Package: SeqGate 1.17.0

Stéphanie Rialle

SeqGate: Filtering of Lowly Expressed Features

Filtering of lowly expressed features (e.g. genes) is a common step before performing statistical analysis, but an arbitrary threshold is generally chosen. SeqGate implements a method that rationalize this step by the analysis of the distibution of counts in replicate samples. The gate is the threshold above which sequenced features can be considered as confidently quantified.

Authors:Christelle Reynès [aut], Stéphanie Rialle [aut, cre]

SeqGate_1.17.0.tar.gz
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SeqGate.pdf |SeqGate.html✨
SeqGate/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/srialle/seqgate/issues

Datasets:

On BioConductor:SeqGate-1.17.0(bioc 3.21)SeqGate-1.16.0(bioc 3.20)

differentialexpressiongeneexpressiontranscriptomicssequencingrnaseq

3.30 score 3 scripts 184 downloads 1 exports 30 dependencies

Last updated 2 months agofrom:9990f13d6c. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 30 2024
R-4.5-winOKNov 30 2024
R-4.5-linuxOKNov 30 2024
R-4.4-winOKNov 30 2024
R-4.4-macOKNov 30 2024
R-4.3-winOKNov 30 2024
R-4.3-macOKNov 30 2024

Exports:applySeqGate

Dependencies:abindaskpassBiobaseBiocGenericsBiocManagercrayoncurlDelayedArraygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangeshttrIRangesjsonlitelatticeMatrixMatrixGenericsmatrixStatsmimeopensslR6S4ArraysS4VectorsSparseArraySummarizedExperimentsysUCSC.utilsXVectorzlibbioc

SeqGate: Filter lowly expressed features

Christelle Reynès, Stéphanie Rialle

Rendered fromSeqgate-html-vignette.Rmdusingknitr::rmarkdownon Nov 30 2024.

Last update: 2021-01-19
Started: 2020-07-10

Readme and manuals

Help Manual

Help pageTopics
Filtering of Lowly Expressed FeaturesSeqGate-package SeqGate
Performs a Filtering of Lowly Expressed FeaturesapplySeqGate seqGate seqgate
Extract of a Transciptome Dataset from Human Melanoma Cell Linedata_MITF_1000genes data_MiTF_1000genes data_mitf_1000genes