Package: PLSDAbatch 1.1.0
Yiwen (Eva) Wang
PLSDAbatch: PLSDA-batch
A novel framework to correct for batch effects prior to any downstream analysis in microbiome data based on Projection to Latent Structures Discriminant Analysis. The main method is named “PLSDA-batch”. It first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data whilst preserving biological variation of interest. PLSDA-batch is highly suitable for microbiome data as it is non-parametric, multivariate and allows for ordination and data visualisation. Combined with centered log-ratio transformation for addressing uneven library sizes and compositional structure, PLSDA-batch addresses all characteristics of microbiome data that existing correction methods have ignored so far. Two other variants are proposed for 1/ unbalanced batch x treatment designs that are commonly encountered in studies with small sample sizes, and for 2/ selection of discriminative variables amongst treatment groups to avoid overfitting in classification problems. These two variants have widened the scope of applicability of PLSDA-batch to different data settings.
Authors:
PLSDAbatch_1.1.0.tar.gz
PLSDAbatch_1.1.0.zip(r-4.5)PLSDAbatch_1.1.0.zip(r-4.4)PLSDAbatch_1.1.0.zip(r-4.3)
PLSDAbatch_1.1.0.tgz(r-4.4-any)PLSDAbatch_1.1.0.tgz(r-4.3-any)
PLSDAbatch_1.1.0.tar.gz(r-4.5-noble)PLSDAbatch_1.1.0.tar.gz(r-4.4-noble)
PLSDAbatch_1.1.0.tgz(r-4.4-emscripten)PLSDAbatch_1.1.0.tgz(r-4.3-emscripten)
PLSDAbatch.pdf |PLSDAbatch.html✨
PLSDAbatch/json (API)
NEWS
| # Install 'PLSDAbatch' in R: |
| install.packages('PLSDAbatch', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/evayiwenwang/plsdabatch/issues
- AD_data - Anaerobic digestion study
- sponge_data - Sponge _A. aerophoba_ study
On BioConductor:PLSDAbatch-1.1.0(bioc 3.20)PLSDAbatch-1.0.0(bioc 3.19)
Last updated 2 months agofrom:cea9d4e12b
Exports:alignment_scorebox_plotdeflate_mtxdensity_plotlinear_regrespartVar_plotpb_colorpercentile_normpercentileofscorePLSDAPLSDA_batchPreFLScatter_Density
Dependencies:abindapeaskpassbackportsbase64encbayestestRBHBiobaseBiocGenericsBiocManagerBiocParallelBiocStyleBiostringsbookdownbootbroombslibcachemcarcarDatacliclustercodetoolscolorspacecorpcorcorrplotcowplotcpp11crayoncurldatawizardDelayedArraydigestdplyrellipseevaluatefansifarverfastmapfontawesomeformatRfsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehighrhtmltoolshttrigraphinsightIRangesisobandjquerylibjsonliteknitrlabelinglambda.rlatticelazyevallifecyclelme4lmerTestmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemgcvmimeminqamixOmicsmunsellnlmenloptrnnetnumDerivopensslpbkrtestperformancepermutepheatmappillarpkgconfigplyrpolynompurrrquantregR6rappdirsrARPACKrbibutilsRColorBrewerRcppRcppEigenRdpackreshape2rlangrmarkdownRSpectrarstatixS4ArraysS4VectorssassscalesSingleCellExperimentsnowSparseArraySparseMstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttidytreetinytextreeioTreeSummarizedExperimentUCSC.utilsutf8vctrsveganviridisLitewithrxfunXVectoryamlyulab.utilszlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Anaerobic digestion study | AD_data |
| Alignment Scores for Evaluating the Degree of Mixing Samples | alignment_score |
| Box Plot | box_plot |
| Matrix Deflation | deflate_mtx |
| Density Plot | density_plot |
| Linear Regression | linear_regres |
| Partitioned Variance Plot | partVar_plot |
| Color Palette for PLSDA-batch | pb_color |
| Percentile Normalisation | percentile_norm |
| Percentile score | percentileofscore |
| Partial Least Squares Discriminant Analysis | PLSDA |
| Partial Least Squares Discriminant Analysis for Batch Effect Correction | PLSDA_batch |
| Prefiltering for Microbiome Data | PreFL |
| Principal Component Analysis (PCA) with Density Plots per Component | Scatter_Density |
| Sponge _A. aerophoba_ study | sponge_data |
