# 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:
```{r install, message=FALSE, warning=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("TOAST")
```
To view the package vignette in HTML format, run the following lines in R
```{r vig, message=FALSE, warning=FALSE}
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.
```{r quick_start, eval = FALSE}
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**
```{r vignette}
vignette("TOAST")
# or
browseVignettes("TOAST")
```
