Biology subjects

Tongsima, S.

Publications and source records attributed to Tongsima, S..

3 recordsLinked to original sources

bioRxiv2020

ipADMIXTURE: R package for inferring sub-population clusters based on genetic admixture

ipADMIXTURE is an R package to infer clusters and their phylogeny based on Q matrices of genetic admixture analysis. It is the first software of its kind to infer not just only clusters, but also the hierarchy of sub-populations w.r.t. the minimum number of ancestors that split any pair of clusters apart. Since inputs of the package, Q matrices, can be obtained from well-known software (ADMIXTURE, STRUCTURE, etc.) and the Q matrices are mandatory information that are used in genetic population structure study, our package has a potential to help scientists and researchers to find deeper explanation of admixture analysis in their studies. Our package comes with a user-friendly interface to make the software accessible for everyone.

bioinformatics↗

bioRxiv2017

A methodology for unsupervised clustering using iterative pruning to capture fine-scale structure

SNP-based information is used in several existing clustering methods to detect shared genetic ancestry or to identify population substructure. Here, we present a methodology for unsupervised clustering using iterative pruning to capture fine-scale structure called IPCAPS. Our method supports ordinal data which can be applied directly to SNP data to identify fine-scale population structure. We compare our method to existing tools for detecting fine-scale structure via simulations. The simulated data do not take into account haplotype information, therefore all markers are independent. Although haplotypes may be more informative than SNPs, especially in fine-scale detection analyses, the haplotype inference process often remains too computationally intensive. Therefore, our strategy has been to restrict attention to SNPs and to investigate the scale of the structure we are able to detect with them. We show that the experimental results in simulated data can be highly accurate and an improvement to existing tools. We are convinced that our method has a potential to detect fine-scale structure.

bioinformatics↗

bioRxiv2017

IPCAPS: an R package for iterative pruning to capture population structure

BackgroundResolving population genetic structure is challenging, especially when dealing with closely related or geographically confined populations. Although Principal Component Analysis (PCA)-based methods and genomic variation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic ancestry, improvements can be made especially when fine-scale population structure is the target.\n\nResultsThis work presents an R package called IPCAPS, which uses SNP information for resolving possibly fine-scale population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipPCA) framework that systematically assigns individuals to genetically similar subgroups. In each iteration, our tool is able to detect and eliminate outliers, hereby avoiding severe misclassification errors.\n\nConclusionsIPCAPS supports different measurement scales for variables used to identify substructure. Hence, panels of gene expression and methylation data can be accommodated as well. The tool can also be applied in patient sub-phenotyping contexts. IPCAPS is developed in R and is freely available from bio3.giga.ulg.ac.be/ipcaps

bioinformatics↗