README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# voomCLR

`voomCLR` allows effective differential cell composition analysis in cell type count data.
It leverages compositional transformations, and adopts bias correction on the estimated effect sizes to correct for compositional bias induced by such transformation. The uncertainty involved in estimating the bias can be propagated in the statistical inference via bootstrapping. Additionally, it accommodates proper modeling of the mean-variance structure of the counts. 

`voomCLR` relies on the `limma` package, and in fact re-uses code chunks from the `limma` R package, which is available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/limma.html.

## Installation

Install the development version from GitHub using

```{r 'install_dev', eval = FALSE}
devtools::install_github("koenvandenberge/voomCLR")
```

# Getting started

The vignette of voomCLR walks you through the basics of using `voomCLR`.


# Analysis code of the paper

All analysis code of the paper can be found at https://github.com/koenvandenberge/voomCLRPaper.