seqgendiff

Sequence Generation for Differential Expression Analysis and Beyond

https://github.com/dcgerard/seqgendiff

Repository

Sequence Generation for Differential Expression Analysis and Beyond

README.Rmd

---
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# RNA-Seq Generation/Modification for Simulation


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[![CRAN status](https://www.r-pkg.org/badges/version/seqgendiff)](https://cran.r-project.org/package=seqgendiff)
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This package will take real RNA-seq data (either single-cell or bulk) and alter it by adding signal to it. This signal is in the form of a generalized linear model with a log (base-2) link function under a Poisson / negative binomial / mixture of negative binomials distribution. The advantage of this way of simulating data is that you can see how your method behaves when the simulated data exhibit common (and annoying) features of real data. This is without you having to specify these features *a priori*. We call the way we add signal "binomial thinning".

The main functions are:

- `select_counts()`: Subsample the columns and rows of a real RNA-seq count 
  matrix. You would then feed this sub-matrix into one of the thinning 
  functions below.
- `thin_diff()`: The function most users should be using for general-purpose 
  binomial thinning. For the special applications of the two-group model or 
  library/gene thinning, see the functions listed below.
- `thin_2group()`: The specific application of thinning in the two-group model.
- `thin_lib()`: The specific application of library size thinning.
- `thin_gene()`: The specific application of total gene expression thinning.
- `thin_all()`: The specific application of thinning all counts.
- `effective_cor()`: Returns an estimate of the actual correlation between 
  the surrogate variables and a user-specified design matrix.
- `ThinDataToSummarizedExperiment()`: Converts a `ThinData` object to a 
  `SummarizedExperiment()` object.
- `ThinDataToDESeqDataSet()`: Converts a `ThinData` object to a `DESeqDataSet`
  object.

If you find a bug or want a new feature, please submit an 
[issue](https://github.com/dcgerard/seqgendiff/issues).

Check out [NEWS](NEWS.md) for updates.

# Installation
To install from CRAN, run the following code in R:
```{r, eval=FALSE}
install.packages("seqgendiff")
```

To install the latest version of seqgendiff, run the following code in R:
```{r, eval = FALSE}
install.packages("devtools")
devtools::install_github("dcgerard/seqgendiff")
```

To get started, check out the vignettes by running the following in R:
```{r, eval=FALSE}
library(seqgendiff)
browseVignettes(package = "seqgendiff")
```
Or you can check out the vignettes I post online: .

# Citation

If you use this package, please cite:

> Gerard, D (2020). "Data-based RNA-seq simulations by binomial thinning." _BMC Bioinformatics_. 21(1), 206. doi: [10.1186/s12859-020-3450-9](https://doi.org/10.1186/s12859-020-3450-9).

A BibTeX entry for LaTeX users is
```{tex, eval = FALSE}
@article{gerard2020data,
	author = {Gerard, David},
	title = {Data-based {RNA}-seq simulations by binomial thinning},
	year = {2020},
	volume={21},
	number={1},
	pages={206},
	doi = {10.1186/s12859-020-3450-9},
	publisher = {BioMed Central Ltd},
	journal = {BMC Bioinformatics}
}
```

# Code of Conduct

Please note that the 'seqgendiff' project is released with a 
[Contributor Code of Conduct](https://github.com/dcgerard/seqgendiff/blob/master/CODE_OF_CONDUCT.md).
By contributing to this project, you agree to abide by its terms.

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