README.md

In kridsadakorn/ipcaps2: Iterative Pruning to Capture Population Structure version 2

IPCAPS2

IPCAPS2 is an unsupervised clustering algorithm based on iterative pruning to capture population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis (ipPCA) algorithm by Intarapanich et al. (2009) \<doi: 10.1186/1471-2105-10-382> and Limpiti et al. (2011)\<doi: 10.1186/1471-2105-12-255>. The IPCAPS2 involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and Clustering EM estimation as in Lebret et al. (2015) \<doi: 10.18637/jss.v067.i06>. In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package KRIS.

Installation

You can install the released version of IPCAPS2 from CRAN with:

install.packages("IPCAPS2")

Alternatively, you can install the dev version of IPCAPS2 from Github with

install.packages("remotes")
remotes::install_github("kridsadakorn/ipcaps2", dependencies = TRUE)

Document

You can see the reference manual from: https://www.biostatgen.org/ipcaps/

Example

This is a basic example which shows you how to use the packages:

library(IPCAPS2)

BED.file <- system.file("extdata", "ipcaps_example.bed", package = "IPCAPS2")
LABEL.file <- system.file("extdata", "ipcaps_example_individuals.txt.gz",
                          package = "IPCAPS2")
my.cluster1 <- ipcaps2(bed = BED.file, label.file = LABEL.file, lab.col = 2,
out = tempdir(), silence = TRUE)

The function ipcaps does unsupervised clusering, and here is the result:

table(my.cluster1$cluster$label, my.cluster1$cluster$group)
#>           
#>             1  2  3  4  5
#>   outlier3  1  1  1  0  0
#>   pop1      0  0  0 50  0
#>   pop2      0  0  0  0 50

The output directory will be indicated in the console or in my.cluster1$output.dir. All result files are saved at:You can naviage to check the html visualizations in the output directory.

print(my.cluster1$output.dir)
#> [1] "/var/folders/sp/hhmj9xvx53z4g4dktf5f503r0000gp/T//RtmprvfefM/cluster_output"

list.files(my.cluster1$output.dir)
#> [1] "groups.txt"                "images"                   
#> [3] "RData"                     "tree_scatter_cluster.html"
#> [5] "tree_scatter_label.html"   "tree_scree.html"          
#> [7] "tree_text.html"

About

• Prof. Kristel Van Steen, visit
• Kridsadakorn Chaichoompu, visit

kridsadakorn/ipcaps2 documentation built on June 11, 2022, 8:35 p.m.