Package index • DaparToolshed

All functions

Add_Aggregated_rowData(): Add aggregated rowData

Add_Item_to_Dataset(): xxx

BuildAdjacencyMatrix(): Function matrix of appartenance group

BuildColumnToProteinDataset(): creates a column for the protein dataset after agregation by using the previous peptide dataset.

BuildMetacell(): metacell function which xxx

Children(): Names of all chidren of a node

CleanRowData(): Customised resetZoomButton of highcharts plots

ConvertListToHtml(): Convert a list to unnumbered HTML list

CountPep(): Compute the number of peptides used to aggregate proteins

aggregateFeatures4Prostar() aggQmetacell() aggregateMethods(): Aggregate an assay's quantitative features

aggregateRedistribution(): Aggregate an assay's quantitative features with shared peptide redistribution

DaparToolshed: DaparToolshed: A package for computating the notorious bar statistic

ExtendPalette(): Extends a base-palette of the package RColorBrewer to n colors.

ExtractUniquePeptides(): Test

GetColorsForConditions(): Builds a complete color palette for the conditions given in argument

GetDetailedNbPeptides(): Computes the detailed number of peptides for each protein

GetDetailedNbPeptidesUsed(): Computes the detailed number of peptides used for aggregating each protein

GetIndices_BasedOnConditions(): Search lines which respects request on one or more conditions.

GetIndices_FunFiltering(): Delete the lines in the matrix of intensities and the metadata table given their indices.

GetIndices_WholeLine(): Search lines which respects query on all their elements.

GetIndices_WholeMatrix(): Search lines which respects request on one or more conditions.

GetNbPeptidesUsed(): Computes the number of peptides used for aggregating each protein

GetNbTags(): Number of each metacell tags

GetSoftAvailables(): The set of available softwares to convert from

GraphPepProt(): Function to create a histogram that shows the repartition of peptides w.r.t. the proteins

Keep_Items_from_Dataset(): xxx

LH0(): xxxxxx

LH0.lm(): xxxxxx

LH1(): xxxxxx

LH1.lm(): xxxxxx

MetacellFilteringScope(): Lists the metacell scopes for filtering

Metacell_DIA_NN(): Sets the metacell dataframe for datasets which are from Dia-NN software

Metacell_maxquant(): Sets the metacell dataframe

Metacell_proline(): Sets the metacell dataframe for datasets which are from Proline software

NAIsZero(): xxx

OWAnova(): Applies aov() on a vector of protein abundances using the design derived from the sample names (simple aov wrapper)

Parent(): Parent name of a node

Pipelines(): xxx

ProstarVersions(): Version number of Prostar suite

paramshistory() `paramshistory<-`() GetMetacellTags() qMetacell() `qMetacell<-`() GetUniqueTags() .GetMetadataSlot() .GetRowdataSlot() ConnectedComp() `ConnectedComp<-`() typeDataset() `typeDataset<-`() idcol() `idcol<-`() parentProtId() `parentProtId<-`() filename() `filename<-`() analysis() `analysis<-`() version() `version<-`() design.qf() `design.qf<-`() mainAssay() HypothesisTest() `HypothesisTest<-`() DifferentialAnalysis() `DifferentialAnalysis<-`() names_metacell() `names_metacell<-`(): List of metacell tags

write2excel() .write2excel() addColors(): Exports a QFeatures object to a Excel file.

FunctionFilter() filterFeaturesOneSE(): Filter features of one SE based on their rowData

last_assay() n_assays_in_qf(): Utility funcitons to dela with QFeatures objects.

ReplaceSpecialChars(): Standardize names

RunAggregation(): Aggregation of peptide-level assay QFeatures

AdjMatFilters() allPeptides() specPeptides() subAdjMat_specificPeptides() sharedPeptides() subAdjMat_sharedPeptides() topnFunctions() topnPeptides() subAdjMat_topnPeptides(): Filter a peptide assay on the basis of its adjacency matrix.

applyAnovasOnProteins(): iteratively applies OWAnova() on the features of an MSnSet object

buildGraph(): Display a CC

check.conditions(): Check if the design is valid

check.design(): Check if the design is valid

classic1wayAnova(): Function to perform a One-way Anova statistical test on a MsnBase dataset

plotCompareAssays(): Compare two assays

compareNormalizationD_HC(): Builds a plot from a dataframe. Same as compareNormalizationD but uses the library highcharter

compute.selection.table(): Applies an FDR threshold on a table of adjusted p-values and summarizes the results

compute_t_tests(): xxxxxx

create_ft_example(): Creates an example dataset

display.CC.visNet(): Display a CC

formatHSDResults(): xxx

formatLimmaResult(): xxxx

formatPHResults(): Extract logFC and raw pvalues from multiple post-hoc models summaries

formatPHTResults(): xxx

ft: ft dataset

ft_na: ft_na dataset

fudge2LRT(): Heuristic to choose the value of the hyperparameter (fudge factor) used to regularize the variance estimator in the likelihood ratio statistic

get.pep.prot.cc(): Build the list of connex composant of the adjacency matrix

getDesignLevel(): xxx

GetExtension(): Extension of a file

getListNbValuesInLines(): Returns the possible number of values in lines in the data

getNumberOfEmptyLines(): Returns the number of empty lines in the data

getProteinsStats(): Computes the number of proteins that are only defined by specific peptides, shared peptides or a mixture of two.

globalAdjPval(): Computes the adjusted p-values on all the stacked contrasts using CP4P

hc_logFC_DensityPlot(): Density plots of logFC values

createQFeatures(): Creates an object of class QFeatures from text file.

impute.pa2(): Missing values imputation from a MSnSet object

inner.aggregate.iter(): Aggregation of quantitative data with redistribution of shared peptides

inner.mean(): Mean-based aggregation

inner.median(): Median-based aggregation

inner.medianpolish(): medianPolish-based aggregation

inner.robustsummary(): robustSummary-based aggregation

inner.sum(): Sum-based aggregation

is.OfType(): Similar to the function is.na() but focused on the equality with the paramter 'type'.

is.subset(): xxx

limmaCompleteTest(): Computes a hierarchical differential analysis

make.contrast(): Builds the contrast matrix

make.design.1(): Builds the design matrix for designs of level 1

make.design.2(): Builds the design matrix for designs of level 2

make.design.3(): Builds the design matrix for designs of level 3

make.design(): Builds the design matrix

match.metacell(): Similar to the function is.na() but focused on the equality with the paramter 'type'.

metacellPerLinesHisto_HC() metacellPerLinesHistoPerCondition_HC() metacellHisto_HC() wrapper.mvImage() mvImage() hc_mvTypePlot2(): Bar plot of missing values per lines using highcharter

metacell_agg(): Metacell aggregation

findMECBlock() reIntroduceMEC() wrapper.impute.KNN() wrapper.impute.fixedValue() wrapper.impute.pa() wrapper.impute.detQuant() getQuantile4Imp() wrapper.impute.slsa(): Finds the LAPALA

my_hc_ExportMenu(): Customised contextual menu of highcharts plots

my_hc_chart(): Customised resetZoomButton of highcharts plots

nEmptyLines(): Number of empty lines in the data

nonzero(): Retrieve the indices of non-zero elements in sparse matrices

normalizeMethods() GlobalQuantileAlignment() SumByColumns() QuantileCentering() MeanCentering() vsn() LOESS(): Normalisation

readExcel() listSheets() write_Assay_To_Excel() WriteHistory() Write_SamplesData_to_Excel() Write_RowData() write.excel(): This function exports a data.frame to a Excel file.

pepa.test(): PEptide based Protein differential Abundance test

plotJitter(): Jitter plot of CC

plotJitter_rCharts(): Display a a jitter plot for CC

postHocTest(): Post-hoc tests for classic 1-way ANOVA

SymFilteringOperators() qMetacellFilteringScope() qMetacellWholeMatrix() qMetacellWholeLine() qMetacellOnConditions(): Search lines which respects request on one or more conditions.

metacell.def() custom_metacell_colors() Set_POV_MEC_tags() Set_POV_MEC_tags2() Metacell_generic() UpdateMetacellAfterImputation() search.metacell.tags() metacombine(): Quantitative metadata vocabulary for entities

samLRT(): xxxxxx

select_topn(): Selection of top n peptides

separateAdjPval(): Computes the adjusted p-values separately on contrast using CP4P

splitAdjacencyMat(): splits an adjacency matrix into specific and shared

test.design(): Check if xxxxxx

testAnovaModels(): Applies a statistical test on each element of a list of linear models

thresholdpval4fdr(): xxx

translatedRandomBeta(): Generator of simulated values

wrapper.dapar.impute.mi(): Missing values imputation using the LSimpute algorithm.

wrapper.impute.mle(): Imputation of peptides having no values in a biological condition.

wrapper.impute.pa2(): Missing values imputation from a MSnSet object

wrapperClassic1wayAnova(): Wrapper for One-way Anova statistical test