Package: seqgendiff 1.2.4

seqgendiff: RNA-Seq Generation/Modification for Simulation

Generates/modifies RNA-seq data for use in simulations. We provide a suite of functions that will add a known amount of signal to a real RNA-seq dataset. The advantage of using this approach over simulating under a theoretical distribution is that common/annoying aspects of the data are more preserved, giving a more realistic evaluation of your method. The main functions are select_counts(), thin_diff(), thin_lib(), thin_gene(), thin_2group(), thin_all(), and effective_cor(). See Gerard (2020) <doi:10.1186/s12859-020-3450-9> for details on the implemented methods.

Authors:David Gerard [aut, cre]

seqgendiff_1.2.4.tar.gz
seqgendiff_1.2.4.zip(r-4.7)seqgendiff_1.2.4.zip(r-4.6)seqgendiff_1.2.4.zip(r-4.5)
seqgendiff_1.2.4.tgz(r-4.6-any)seqgendiff_1.2.4.tgz(r-4.5-any)
seqgendiff_1.2.4.tar.gz(r-4.7-any)seqgendiff_1.2.4.tar.gz(r-4.6-any)
seqgendiff_1.2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
seqgendiff/json (API)

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

Bug tracker:https://github.com/dcgerard/seqgendiff/issues

On CRAN:

Conda:

5.96 score 10 stars 92 scripts 201 downloads 1 mentions 13 exports 67 dependencies

Last updated from:9629ad951b. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK222
source / vignettesOK263
linux-release-x86_64OK189
macos-release-arm64OK156
macos-oldrel-arm64OK127
windows-develOK171
windows-releaseOK141
windows-oldrelOK131
wasm-releaseOK158

Exports:corassigneffective_corfix_corpoisthinselect_countsthin_2groupthin_allthin_basethin_diffthin_genethin_libThinDataToDESeqDataSetThinDataToSummarizedExperiment

Dependencies:annotateAnnotationDbiaskpassassertthatBHBiobaseBiocGenericsBiocParallelBiostringsbitbit64blobcachemcliclueclustercodetoolscpp11crayoncurlDBIedgeRfastmapformatRfutile.loggerfutile.optionsgenefiltergenericsgluehttrIRangesirlbajsonliteKEGGRESTlambda.rlatticelifecyclelimmalocfitmatchingRMatrixMatrixGenericsmatrixStatsmemoisemgcvmimenlmeopensslpdistpkgconfigpngR6RcppRcppArmadillorlangRSQLiteS4VectorsSeqinfosnowstatmodsurvivalsvasysvctrsXMLxtableXVector

Applying Different Thinning Functions
Abstract | Generate Data | Subsetting | Thinning Library Size | Total Thinning | General Thinning | Correlation with Surrogate Variables | Estimate Actual Correlation | Special Case of Two-group Model | References

Last update: 2022-02-19
Started: 2019-07-17

Simulate RNA-seq Data from Real Data

Last update: 2022-02-19
Started: 2019-05-23