Package: NormalyzerDE 1.30.0

Jakob Willforss

NormalyzerDE: Evaluation of normalization methods and calculation of differential expression analysis statistics

NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis.

Authors:Jakob Willforss

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
NormalyzerDE/json (API)

# Install 'NormalyzerDE' in R:

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

Bug tracker:https://github.com/computationalproteomics/normalyzerde/issues

Pkgdown/docs site:https://computationalproteomics.github.io

On BioConductor:NormalyzerDE-1.31.0(bioc 3.24)NormalyzerDE-1.30.0(bioc 3.23)

normalization multiplecomparison visualization bayesian proteomics metabolomics differentialexpression bioconductor bioinformatics limma

7.94 score 26 stars 1 packages 612 downloads 8 mentions 28 exports 96 dependencies

Last updated from:a701c9979c (on RELEASE_3_23). Checks:1 NOTE, 9 OK. Indexed: no.

Target	Result	Time
bioc-checks	NOTE	230
linux-devel	OK	394
source / vignettes	OK	357
linux-release	OK	326
macos-release	OK	292
macos-oldrel	OK	197
windows-devel	OK	243
windows-release	OK	223
windows-oldrel	OK	217
wasm-release	OK	167

Exports:analyzeNormalizations calculateContrasts generateAnnotatedMatrix generatePlots generateStatsReport getRTNormalizedMatrix getSmoothedRTNormalizedMatrix getVerifiedNormalyzerObject globalIntensityNormalization meanNormalization medianNormalization normalyzer normalyzerDE NormalyzerEvaluationResults NormalyzerResults NormalyzerStatistics normMethods performCyclicLoessNormalization performGlobalRLRNormalization performQuantileNormalization performSMADNormalization performVSNNormalization reduceTechnicalReplicates setupJobDir setupRawContrastObject setupRawDataObject setupTestData writeNormalizedDatasets

Dependencies:abind affy affyio ape backports base64enc Biobase BiocGenerics BiocManager boot broom car carData cli colorspace cowplot cpp11 DelayedArray Deriv digest doBy dplyr farver forecast Formula fracdiff generics GenomicRanges ggforce ggplot2 glue gtable IRanges isoband jsonlite labeling lattice lifecycle limma lme4 lmtest magrittr MASS Matrix MatrixGenerics MatrixModels matrixStats mgcv minqa modelr nlme nloptr nnet numDeriv pbkrtest pillar pkgconfig polyclip preprocessCore purrr quantreg R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas rlang S4Arrays S4Vectors S7 scales Seqinfo SparseArray SparseM statmod stringi stringr SummarizedExperiment survival systemfonts tibble tidyr tidyselect timeDate tweenr urca utf8 vctrs viridisLite vsn withr XVector zoo

Evaluation and statistics of expression data using NormalyzerDE

Rendered fromvignette.Rmdusingknitr::rmarkdown

Last update: 2023-09-11
Started: 2018-09-07

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Calculate measures for normalization results	analyzeNormalizations
Performs statistical comparisons between the supplied conditions. It uses the design matrix and data matrix in the supplied NormalyzerStatistics object. A column is supplied specifying which of the columns in the design matrix that is used for deciding the sample groups. The comparisons vector specifies which pairwise comparisons between condition levels that are to be calculated.	calculateContrasts calculateContrasts,NormalyzerStatistics-method
Generate an annotated data frame from statistics object	generateAnnotatedMatrix
Generates a number of visualizations for the performance measures calculated for the normalized matrices. These contain both general measures and direct comparisons for different normalization approaches.	generatePlots
Generate full output report plot document. Plots p-value histograms for each contrast in the NormalyzerStatistics instance and writes these to a PDF report.	generateStatsReport
Perform RT-segmented normalization by performing the supplied normalization over retention-time sliced data	getRTNormalizedMatrix
Generate multiple RT time-window normalized matrices where one is shifted. Merge them using a specified method (mean or median) and return the result.	getSmoothedRTNormalizedMatrix
Verify that input data is in correct format, and if so, return a generated NormalyzerDE data object from that input data	getVerifiedNormalyzerObject
The normalization divides the intensity of each variable in a sample with the sum of intensities of all variables in the sample and multiplies with the median of sum of intensities of all variables in all samples. The normalized data is then log2-transformed.	globalIntensityNormalization
Load raw data into dataframe	loadData
Load raw design into dataframe	loadDesign
Intensity of each variable in a given sample is divided by the mean of sum of intensities of all variables in the sample and then multiplied by the mean of sum of intensities of all variables in all samples. The normalized data is then transformed to log2.	meanNormalization
Intensity of each variable in a given sample is divided by the median of intensities of all variables in the sample and then multiplied by the mean of median of sum of intensities of all variables in all samples. The normalized data is then log2-transformed.	medianNormalization
NormalyzerDE pipeline entry point	normalyzer
NormalyzerDE differential expression	normalyzerDE
Representation of evaluation results by calculating performance measures for an an NormalyzerResults instance	NormalyzerEvaluationResults
Representation of the results from performing normalization over a dataset	NormalyzerResults
Class representing a dataset for statistical processing in NormalyzerDE	NormalyzerStatistics
Perform normalizations on Normalyzer dataset	normMethods
Cyclic Loess normalization	performCyclicLoessNormalization
Global linear regression normalization	performGlobalRLRNormalization
Quantile normalization is performed by the function "normalize.quantiles" from the package preprocessCore.	performQuantileNormalization
Median absolute deviation normalization Normalization subtracts the median and divides the data by the median absolute deviation (MAD).	performSMADNormalization
Log2 transformed data is normalized using the function "justvsn" from the VSN package.	performVSNNormalization
Remove technical replicates from data and design	reduceTechnicalReplicates
Create empty directory for run	setupJobDir
Prepare SummarizedExperiment object for statistics data	setupRawContrastObject
Prepare SummarizedExperiment object for raw data to be normalized containing data, design and annotation information	setupRawDataObject
Write normalization matrices to file	writeNormalizedDatasets