#normR - normR obeys regime mixture rules
## Normalization and Difference Calling for Next Generation Sequencing (NGS)
Experiments via Joint Multinomial Modeling
---
Two NGS tracks are modeled simultaneously by fitting a binomial mixture model
on mapped read counts. In the first counting process, a desired smoothing
kernel (bin size) and read characteristic threshold (quality, SAMFLAG) can be
specified. In a second step a binomial mixture model with a user-specified
number of components is fit to the data. The fit yields different enrichment
regimes in the supplied NGS tracks. Log-space computation is done in C/C++
where [OpenMP](http://openmp.org) enables for fast parallel computation.
### Release Version
The master branch is always in sync with the [normR Bioconductor release](
http://bioconductor.org/packages/devel/bioc/html/normr.html) and the [normR
github Bioconductor mirror]( https://github.com/Bioconductor-mirror/normr). A
R 3.2 compliant version can be found in the [normR R3.2 tree](
https://github.com/your-highness/normR/tree/R3.2).
### Installation
To install normR from the release repository, easiest way is to use
Bioconductor or devtools:
```R
#install dependencies
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("bamsignals", suppressUpdates=T)
#fetch current normR version from github
install.packages("devtools")
require(devtools)
devtools::install_github("your-highness/normr")
```
### Usage
See the
[vignette](
https://cdn.rawgit.com/your-highness/normR/development/inst/doc/normr.html)
for a toy example on normR usage. The documentation of routines can be accessed
from with R with ``?``.
#### Use cases
* ChIP-seq normalization / enrichment calling with an Input experiment (Whole
Cell Extract, H3/IgG ChIP-seq)
* ChIP-seq differential enrichment calling for two different antigens in same
sample population
* ChIP-seq identification of enrichment regimes to investigate on sample
heterogeneity
* RNA-seq differential expression calling
* ChIP-seq differential enrichment calling in two different samples (be aware
of CNVs!)
* CNV identification
#### Useful links
Be sure to check out the following amazing github projects for your upcoming
NGS magic:
[bamsignals](https://github.com/lamortenera/bamsignals) - Efficient Counting in
Indexed Bam Files for Single End and Paired End NGS Data
[EpicSeg](https://github.com/lamortenera/epicseg) - Chromatin Segmentation
Based on a Probabilistic Multinomial Model for Read Counts
[kfoots](https://github.com/lamortenera/kfoots) - Fit Multivariate Discrete
Probability Distributions to Count Data
[deepTools](https://github.com/fidelram/deepTools) - User-Friendly Tools for
Normalization and Visualization of Deep-Sequencing Data
