Tweedieverse: Differential analysis of omics data based on the Tweedie distribution

================

Himel Mallick, Ali Rahnavard

2022-05-03 <img src="docs/logo.jpg" align="right" width="365px"/> 

-   [Introduction](#introduction)

-   [Installation](#installation)

-   [Basic Usage](#basic-usage)

-   [Input](#input)

-   [Output](#output)

-   [Getting Started with Tweedieverse](#getting-started-with-tweedieverse)

-   [Citation](#citation)

-   [Issues](#issues)

<!-- Himel Mallick, Ali Rahnavard -->

<!-- 2022-05-03 <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, Chatterjee, S, Chowdhury, S, Chatterjee, S, Rahnavard, A, Hicks, SC. [Differential expression of single-cell RNA-seq data using Tweedie models](https://onlinelibrary.wiley.com/doi/10.1002/sim.9430). Statistics in Medicine. 2022; 41( 18): 3492- 3510. doi:10.1002/sim.9430

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.