Analyses of high-throughput data from heterogeneous samples with TOAST

TOAST is an R package designed for the analyses of high-throughput data from complex, heterogeneous tissues. It is designed for the analyses of high-throughput data from heterogeneous tissues, which is a mixture of different cell types.

TOAST offers functions for detecting cell-type specific differential expression (csDE) or differential methylation (csDM) for microarray data, and improving reference-free deconvolution based on cross-cell type differential analysis. TOAST implements a rigorous staitstical framework, based on linear model, which provides great flexibility for csDE/csDM detection and superior computationl performance.

In this readme file, we briefly present how to install TOAST package through GitHub. For detailed usage of TOAST, please refer to the vignette file.

Installation and quick start

Install TOAST

To install this package, start R (version "3.6") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("TOAST") 

To view the package vignette in HTML format, run the following lines in R

library(TOAST)
vignette("TOAST")

The content in this README file is essentially the same as the package vignette.

How to get help for TOAST

Any TOAST questions should be posted to the GitHub Issue section of TOAST homepage at https://github.com/ziyili20/TOAST/issues.

Quick start on detecting cell type-specific differential signals

Here we show the key steps for a cell type-specific different analysis. This code chunk assumes you have an expression or DNA methylation matrix called Y_raw, a data frame of sample information called design, and a table of cellular composition information (i.e. mixing proportions) called prop. Instead of a data matrix, Y_raw could also be a SummarizedExperiment object. If the cellular composition is not available, our vignette file provides discussions about how to obtain mixing proportions using reference-free deconvolution or reference-based deconvolution.

Design_out <- makeDesign(design, Prop)
fitted_model <- fitModel(Design_out, Y_raw)
fitted_model$all_coefs # list all phenotype names
fitted_model$all_cell_types # list all cell type names
# coef should be one of above listed phenotypes
# cell_type should be one of above listed cell types
res_table <- csTest(fitted_model, coef = "age", 
                    cell_type = "Neuron", contrast_matrix = NULL)
head(res_table)

For detailed usage of TOAST, please refer to the vignette file through

vignette("TOAST")
# or
browseVignettes("TOAST")