Package: MultiRNAflow 1.3.0

Rodolphe Loubaton

MultiRNAflow: An R package for integrated analysis of temporal RNA-seq data with multiple biological conditions

Our R package MultiRNAflow provides an easy to use unified framework allowing to automatically make both unsupervised and supervised (DE) analysis for datasets with an arbitrary number of biological conditions and time points. In particular, our code makes a deep downstream analysis of DE information, e.g. identifying temporal patterns across biological conditions and DE genes which are specific to a biological condition for each time.

Authors:Rodolphe Loubaton [aut, cre], Nicolas Champagnat [aut, ths], Laurent Vallat [aut, ths], Pierre Vallois [aut], Région Grand Est [fnd], Cancéropôle Est [fnd]

MultiRNAflow_1.3.0.tar.gz
MultiRNAflow_1.3.0.zip(r-4.5)MultiRNAflow_1.3.0.zip(r-4.4)MultiRNAflow_1.3.0.zip(r-4.3)
MultiRNAflow_1.3.0.tgz(r-4.4-any)MultiRNAflow_1.3.0.tgz(r-4.3-any)
MultiRNAflow_1.3.0.tar.gz(r-4.5-noble)MultiRNAflow_1.3.0.tar.gz(r-4.4-noble)
MultiRNAflow_1.3.0.tgz(r-4.4-emscripten)MultiRNAflow_1.3.0.tgz(r-4.3-emscripten)
MultiRNAflow.pdf |MultiRNAflow.html✨
MultiRNAflow/json (API)
NEWS

# Install 'MultiRNAflow' in R:
install.packages('MultiRNAflow', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/loubator/multirnaflow/issues

Datasets:

On BioConductor:MultiRNAflow-1.3.0(bioc 3.20)MultiRNAflow-1.2.0(bioc 3.19)

bioconductor-package

32 exports 1 stars 0.82 score 171 dependencies

Last updated 2 months agofrom:850819af5b

Exports:CharacterNumbersColnamesToFactorsDATAnormalizationDATAplotBoxplotSamplesDATAplotExpression1GeneDATAplotExpressionGenesDATAprepSEDEanalysisGlobalDEanalysisGroupDEanalysisSubDataDEanalysisTimeDEanalysisTimeAndGroupDEplotAlluvialDEplotBarplotDEplotBarplotFacetGridDEplotBarplotTimeDEplotHeatmapsDEplotVennBarplotGroupDEplotVennBarplotTimeDEplotVolcanoMADEresultGroupDEresultGroupPerTimeGSEApreprocessingGSEAQuickAnalysisHCPCanalysisMFUZZanalysisMFUZZclustersNumberPCAanalysisPCAgraphicsPCApreprocessingPCArealizationRawCountsSimulation

Dependencies:abindaskpassbackportsbase64encBHBiobaseBiocGenericsBiocParallelbitopsbootbroombslibcachemcarcarDatacirclizeclasscliclueclustercodetoolscolorspaceComplexHeatmapcorrplotcowplotcpp11crayoncrosstalkcurldata.tableDelayedArraydendextendDESeq2digestdoParalleldplyrDTDynDoce1071ellipseemmeansestimabilityevaluatefactoextraFactoMineRfansifarverfastmapflashClustfontawesomeforeachformatRfsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesGetoptLongggalluvialggplot2ggplotifyggpubrggrepelggsciggsignifGlobalOptionsgluegprofiler2gridExtragridGraphicsgtablehighrhtmltoolshtmlwidgetshttpuvhttrIRangesisobanditeratorsjquerylibjsonliteknitrlabelinglambda.rlaterlatticelazyevalleapslifecyclelme4locfitmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoiseMfuzzmgcvmimeminqamisc3dmultcompViewmunsellmvtnormnlmenloptrnnetnumDerivopensslpbkrtestpillarpkgconfigplot3Dplot3DrglplotlyplyrpngpolynompromisesproxypurrrquantregR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRCurlreshape2rglrjsonrlangrmarkdownrstatixS4ArraysS4Vectorssassscalesscatterplot3dshapesnowSparseArraySparseMstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttinytextkWidgetsUCSC.utilsUpSetRutf8vctrsviridisviridisLitewidgetToolswithrxfunXVectoryamlyulab.utilszlibbioc

MultiRNAflow: A R package for analysing RNA-seq raw counts with different time points and several biological conditions.

Rendered fromMultiRNAflow_vignette-knitr.Rnwusingknitr::knitron Jun 26 2024.

Last update: 2024-04-10
Started: 2023-04-01

Running MultiRNAflow on a RNA-seq raw counts with different time points and several biological conditions

Rodolphe Loubaton, Nicolas Champagnat, Pierre Vallois, Laurent Vallat

Rendered fromRunning_analysis_with_MultiRNAflow.Rmdusingknitr::rmarkdownon Jun 26 2024.

Last update: 2024-04-10
Started: 2023-02-15

Readme and manuals

Help Manual

Help pageTopics
Transformation of a vector of integers into a vector of class "character".CharacterNumbers
Extraction of factors information and suitable column names creation from the column names of a dataset.ColnamesToFactors
Normalization of raw counts (Main Function).DATAnormalization
Visualization of the distribution of all gene expressions using a boxplot for each sample.DATAplotBoxplotSamples
Plot expression of one gene.DATAplotExpression1Gene
Plot expression of a subset of genes.DATAplotExpressionGenes
Data preparation for exploratory and statistical analysis (Main Function)DATAprepSE
Realization of the DE analysis (Main Function).DEanalysisGlobal
DE Analysis when samples belong to different biological conditions.DEanalysisGroup
Sub data of a data.frameDEanalysisSubData
DE analysis when samples belong to different time points only.DEanalysisTime
DE analysis when samples belong to different biological condition and time points.DEanalysisTimeAndGroup
Alluvial graphs of differentially expressed (DE) genesDEplotAlluvial
Barplot of DE genes from a contingency table.DEplotBarplot
Faceted barplot of specific DE genesDEplotBarplotFacetGrid
Barplot of DE genes per timeDEplotBarplotTime
Heatmaps of DE genesDEplotHeatmaps
Venn barplot of DE genes across pairs of biological conditions.DEplotVennBarplotGroup
Venn barplot of DE genes across time.DEplotVennBarplotTime
Volcano and MA graphsDEplotVolcanoMA
Intermediate analysis when samples belong to different biological conditionsDEresultGroup
Intermediate analysis when samples belong to different biological conditions and different time points.DEresultGroupPerTime
GSEA preprocessing for official software and online tools.GSEApreprocessing
GSEA analysis with gprofiler2GSEAQuickAnalysis
Hierarchical clustering analysis with HCPC (Main function)HCPCanalysis
Clustering of temporal patterns (Main function).MFUZZanalysis
Automatic choice of the number of clusters to use for the Mfuzz analysisMFUZZclustersNumber
Automatic PCA analysis (Main function)PCAanalysis
Multiple 2D and 3D PCA graphs.PCAgraphics
Reshaped dataset for factorial analysis.PCApreprocessing
PCA realizationPCArealization
Mouse raw counts dataRawCounts_Antoszewski2022_MOUSEsub500
Yeast times series raw counts data after stimulation with and without silencingRawCounts_Leong2014_FISSIONsub500wt
Human CCL times series raw counts data after stimulation with and without silencingRawCounts_Schleiss2021_CLLsub500
Mouse count data with four biological conditions, six time measurements and 500 genes.RawCounts_Weger2021_MOUSEsub500
RNA-seq raw counts data simulationRawCountsSimulation
DE results of three datasetResults_DEanalysis_sub500
Homo sapiens transcript databaseTranscript_HomoSapiens_Database