index.Rmd

## Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC) for large genomic data



### Install packages

Install the most recent version of the KLFDAPC package using devtools:
`````{r}
library("devtools")

devtools::install_github("xinghuq/KLFDAPC")
``````
Alternatively, you can install from the source files, run the following commands in the shell:

```{shell}
R CMD build KLFDAPC
R CMD check --as-cran KLFDAPC_0.1.0.tar.gz
R CMD INSTALL KLFDAPC_0.1.0.tar.gz
```


### Dependencies

Before install or during installation, make sure the below dependences are installed.
``````{r}
requireNamespace("SNPRelate")

if (!requireNamespace("BiocManager", quietly=TRUE))

  install.packages("BiocManager",repos = "http://cran.us.r-project.org")
  
if (!requireNamespace("SNPRelate", quietly=TRUE))

  BiocManager::install("SNPRelate")
  
 if (!requireNamespace("DA", quietly=TRUE))
 
  devtools::install_github("xinghuq/DA")
``````

### Vignettes and tutorials

``````{r}

vignette("Population_structure_of_Covid")

vignette("Population_structure_of_RegMap")

vignette("Genome_scan_KLFDAPC")

``````



Welcome any [feedback](https://github.com/xinghuq/KLFDAPC/issues) and [pull request](https://github.com/xinghuq/KLFDAPC/pulls). 


### Citation

Qin. X. 2020. KLFDAPC: Kernel local Fisher discriminant analysis of principal components (KLFDAPC) for large genomic data. R package version 0.2.0.https://xinghuq.github.io/KLFDAPC/

Qin, X., Chiang, C.W.K., and Gaggiotti, O.E. (2021). [Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC) significantly improves the accuracy of predicting geographic origin of individuals](https://www.biorxiv.org/content/10.1101/2021.05.15.444294v2.full). bioRxiv, 2021.2005.2015.444294.