Package: TOAST 1.21.0

Ziyi Li

TOAST: Tools for the analysis of heterogeneous tissues

This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge.

Authors:Ziyi Li and Weiwei Zhang and Luxiao Chen and Hao Wu

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NEWS

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

Peer review:

Bug tracker:https://github.com/ziyili20/toast/issues

Datasets:
  • CBS_PBMC_array - An example dataset for partial reference-free cell composition estimation from tissue gene expression
  • RA_100samples - An example dataset for cellular proportion estimation and multiple factor design
  • beta_emp - Simulated methylation 450K array data with related

On BioConductor:TOAST-1.21.0(bioc 3.21)TOAST-1.20.0(bioc 3.20)

dnamethylationgeneexpressiondifferentialexpressiondifferentialmethylationmicroarraygenetargetepigeneticsmethylationarray

7.98 score 10 stars 3 packages 105 scripts 784 downloads 174 mentions 16 exports 84 dependencies

Last updated 2 months agofrom:3fa4c263bd. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 18 2024
R-4.5-winOKDec 18 2024
R-4.5-linuxOKDec 18 2024
R-4.4-winOKDec 18 2024
R-4.4-macOKDec 18 2024
R-4.3-winOKDec 18 2024
R-4.3-macOKDec 18 2024

Exports:assignCellTypecedarChooseMarkercsDeconvcsTestDEVarSelectfindRefinxfitModelGetPriormakeDesignMDeconvmyprojectMixmyRefFreeCellMixmyRefFreeCellMixInitializeplotCorrTsisal

Dependencies:abindaskpassBiobaseBiocGenericsclassclicodetoolscolorspacecorpcorcpp11crayoncurlDelayedArraydoParalleldplyre1071EpiDISHfansifarverforcatsforeachgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesGGallyggplot2ggstatsgluegtablehmshttrIRangesisobanditeratorsjsonlitelabelinglatticelifecyclelimmalocfdrmagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemunsellnlmennlsopensslpatchworkpillarpkgconfigplyrprettyunitsprogressproxypurrrquadprogR6RColorBrewerRcpprlangS4ArraysS4VectorsscalesSparseArraystatmodstringistringrSummarizedExperimentsystibbletidyrtidyselectUCSC.utilsutf8vctrsviridisLitewithrXVectorzlibbioc

Analyses of high-throughput data from heterogeneous samples with TOAST

Rendered fromTOAST.Rmdusingknitr::rmarkdownon Dec 18 2024.

Last update: 2022-08-14
Started: 2019-05-21

Readme and manuals

Help Manual

Help pageTopics
Align cell types when reference proportions are knownassignCellType
Simulated methylation 450K array data with relatedbeta_emp
An example dataset for partial reference-free cell composition estimation from tissue gene expressionCBS_PBMC_array
Testing cell type specific differential signals for specified phenotype by considering DE/DM state corrleation between cell types.cedar
Choose cell type-specific markers from pure cell type profiles or single cell dataChooseMarker
Improve reference-free deconvolution using cross-cell type differential analysiscsDeconv
Testing differential signals for specified phenotype and cell type(s).csTest
Feature selection for reference-free deconvolution using cross-cell type differential analysisDEVarSelect
findRefinxfindRefinx
Fit model with proportions and phenotypes.fitModel
Get prior knowledge for supported tissue typesGetPrior
Generate design matrix from input phenotypes and proportions.makeDesign
Estimate cell compoisitons via partial reference-free deconvolution.MDeconv
Replicate the function myprojectMix() from RefFreeEWAS packagemyprojectMix
Replicate the function RefFreeCellMix() from RefFreeEWAS packagemyRefFreeCellMix
Replicate the function RefFreeCellMixInitialize() from RefFreeEWAS packagemyRefFreeCellMixInitialize
Show DE/DM state correlation between cell typesplotCorr
An example dataset for cellular proportion estimation and multiple factor designRA_100samples
Complete Deconvolution of DNA methylation data based on TOAST and SISALTsisal