Package index • VisomX

All functions

Get.bwss(): Compute within group and between group sum of squares (BSS/WSS) for each row of a matrix which may have NA

Intersect(): Multiple set version of intersect

Perform.permutation(): Permutation

Setdiff(): Remove the union of the y's from the common x's.

Union(): Multiple set version of union

colFmt(): Format font color for Markdown reports

delete_prefix(): Delete Prefix

.onAttach(): Run upon attaching package VisomX

fast.write.csv(): Write object in CSV file

filter_missval(): Filter SummarizedExperiment object based on missing values

flux_to_map(): Create a metabolic map with fluxes and export as SVG file

get_prefix(): Get the common prefix of words

make_se(): Create a SummarizedExperiment object from a data frame

manual_impute(): Imputation by random draws from a manually defined distribution

meanSdPlot(): Plot row standard deviations versus row means

met.ANOVA.Anal(): Perform ANOVA analysis

met.FC.Anal(): Fold change analysis, unpaired

met.FilterVariable(): Methods for non-specific filtering of variables

met.GetFC(): Used by higher functions to calculate fold change

met.GetTtestRes(): Used by higher functions to retrieve T-test p-values

met.PCA.Anal(): Perform a Principal Component Analysis

met.PLSDA.CV(): PLS-DA classification and feature selection

met.PLSDA.Permut(): Perform PLS-DA permutation

met.PLSR.Anal(): Partial least squares (PLS) analysis using oscorespls (Orthogonal scores algorithm)

met.PerformFeatureFilter(): Used by higher functions for non-specific filtering of variables

met.PreparePrenormData(): Prepare data for normalization

met.SanityCheck(): Perform sanity check on metabolomics data

met.Ttests.Anal(): Perform T-test analysis

met.UpdateData(): Remove defined samples and features from the metabolomics dataset.

met.impute(): Impute missing variables

met.initialize(): Constructs a dataSet object for storing metabolomics data

met.normalize(): Data normalization

met.plot_ANOVA(): Visualize p values determined by ANOVA analysis.

met.plot_CorrHeatMap_Features(): Pattern hunter, plot correlation heatmap

met.plot_CorrHeatMap_Samples(): Pattern hunter, plot correlation heatmap

met.plot_FeatureNormSummary(): Visualize effect of normalization on feature density distributions.

met.plot_ImpVar(): Plot PLS important features

met.plot_PCA2DLoading(): Plot PCA loadings and also set up the matrix for display

met.plot_PCA2DScore(): Create 2D PCA score plot

met.plot_PCA3DLoading(): Create 3D PCA loading plot

met.plot_PCA3DScore(): Create 3D PCA Score plot

met.plot_PCAScree(): Plot PCA scree plot

met.plot_PLS.Crossvalidation(): Plot PLS-DA classification performance using different components

met.plot_PLS.Permutation(): Plot PLS-DA classification performance using different components, permutation

met.plot_PLS2DLoading(): Plot PLS-DA loadings and also set up the matrix for display

met.plot_PLS2DScore(): Create 2D PLS-DA score plot

met.plot_PLS3DLoading(): Create 3D PLS-DA loading plot

met.plot_PLS3DScore(): Create 3D PLS-DA Score plot

met.plot_PLSImpScatter(): Plot PLS important features

met.plot_PLS_Imp(): Plot PLS important features

met.plot_SampleNormSummary(): Visualize effect of normalization on sample density distributions.

met.plot_detect(): Visualize intensities of compounds with missing values

met.plot_heatmap(): Create heat map of hierarchically clustered features and samples

met.plot_missval(): Plot a heatmap of compounds with missing values

met.plot_volcano(): Volcano Plot

met.print_PCA3DLoading(): Plot a generated 3D PCA loading plot in RStudio

met.print_PCA3DScore(): Plot a generated 3D PCA scores plot in RStudio

met.print_PLS3DLoading(): Plot a generated 3D PLS-DA loading plot in RStudio

met.print_PLS3DScore(): Plot a generated 3D PLS-DA scores plot in RStudio

met.read_data(): Construct mSet data container, read metabolomics data, filter data, and impute missing values

met.report(): Generate a markdown report for metabolomics data analysis

met.report_test_normalization(): Generate a markdown report for the screening of metabolomics data pre-processing methods.

met.test_normalization(): Test for optimal data processing conditions

met.workflow(): Perform metabolomics data analysis workflow

pathway_enrich(): Pathway Enrichment Analysis

plot_coverage(): Plot coverage of a SummarizedExperiment

png_to_gif(): Convert PNGs to an animated GIF

prot.add_rejections(): Add rejections to a SummarizedExperiment

prot.boxplot_intensity(): Boxplot Intensity

prot.filter_missing(): Filter proteins based on missing values

prot.get_kegg_pathways(): Multiple set version of union

prot.get_results(): Get Results

prot.impute(): Impute missing values in a SummarizedExperiment object

prot.make_unique(): Make Unique Proteins

prot.normalize_vsn(): Normalize the data via variance stabilization normalization

prot.pca(): PCA Analysis

prot.plot_bar(): Plot bar plots for protein abundance or fold change

prot.plot_corrheatmap(): Plot a correlation heatmap

prot.plot_density(): Generates density plots for a SummarizedExperiment object

prot.plot_detect(): Plot detectability for a SummarizedExperiment

prot.plot_enrichment(): Plot pathway enrichment

prot.plot_heatmap(): Plots a heatmap of the differentially expressed proteins

prot.plot_heatmap_all(): Plots a heatmap of all protein abundances

prot.plot_imputation(): Plot imputation results

prot.plot_loadings(): Plot the loadings of a principal components analysis

prot.plot_missval(): Plot missing values in SummarizedExperiment

prot.plot_numbers(): Plot the number of proteins identified in a SummarizedExperiment object

prot.plot_pca(): Plot Results of Principal Component Analysis

prot.plot_screeplot(): SCREE plot

prot.plot_upset(): Plot an UpSet enrichment plot

prot.plot_volcano(): Plots a volcano plot for a given contrast

prot.read_data(): Read proteomics data in table format and create SummarizedExperiment

prot.report(): Report Proteomics Data

prot.test_diff(): Test Differential Expression

prot.workflow(): Run a complete proteomics analysis workflow.

prot_to_map(): Title

read_file(): Call the appropriate function required to read a table file and return the table as a dataframe object.

read_flux(): Reads flux data from a file or data frame

rna.filter_missing(): Filter genes based on missing values

rna.impute(): Impute missing values in a SummarizedExperiment object

rna.make_se(): Create a SummarizedExperiment object

rna.plot_bar(): Plot bar plots for gene abundance or fold change

rna.plot_corrheatmap(): Plot a correlation heatmap

rna.plot_heatmap(): Plots a heatmap of the differentially expressed genes

rna.plot_imputation(): Plot imputation results

rna.plot_numbers(): Plot the number of genes identified in a DESeqDataSet object

rna.plot_pca(): Plot Results of Principal Component Analysis

rna.plot_volcano(): Plots a volcano plot for a given contrast

rna.read_data(): Read Transcriptomics Data

rna.report(): Generate a transcriptomics analysis report

rna.workflow(): RNA sequencing workflow

svg_to_pdf(): Convert SVG to PDF

theme_DEP1(): theme_DEP1

theme_DEP2(): Theme DEP2