Package: MatrixQCvis 1.13.5

Thomas Naake

MatrixQCvis: Shiny-based interactive data-quality exploration for omics data

Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object.

Authors:Thomas Naake [aut, cre], Wolfgang Huber [aut]

MatrixQCvis_1.13.5.tar.gz
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MatrixQCvis.pdf |MatrixQCvis.html✨
MatrixQCvis/json (API)
NEWS

# Install 'MatrixQCvis' in R:

install.packages('MatrixQCvis', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

On BioConductor:MatrixQCvis-1.13.4(bioc 3.20)MatrixQCvis-1.12.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

39 exports 1.31 score 179 dependencies

Last updated 13 days agofrom:465423b5d1

Exports:barplotSamplesMeasuredMissing batchCorrectionAssay createBoxplot createDfFeature cv cvFeaturePlot dimensionReduction dimensionReductionPlot distSample distShiny driftPlot ECDF explVar extractComb featurePlot hist_sample hist_sample_num histFeature histFeatureCategory hoeffDPlot hoeffDValues imputeAssay MAplot MAvalues measuredCategory mosaic normalizeAssay permuteExplVar plotCV plotPCALoadings plotPCAVar plotPCAVarPvalue samplesMeasuredMissing shinyQC sumDistSample tblPCALoadings transformAssay upsetCategory volcanoPlot

Dependencies:abind affy affyio annotate AnnotationDbi AnnotationHub askpass backports base64enc BH Biobase BiocFileCache BiocGenerics BiocManager BiocParallel BiocVersion Biostrings bit bit64 blob bslib cachem checkmate circlize cli clue cluster codetools colorspace commonmark ComplexHeatmap cpp11 crayon crosstalk curl data.table DBI dbplyr DelayedArray digest doParallel dplyr DT edgeR evaluate ExperimentHub extraDistr fansi farver fastmap filelock fontawesome foreach foreign formatR Formula fs futile.logger futile.options genefilter generics GenomeInfoDb GenomeInfoDbData GenomicRanges GetoptLong ggplot2 GlobalOptions glue gmm gridExtra gtable here highr Hmisc htmlTable htmltools htmlwidgets httpuv httr impute imputeLCMD IRanges isoband iterators jquerylib jsonlite KEGGREST knitr labeling lambda.r later lattice lazyeval lifecycle limma locfit magrittr markdown MASS Matrix MatrixGenerics matrixStats memoise mgcv mime munsell mvtnorm nlme nnet norm openssl pcaMethods pillar pkgconfig plogr plotly plyr png preprocessCore proDA promises purrr R6 rappdirs RColorBrewer Rcpp RcppEigen RcppTOML reticulate rjson rlang rmarkdown rpart rprojroot RSpectra RSQLite rstudioapi Rtsne S4Arrays S4Vectors sandwich sass scales shape shiny shinydashboard shinyhelper shinyjs snow sourcetools SparseArray statmod stringi stringr SummarizedExperiment survival sva sys tibble tidyr tidyselect tinytex tmvtnorm UCSC.utils umap UpSetR utf8 vctrs viridis viridisLite vsn withr xfun XML xtable XVector yaml zlibbioc zoo

MatrixQCvis: shiny-based interactive data quality exploration of omics data

Thomas Naake and Wolfgang Huber

Rendered fromMatrixQCvis.Rmdusingknitr::rmarkdownon Jun 28 2024.

Last update: 2024-06-27
Started: 2021-03-11

Help page	Topics
Barplot of number of measured/missing features of samples	barplotSamplesMeasuredMissing
Remove batch effects from (count/intensity) values of a 'SummarizedExperiment'	batchCorrectionAssay
Create a boxplot of (count/intensity) values per sample	createBoxplot
Create data frame of (count/intensity) values for a selected feature along data processing steps	createDfFeature
Calculate coefficient of variation	cv
Plot of feature-wise coefficient of variation values	cvFeaturePlot
Dimensionality reduction with dimensionReduction methods PCA, PCoA, NMDS, UMAP and tSNE	dimensionReduction
Plot the coordinates from 'dimensionReduction' values	dimensionReductionPlot
Create a heatmap using distance information between samples	distSample
Create distance matrix from numerical matrix	distShiny
Plot the trend line for aggregated values	driftPlot
Create ECDF plot of a sample against a reference	ECDF
Retrieve the explained variance for each principal component (PCA) or axis (PCoA)	explVar
Obtain the features that are present in a specified set	extractComb
Create a plot of (count/intensity) values over the samples	featurePlot
Plot a histogram of the number of a category	hist_sample
Return the number of a category	hist_sample_num
Histogram for measured value per feature	histFeature
Histogram of features per sample type	histFeatureCategory
Create a plot from a list of Hoeffding's D values	hoeffDPlot
Create values of Hoeffding's D statistics from M and A values	hoeffDValues
Impute missing values in a 'matrix'	imputeAssay
Create a MA plot	MAplot
Create values (M and A) for MA plot	MAvalues
Obtain the number of measured intensities per sample type	measuredCategory
Mosaic plot for two factors in colData(se)	mosaic
Normalize a data sets (reduce technical sample effects)	normalizeAssay
Permute the expression values and retrieve the explained variance	permuteExplVar
Plot CV values	plotCV
Plot for PCA loadings of features	plotPCALoadings
Plot of explained variance against the principal components	plotPCAVar
Plot p-values for the significance of principal components	plotPCAVarPvalue
Create tibble containing number of measured/missing features of samples	samplesMeasuredMissing
Shiny application for initial QC exploration of -omics data sets	shinyQC
Plot the sum of distances to other samples	sumDistSample
Return tibble with PCA loadings for features	tblPCALoadings
Transform the (count/intensity) values of a 'data.frame', 'tbl' or 'matrix'	transformAssay
UpSet plot to display measures values across sample types	upsetCategory
Volcano plot of fold changes/differences against p-values	volcanoPlot

Package: MatrixQCvis 1.13.5

MatrixQCvis: Shiny-based interactive data-quality exploration for omics data

MatrixQCvis: shiny-based interactive data quality exploration of omics data

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