Introduction | Installation | An example run on published Golub data | Session
Package: pvca 1.53.0
Jianying LI
pvca: Principal Variance Component Analysis (PVCA)
This package contains the function to assess the batch sourcs by fitting all "sources" as random effects including two-way interaction terms in the Mixed Model(depends on lme4 package) to selected principal components, which were obtained from the original data correlation matrix. This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12.
Authors:
pvca_1.53.0.tar.gz
pvca_1.53.0.zip(r-4.7)pvca_1.53.0.zip(r-4.6)pvca_1.53.0.zip(r-4.5)
pvca_1.53.0.tgz(r-4.6-any)pvca_1.53.0.tgz(r-4.5-any)
pvca_1.53.0.tar.gz(r-4.7-any)pvca_1.53.0.tar.gz(r-4.6-any)
pvca_1.53.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
pvca/json (API)
| # Install 'pvca' in R: |
| install.packages('pvca', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
On BioConductor:pvca-1.53.0(bioc 3.24)pvca-1.52.0(bioc 3.23)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:4399c6ea71. Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | ERROR | 145 | ||
| linux-devel-x86_64 | NOTE | 236 | ||
| source / vignettes | OK | 238 | ||
| linux-release-x86_64 | NOTE | 204 | ||
| macos-release-arm64 | NOTE | 108 | ||
| macos-oldrel-arm64 | NOTE | 106 | ||
| windows-devel | NOTE | 109 | ||
| windows-release | NOTE | 121 | ||
| windows-oldrel | NOTE | 121 | ||
| wasm-release | OK | 149 |
Exports:pvcaBatchAssess
Dependencies:affyaffyioBiobaseBiocGenericsBiocManagerbootclicpp11farvergenericsggplot2gluegtableisobandlabelinglatticelifecyclelimmalme4MASSMatrixminqanlmenloptrpreprocessCoreR6rbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangS7scalesstatmodvctrsviridisLitevsnwithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| A package that provides an approach to assess the source of batch effects in a microarray gene expression experiment | pvca-package pvca |
| Principal Variance Component Analysis (PVCA) | pvcaBatchAssess |