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---
title: "Tweedieverse: Differential analysis of omics data based on the Tweedie distribution"
author: "Himel Mallick, Ali Rahnavard"
date: "`r Sys.Date()`"
output: 
  rmarkdown::github_document:
    toc: yes
    toc_depth: 4
vignette: >
  %\VignetteIndexEntry{Tweedieverse : Differential analysis of omics data based on the Tweedie distribution}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

<!-- Himel Mallick -->
<!-- 2021-03-03 <img src="docs/logo.jpg" align="right" width="365px"/> -->

## Introduction 

Tweedieverse is an R package for differential analysis of omics data implementing a range of statistical methodology based on the [Tweedie distribution](https://en.wikipedia.org/wiki/Tweedie_distribution).

Unlike traditional single-omics tools, Tweedieverse is technology-agnostic and can be applied to both count and continuous measurements arising from diverse high-throughput technologies (e.g., transcript abundances from bulk and single-cell RNA-Seq studies in the form of UMI counts or non-UMI counts, microbiome taxonomic and functional profiles in the form of counts or relative abundances, and compound abundance levels or peak intensities from metabolomics and other mass spectrometry-based experiments, among others).

The software includes multiple analysis methods (e.g., self-adaptive, zero-inflated, and non-zero-inflated statistical models) as well as multiple customization options such as the inclusion of random effects and multiple covariates along with several data exploration capabilities and visualization modules in a unified estimation umbrella.

## Installation

To install the latest release version of `Tweedieverse` from [CRAN](https://cran.r-project.org/) (_*not yet available*_) run the following command:

```r
install.packages("Tweedieverse")
library(Tweedieverse)
```
Alternatively, the latest development version of `Tweedieverse` can be loaded using the following command (execute from within a fresh R session):

```r
install.packages('devtools')
library(devtools)
devtools::install_github("himelmallick/Tweedieverse")
library(Tweedieverse)
```

After installing `Tweedieverse`, please make sure the following package versions are also installed (a prerequisite for zero-inflated Tweedie models):

```R
devtools::install_version("statmod", version = "1.4.33", repos ="http://cran.us.r-project.org")

```
```R
devtools::install_version("cplm", version = "0.7-8", repos = "http://cran.us.r-project.org")
```

## Basic Usage

```r
Tweedieverse(features, metadata, output)
```

## Input

Tweedieverse requires two input files:

- **features**: A data frame of omics features such as taxa, genes, transcripts, metabolites, etc.
- **metadata**: A data frame of metadata to be associated. 

For full options, check out the [user manual](https://github.com/himelmallick/Tweedieverse/tree/master/vignettes) or type  ``` ?Tweedieverse``` in your R console.

## Output

A data frame containing coefficient estimates, p-values, and q-values (multiplicity-adjusted p-values) are returned, along with other parameter estimates from the fitted per-feature models.  

## Getting Started with Tweedieverse

Check out the [Tweedie Labs](https://github.com/himelmallick/TweedieLabs/) repository for a collection of walkthrough tutorials (available as source codes, cloud-compatible images, and installable packages) on how to use Tweedieverse with various omics data types.

## Citation

To cite **`Tweedieverse`** in publications, please use:

Mallick H et al. (2021). [Differential Expression of Single-cell RNA-seq Data using Tweedie Models](https://www.biorxiv.org/content/10.1101/2021.03.28.437378v1). bioRxiv, https://doi.org/10.1101/2021.03.28.437378.

To cite the **`Tweedieverse`** software, please use:

Mallick H et al. (2021). [Tweedieverse - A Unified Statistical Framework for Differential Analysis of Multi-omics Data](https://github.com/himelmallick/Tweedieverse). R package, https://github.com/himelmallick/Tweedieverse.

## Issues

We are happy to troubleshoot any issues with the package. Please contact the maintainer via email or [open an issue](https://github.com/himelmallick/tweedieverse/issues) in the GitHub repository.