Science Score: 39.0%

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    Found 6 DOI reference(s) in README
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    Low similarity (15.9%) to scientific vocabulary
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Created about 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Contributing License Codemeta

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# reclanc


[![codecov](https://codecov.io/gh/KaiAragaki/reclanc/branch/master/graph/badge.svg?token=Z1AquW3Awo)](https://codecov.io/gh/KaiAragaki/reclanc)


reclanc is a revival of [ClaNC (Classification of microarrays to nearest centroids), by Alan R. Dabney](https://doi.org/10.1093/bioinformatics/bti681).

Since the source has been lost (at least to my knowledge), the code comes from [here](https://github.com/naikai/sake/blob/master/R/clanc.R) with heavy modification.

reclanc is a nearest-centroid classifier for expression data. It tends to be a little more sensitive and accurate than similar models like PAM.

Besides its mere existence, reclanc differs slightly from the original ClaNC package in a few ways:

- reclanc supports a wide variety of inputs (`data.frame`, `matrix`, `formula`,  `recipe`, `ExpressionSet`, and `SummarizedExperiment`)
- reclanc plays nicely with [tidymodels](https://www.tidymodels.org/), and offloads things like making folds to `rsample` and tuning to `tune` (see [this vignette](https://kaiaragaki.github.io/reclanc/articles/case-study.html) for how to leverage tidymodels with reclanc). 
- Provides a prediction method based on correlation, rather than distance - useful for predicting classes from data from different sequencing modalities

## Installation

You can install the development version of reclanc like so:

``` r
# install.packages("pak")
pak::pak("KaiAragaki/reclanc")
```

## How to use it

```{r}
library(reclanc)
lapply(synthetic_expression, head) # dummy data
```

```{r}
centroids <- clanc(
  synthetic_expression$expression,
  classes = synthetic_expression$classes,
  active = 5
)
centroids
```

For information on basic usage, see [this](https://kaiaragaki.github.io/reclanc/articles/using-reclanc.html) vignette. For a case study, as well as how to optimize the `active` parameter, see [this](https://kaiaragaki.github.io/reclanc/articles/case-study.html) vignette.


## How it works

You can find a gentle introduction to how reclanc works [here](https://kai.rbind.io/posts/projects-reclanc/) and a more in-depth and statistically rigorous definition of how this algorithm works in the  [original paper](https://doi.org/10.1093/bioinformatics/bti681).

## Citation

Citation for the original ClaNC paper:

Alan R. Dabney, Classification of microarrays to nearest centroids, Bioinformatics, Volume 21, Issue 22, November 2005, Pages 4148–4154, https://doi.org/10.1093/bioinformatics/bti681

Owner

  • Name: Kai Aragaki
  • Login: KaiAragaki
  • Kind: user

whatever the exact opposite of an emotional support dog is

CodeMeta (codemeta.json)

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Dependencies

DESCRIPTION cran
  • dplyr * imports
  • testthat >= 3.0.0 suggests