Package: rsemflow 0.1.1

rsemflow: Metadata-Driven Downstream Analysis for RSEM Gene-Level Results

A command-line and R interface for reproducible downstream analysis of RSEM gene-level expression estimates. 'rsemflow' imports '*.genes.results' files together with ordinary sample metadata, computes expression-level summaries, normalization, principal component analysis, sample correlation, 'DESeq2' differential expression, preranked gene set enrichment analysis, 'GSVA' or 'ssGSEA' pathway scores, and Ensembl gene annotation. Every result is written as a tab-separated table; the package does not draw figures.

Authors:Daiki Kumakura [aut, cre]

rsemflow_0.1.1.tar.gz
rsemflow_0.1.1.zip(r-4.7-any)rsemflow_0.1.1.zip(r-4.6-any)rsemflow_0.1.1.zip(r-4.5-any)
rsemflow_0.1.1.tgz(r-4.6-any)rsemflow_0.1.1.tgz(r-4.5-any)
rsemflow_0.1.1.tar.gz(r-4.7-any)rsemflow_0.1.1.tar.gz(r-4.6-any)
rsemflow_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rsemflow/json (API)

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

Bug tracker:https://github.com/daikikumakura/rsemflow/issues

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

On CRAN:

Conda:

rnaseqdifferentialexpressiongenesetenrichmenttranscriptomicsbioinformaticsdeseq2gsearna-seqrsem

3.60 score 1 stars 13 exports 110 dependencies

Last updated from:66f38bb6c8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK248
source / vignettesOK340
linux-releaseOK244
macos-releaseOK173
macos-oldrelOK213
windows-develOK178
windows-releaseOK168
windows-oldrelOK220
wasm-releaseOK199

Exports:annotate_ensemblannotate_gtfcli_maincompute_correlationcompute_pcadifferential_deseq2normalize_expressionread_rsem_studyread_studyrun_gsearun_gsvasummarize_studywrite_study

Dependencies:abindannotateAnnotationDbiaskpassassertthatbabelgeneBHBiobaseBiocBaseUtilsBiocFileCacheBiocGenericsbiocmakeBiocParallelBiostringsbitbit64blobcachemclicodetoolscowplotcpp11crayoncurldata.tableDBIdbplyrDelayedArrayDelayedMatrixStatsDESeq2dir.expirydplyrfarverfastmapfastmatchfgseafilelockformatRfutile.loggerfutile.optionsgenericsGenomicRangesggplot2gluegraphGSEABaseGSVAgtableh5mreadHDF5Arrayhttrhttr2IRangesisobandjsonliteKEGGRESTlabelinglambda.rlatticelifecyclelocfitmagickmagrittrMatrixMatrixGenericsmatrixStatsmemoisememusemimemsigdbropensslpillarpkgconfigpngpurrrR6RColorBrewerRcppRcppArmadillorhdf5rhdf5filtersRhdf5librjsonrlangRSQLiteS4ArraysS4VectorsS7scalesSeqinfoSingleCellExperimentsnowSparseArraysparseMatrixStatsSpatialExperimentstringistringrSummarizedExperimentsystibbletidyrtidyselecttximportutf8vctrsviridisLitewithrXMLxtableXVector

Differential-expression designs
Two groups | Adjusting for batch | Paired samples | More than two groups | A numeric covariate | Interaction | The effect within one group (simple effect) | Any effect of time (likelihood-ratio test) | What every run records | What rsemflow checks, and what it does not
Rendered fromdesigns.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-10-05

Get started with rsemflow
What you need | Three commands | The same analysis in R | How rsemflow treats your design | Next steps
Rendered fromrsemflow.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-10-05

Installation
Requirements | Option 1: install the R package from GitHub | Option 2: install from source with the command-line launcher | Running the command line on Windows | Check the installation | MSigDB gene sets | Troubleshooting
Rendered frominstallation.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-10-05

Tutorial: a complete command-line analysis
0. The input | 1. Import | 2. Expression summaries | 3. Sample structure: PCA and correlation | 4. Normalized expression | 5. Differential expression | 6. Preranked GSEA | 7. GSVA and ssGSEA scores | 8. Gene annotation | Everything that was written
Rendered fromtutorial.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-10-05