readme.Rmd

---
title: "Nearest-neighbor Projected-Distance Regression (NPDR)"
output: github_document
---
## tldr

NPDR is a nearest-neighbor feature selection algorithm that fits a generalized linear model for _projected distances_ of a given attribute over all pairs of instances in a neighborhood. 
In the NPDR model, the predictor is the attribute distance between neighbors projected onto the attribute dimension, and the outcome is the projected phenotype distance (for quantitative traits) or hit/miss (for case/control) between all pairs of nearest neighbor instances.
NPDR can fit any combination of predictor data types (categorical or numeric) and outcome data types (case-control or quantitative) as well as adjust for covariates that may be confounding. 
As with [STIR](https://insilico.github.io/stir/) (STatistical Inference Relief), NDPR allows for the calculation of statistical significance of importance scores and adjustment for multiple testing.   

## Install
You can install the development version from GitHub with remotes:

```{r eval=FALSE}
# install.packages("remotes") # uncomment to install remotes
remotes::install_github("insilico/npdr")

library(npdr)
# data(package = "npdr")
```

### Dependencies

To set `fast.reg = TRUE` or `fast.dist = TRUE` or `use.glmnet = TRUE`, please install the `speedglm` and `glmnet` packages:

```{r eval=FALSE}
install.packages(c("speedglm", "wordspace", "glmnet"))
```

If an issue arises with updating `openssl`, try updating it on your own system, e.g. for MacOS `brew install openssl@1.1`.

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## Details

Relief-based methods are nearest-neighbor machine learning feature selection algorithms that compute the importance of attributes that may involve interactions in high-dimensional data.
Previously we introduced STIR, which extended Relief-based methods to compute statistical significance of attributes in case-control data by reformulating the Relief score as a pseudo t-test.
Here we extend the statistical formalism of STIR to a generalized linear model (glm) formalism to handle quantitative and case-control outcome variables, any predictor data type (continuous or categorical), and  adjust  for  covariates  while  computing statistical significance of attributes.


## Contact
[brett.mckinney@gmail.com](brett.mckinney@gmail.com)


## Websites

- [insilico Github Organization](https://github.com/insilico)

- [insilico McKinney Lab](http://insilico.utulsa.edu/)


## Related references

- [2017 STIR paper in Bioinformatics](https://doi.org/10.1093/bioinformatics/bty788)

- [2013 Gene-Wise Adaptive-Neighbors paper in PLoS One](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0081527)