or correlations and the adjusted p-values in feature metadata
#' @rdname hypotesting
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' sacurine.se <- correcting(sacurine.se, figure.c = 'none')
#' sacurine.se <- sacurine.se[, colData(sacurine.se)[, "sampleType"] != "pool"]
#' sacurine.se <- transforming(sacurine.se)
#' sacurine.se <- sacurine.se[, colnames(sacurine.se) != "HU_neg_096_b2"]
#' # Student's T test
#' sacurine.se <- hypotesting(sacurine.se, "ttest", "gender")
#' # Pearson correlation test
#' sacurine.se <- hypotesting(sacurine.se, "pearson", "age")
#' # ANOVA
#' colData(sacurine.se)[, "ageGroup"] <- vapply(colData(sacurine.se)[, "age"],
#' function(x) {
#' if (x < 35) {
#' return("thirty")
#' } else if (x < 50) {
#' return("fourty")
#' } else {
#' return("fifty")}},
#' FUN.VALUE = character(1))
#' sacurine.se <- hypotesting(sacurine.se, "anova", "ageGroup")
#' prometis.mset <- reading(system.file("extdata/prometis", package="phenomis"))
#' prometis.mset <- hypotesting(prometis.mset, "limma", "gene")
setGeneric("hypotesting",
function(x,
test.c = c("ttest", "limma", "wilcoxon",
"anova", "kruskal",
"pearson", "spearman",
"limma2ways", "limma2waysInter",
"anova2ways", "anova2waysInter")[2],
factor_names.vc,
factor_levels.ls = list(factor1Vc = "default", factor2Vc = "default"),
adjust.c = c("holm", "hochberg", "hommel", "bonferroni", "BH",
"BY", "fdr", "none")[5],
adjust_thresh.n = 0.05,
signif_maxprint.i = NA,
title.c = NA,
display_signif.l = FALSE,
prefix.c = "",
figure.c = c("none", "interactive", "interactive_plotly", "myfile.pdf")[2],
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("hypotesting"))
#### inspecting ####
#' Inspecting
#'
#' Provides numerical metrics and graphical overview of an ExpressionSet or MultiDataSet instance
#' Please note that all variables with a proportion of missing values > 'max_na_prop.n'
#' or a variance of 0 will be filtered out at the beginning of the method and
#' therefore in the output SummarizedExperiments(s)
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param pool_as_pool1.l Logical: should pool be included (as pool1) in the correlation
#' with the dilution factor?
#' @param pool_cv.n Numeric: threshold for the coefficient of variation of the pools
#' @param loess_span.n Numeric: span parameter used in the loess trend estimation
#' @param sample_intensity.c Character: function to be used to display the global
#' sample intensity; default: 'mean'
#' @param title.c Character: MultiAssayExperiment: title of the barplot showing the number
#' of samples and variables in each dataset; ExpressionSet: title of the multipanel
#' graphic displaying the metrics (if NA -default- the title slot from the experimentData
#' will be used)
#' @param plot_dims.l (MultiAssayExperiment) Logical: should an overview of the number of samples and
#' variables in all datasets be barplotted?
#' @param col_batch.c Character: name of the column from colData(x) containing
#' the batch information (encoded as characters)
#' @param col_injectionOrder.c Character: name of the column from colData(x)
#' containing the injection order information (encoded as numerics)
#' @param col_sampleType.c Character: name of the column from colData(x)
#' containing the sample type information (encoded as characters)
#' @param figure.c Character: File name with '.pdf' extension for the figure;
#' if 'interactive' (default), figures will be displayed interactively; if 'none',
#' no figure will be generated
#' @param report.c Character: File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including the computed
#' in rowData and colData sample and variable metrics
#' @rdname inspecting
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' sacurine.se <- inspecting(sacurine.se)
#' sacurine.se <- correcting(sacurine.se)
#' sacurine.se <- inspecting(sacurine.se)
#' sacurine.se <- transforming(sacurine.se)
#' sacurine.se <- inspecting(sacurine.se)
#' # MultiAssayExperiment
#' prometis.mae <- reading(system.file("extdata/prometis", package = "phenomis"))
#'\dontrun{
#' prometis.mae <- inspecting(prometis.mae)
#'}
setGeneric("inspecting",
function(x,
pool_as_pool1.l = FALSE,
pool_cv.n = 0.3,
loess_span.n = 1,
sample_intensity.c = c("median", "mean", "sum")[2],
title.c = NA,
plot_dims.l = TRUE,
col_batch.c = "batch",
col_injectionOrder.c = "injectionOrder",
col_sampleType.c = "sampleType",
figure.c = c("none", "interactive", "myfile.pdf")[2],
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("inspecting"))
#### normalizing ####
#' Normalization of the dataMatrix
#'
#' Normalization of the dataMatrix intensities
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param method.vc character of length 1 or the total number of datasets: method(s) to be used for each dataset (default is 'pqn'); in case the parameter is of length 1 and x contains multiple datasets, the same method will be used for all datasets
#' @param report.c character(1): File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} or \code{MultiAssayExperiment} including the (list of) matrix
#' with normalized intensities
#' @rdname normalizing
#' @export
#' @examples
#' sacurine.se <- reading(file.path(system.file(package = "phenomis"),
#' "extdata/W4M00001_Sacurine-statistics"))
#' sacurine.se <- sacurine.se[, colnames(sacurine.se) != 'HU_neg_096_b2']
#' sacurine.se <- transforming(sacurine.se, method.vc = "log10")
#' norm.se <- normalizing(sacurine.se, method.vc = "pqn")
#' # MultiDataSet
setGeneric("normalizing",
function(x,
method.vc = "pqn",
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("normalizing"))
#### reducing ####
#' Grouping chemically redundant MS1 features
#'
#' This method groups chemically redundant features from a peak table, based on
#' 1) correlation of sample profiles, 2) retention time window, 3) referenced
#' m/z differences. The initial algorithm is named 'Analytic Correlation Filtration'
#' (Monnerie et al., 2019; DOI:10.3390/metabo9110250) and is available in Perl and
#' on the Workflow4Metabolomics platform.
#' Here, the algorithm described in the paper was implemented in R as follows:
#' An adjacency matrix of all pairs of features is built, containing a 1 when the
#' features have a (Pearson) correlation above the (0.9) threshold, a retention time
#' difference between the (6) seconds threshold, and an m/z difference belonging to
#' referenced adducts, isotopes and fragments m/z difference, and containing a 0 otherwise.
#' The connex components of this adjacency matrix are extracted ('igraph' package).
#' Within each component, the features are ranked by decreasing average intensity in samples;
#' all features except the first one are flagged as 'redundant'.
#' Note: the algorithm relies on the 'mzdiff_db.tsv' file referencing the known adducts,
#' isotopes, and fragments.
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}:
#' the dataset(s) must contain the dataMatrix and the variableMetadata (with the
#' 'mz' and 'rt' columns)
#' @param cor_method.c character(1): correlation method (default: 'pearson')
#' @param cor_threshold.n numeric(1): correlation threshold (default: 0.9)
#' @param rt_tol.n numeric(1): retention time width in seconds (default: 6 s)
#' @param rt_colname.c character(1): column name for the retention time in the fData
#' (default: 'rt')
#' @param mzdiff_tol.n numeric(1): tolerance in Da for the matching of m/z differences
#' and referenced adducts, isotopes, and fragments (default: 0.005 Da)
#' @param mz_colname.c character(1): column name for the m/z in the fData (default: 'mz')
#' @param return_adjacency.l logical(1): should the adjacency matrix be returned
#' (in addition to the updated ExpressionSet)?
#' @param report.c character(1): File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return updated \code{SummarizedExperiment} or \code{MultiAssayExperiment}: the summarized experiment(s)
#' now include(s) 5 new columns in the rowData: 'redund_samp_mean', 'redund_is',
#' 'redund_group', redund_iso_add_frag', 'redund_repres' and 'redund_relative'
#' containing, respectively, the redundant features (coded by 1; i.e. features with
#' a relative annotation distinct from '' and 'M'), the connected components,
#' the m/z diff. chemical annotations, the representative ion of each group, and
#' the annotations relative to this representative ion within each group
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00002_Sacurine-comprehensive",
#' package = "phenomis"),
#' report.c = "none")
#' sacurine.se <- reducing(sacurine.se,
#' rt_tol.n = 6)
#' table(rowData(sacurine.se)[, "redund_group"])
setGeneric("reducing",
function(x,
cor_method.c = "pearson",
cor_threshold.n = 0.9,
rt_tol.n = 6,
rt_colname.c = "rt",
mzdiff_tol.n = 0.005,
mz_colname.c = "mz",
return_adjacency.l = FALSE,
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("reducing"))
#### transforming ####
#' Transformation of the dataMatrix
#'
#' Transformation of the dataMatrix intensities
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment} (\code{ExpressionSet} and \code{MultiDataSet} are deprecated)
#' @param method.vc character(): type of transformation (either 'log2', 'log10', or 'sqrt'); in case of a MultiAssayExperiment, distinct methods may be provided for each dataset
#' @param report.c Character: File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return \code{SummarizedExperiment} (or \code{MultiAssayExperiment}) including the (list of) matrix
#' with transformed intensities
#' @rdname transforming
#' @export
#' @examples
#' sacurine.se <- reading(system.file("extdata/W4M00001_Sacurine-statistics", package = "phenomis"))
#' sacurine.se <- correcting(sacurine.se)
#' sacurine.se <- sacurine.se[, colData(sacurine.se)[, "sampleType"] != "pool"]
#' sacurine.se <- transforming(sacurine.se)
#' # MultiDataSet
#' prometis.mae <- reading(system.file("extdata/prometis", package = "phenomis"))
#' prometis.mset <- transforming(prometis.mset)
setGeneric("transforming",
function(x,
method.vc = c("log2", "log10", "sqrt")[1],
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("transforming"))
#### writing ####
#' Exporting a SummarizedExperiment (or MultiAssayExperiment) instance into (subfolders with)
#' the 3 tabulated files 'dataMatrix.tsv', sampleMetadata.tsv', 'variableMetadata.tsv'
#'
#' Note that the \code{dataMatrix} is transposed before export (e.g., the samples
#' are written column wise in the 'dataMatrix.tsv' exported file).
#'
#' @param x An S4 object of class \code{SummarizedExperiment} or \code{MultiAssayExperiment}
#' @param dir.c character(1): directory where each dataset should be written
#' @param prefix.c character(1): prefix to be used (followed by '_') in the
#' 'dataMatrix.tsv', 'sampleMetadata.tsv', and 'variableMetadata.tsv' file names
#' @param files.ls list: alternatively to the dir.c argument, the full names of the files can be provided as a list
#' @param overwrite.l logical(1): should existing files be overwritten?
#' @param report.c character(1): File name with '.txt' extension for the printed
#' results (call to sink()'); if 'interactive' (default), messages will be
#' printed on the screen; if 'none', no verbose will be generated
#' @return No object returned.
#' @rdname writing
#' @export
#' @examples
#' metabo.se <- reading(system.file("extdata/prometis/metabolomics", package="phenomis"))
#'\dontrun{
#' writing(metabo.se, dir.c = file.path(getwd(), "metabolomics"))
#'}
#'# MultiAssayExperiment
#' prometis.mae <- reading(system.file("extdata/prometis",package="phenomis"))
#'\dontrun{
#' writing(prometis.mae, dir.c = file.path(getwd(), "prometis"))
#' # alternatively
#' writing(prometis.mae,
#' dir.c = NA,
#' files.ls = list(metabolomics = list(dataMatrix.tsvC = file.path(getwd(), "met_dataMatrix.tsv"),
#' sampleMetadata.tsvC = file.path(getwd(), "met_sampleMetadata.tsv"),
#' variableMetadata.tsvC = file.path(getwd(), "met_variableMetadata.tsv")),
#' proteomics = list(dataMatrix.tsvC = file.path(getwd(), "pro_dataMatrix.tsv"),
#' sampleMetadata.tsvC = file.path(getwd(), "pro_sampleMetadata.tsv"),
#' variableMetadata.tsvC = file.path(getwd(), "pro_variableMetadata.tsv"))))
#'}
setGeneric("writing",
function(x,
dir.c,
prefix.c = "",
files.ls = NULL,
overwrite.l = FALSE,
report.c = c("none", "interactive", "myfile.txt")[2])
standardGeneric("writing"))
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.