Package: TOAST 1.19.0

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'))

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-1.19.0(bioc 3.20)TOAST-1.18.0(bioc 3.19)

bioconductor-package

16 exports 1.31 score 95 dependencies 3 dependents 174 mentions

Last updated 2 months agofrom:81d1be10cd

Exports:assignCellType cedar ChooseMarker csDeconv csTest DEVarSelect findRefinx fitModel GetPrior makeDesign MDeconv myprojectMix myRefFreeCellMix myRefFreeCellMixInitialize plotCorr Tsisal

Dependencies:abind askpass backports Biobase BiocGenerics bit bit64 broom broom.helpers class cli clipr codetools colorspace corpcor cpp11 crayon curl DelayedArray doParallel dplyr e1071 EpiDISH fansi farver forcats foreach generics GenomeInfoDb GenomeInfoDbData GenomicRanges GGally ggplot2 ggstats glue gtable haven hms httr IRanges isoband iterators jsonlite labeling labelled lattice lifecycle limma locfdr magrittr MASS Matrix MatrixGenerics matrixStats mgcv mime munsell nlme nnls openssl patchwork pillar pkgconfig plyr prettyunits progress proxy purrr quadprog R6 RColorBrewer Rcpp readr rlang S4Arrays S4Vectors scales SparseArray statmod stringi stringr SummarizedExperiment sys tibble tidyr tidyselect tzdb UCSC.utils utf8 vctrs viridisLite vroom withr XVector zlibbioc

Analyses of high-throughput data from heterogeneous samples with TOAST

Ziyi Li, Hao Wu

Rendered fromTOAST.Rmdusingknitr::rmarkdownon Jun 30 2024.

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Align cell types when reference proportions are known	assignCellType
Simulated methylation 450K array data with related	beta_emp
An example dataset for partial reference-free cell composition estimation from tissue gene expression	CBS_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 data	ChooseMarker
Improve reference-free deconvolution using cross-cell type differential analysis	csDeconv
Testing differential signals for specified phenotype and cell type(s).	csTest
Feature selection for reference-free deconvolution using cross-cell type differential analysis	DEVarSelect
findRefinx	findRefinx
Fit model with proportions and phenotypes.	fitModel
Get prior knowledge for supported tissue types	GetPrior
Generate design matrix from input phenotypes and proportions.	makeDesign
Estimate cell compoisitons via partial reference-free deconvolution.	MDeconv
Replicate the function myprojectMix() from RefFreeEWAS package	myprojectMix
Replicate the function RefFreeCellMix() from RefFreeEWAS package	myRefFreeCellMix
Replicate the function RefFreeCellMixInitialize() from RefFreeEWAS package	myRefFreeCellMixInitialize
Show DE/DM state correlation between cell types	plotCorr
An example dataset for cellular proportion estimation and multiple factor design	RA_100samples
Complete Deconvolution of DNA methylation data based on TOAST and SISAL	Tsisal