R-universe search: admixture

Showing 20 of total 20 results (show query)

xaviermilhaud

admix:Package Admix for Admixture (aka Contamination) Models

Implements techniques to estimate the unknown quantities related to two-component admixture models, where the two components can belong to any distribution (note that in the case of multinomial mixtures, the two components must belong to the same family). Estimation methods depend on the assumptions made on the unknown component density; see Bordes and Vandekerkhove (2010) <doi:10.3103/S1066530710010023>, Patra and Sen (2016) <doi:10.1111/rssb.12148>, and Milhaud, Pommeret, Salhi, Vandekerkhove (2024) <doi:10.3150/23-BEJ1593>. In practice, one can estimate both the mixture weight and the unknown component density in a wide variety of frameworks. On top of that, hypothesis tests can be performed in one and two-sample contexts to test the unknown component density (see Milhaud, Pommeret, Salhi and Vandekerkhove (2022) <doi:10.1016/j.jspi.2021.05.010>, and Milhaud, Pommeret, Salhi, Vandekerkhove (2024) <doi:10.3150/23-BEJ1593>). Finally, clustering of unknown mixture components is also feasible in a K-sample setting (see Milhaud, Pommeret, Salhi, Vandekerkhove (2024) <https://jmlr.org/papers/v25/23-0914.html>).

Maintained by Xavier Milhaud. Last updated 25 days ago.

cpp

33.0 match 1 stars 5.48 score 8 scripts

storeylab

bnpsd:Simulate Genotypes from the BN-PSD Admixture Model

The Pritchard-Stephens-Donnelly (PSD) admixture model has k intermediate subpopulations from which n individuals draw their alleles dictated by their individual-specific admixture proportions. The BN-PSD model additionally imposes the Balding-Nichols (BN) allele frequency model to the intermediate populations, which therefore evolved independently from a common ancestral population T with subpopulation-specific FST (Wright's fixation index) parameters. The BN-PSD model can be used to yield complex population structures. This simulation approach is now extended to subpopulations related by a tree. Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.

Maintained by Alejandro Ochoa. Last updated 2 years ago.

18.3 match 8 stars 5.42 score 33 scripts

spacecowboy-71

xadmix:Subsetting and Plotting Optimized for Admixture Data

A few functions which provide a quick way of subsetting genomic admixture data and generating customizable stacked barplots.

Maintained by Lukas Schönmann. Last updated 3 years ago.

admixture genomics ggplot2 plotting

22.9 match 3.70 score 6 scripts

tom-jenkins

mapmixture:Spatial Visualisation of Admixture on a Projected Map

Visualise admixture as pie charts on a projected map, admixture as traditional structure barplots or facet barplots, and scatter plots from genotype principal components analysis. A 'shiny' app allows users to create admixture maps interactively. Jenkins TL (2024) <doi:10.1111/1755-0998.13943>.

Maintained by Tom Jenkins. Last updated 2 months ago.

admixture ancestry mapmixture population-structure shiny

13.9 match 42 stars 5.58 score 13 scripts

mgautierinra

poolfstat:Computing f-Statistics and Building Admixture Graphs Based on Allele Count or Pool-Seq Read Count Data

Functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, Patterson's F2, F3, F3*, F4 and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as 'vcf' files or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in TreeMix, BayPass and SelEstim format).

Maintained by Mathieu Gautier. Last updated 4 months ago.

cpp

13.4 match 3.67 score 118 scripts

ochoalab

simfam:Simulate and Model Family Pedigrees with Structured Founders

The focus is on simulating and modeling families with founders drawn from a structured population (for example, with different ancestries or other potentially non-family relatedness), in contrast to traditional pedigree analysis that treats all founders as equally unrelated. Main function simulates a random pedigree for many generations, avoiding close relatives, pairing closest individuals according to a 1D geography and their randomly-drawn sex, and with variable children sizes to result in a target population size per generation. Auxiliary functions calculate kinship matrices, admixture matrices, and draw random genotypes across arbitrary pedigree structures starting from the corresponding founder values. The code is built around the plink FAM table format for pedigrees. There are functions that simulate independent loci and also functions that use an explicit recombination model to simulate linkage disequilibrium (LD) in the pedigree, as well as population analogs resembling the Li-Stephens model. Described in Yao and Ochoa (2023) <doi:10.7554/eLife.79238>.

Maintained by Alejandro Ochoa. Last updated 3 months ago.

cpp

7.5 match 3 stars 5.12 score 11 scripts

gbradburd

conStruct:Models Spatially Continuous and Discrete Population Genetic Structure

A method for modeling genetic data as a combination of discrete layers, within each of which relatedness may decay continuously with geographic distance. This package contains code for running analyses (which are implemented in the modeling language 'rstan') and visualizing and interpreting output. See the paper for more details on the model and its utility.

Maintained by Gideon Bradburd. Last updated 1 years ago.

cpp

4.5 match 35 stars 8.39 score 70 scripts

sebastien-plutniak

archeofrag:Spatial Analysis in Archaeology from Refitting Fragments

Methods to analyse spatial units in archaeology from the relationships between refitting fragmented objects scattered in these units (e.g. stratigraphic layers). Graphs are used to model archaeological observations. The package is mainly based on the 'igraph' package for graph analysis. Functions can: 1) create, manipulate, and simulate fragmentation graphs, 2) measure the cohesion and admixture of archaeological spatial units, and 3) characterise the topology of a specific set of refitting relationships. Empirical datasets are provided as examples. Documentation about 'archeofrag' is provided by the vignette included in this package, by the accompanying scientific papers: Plutniak (2021, Journal of Archaeological Science, <doi:10.1016/j.jas.2021.105501>) and Plutniak (2022, Journal of Open Source Software, <doi:10.21105/joss.04335>). This package is complemented by a companion GUI application available at <https://analytics.huma-num.fr/Sebastien.Plutniak/archeofrag/>.

Maintained by Sebastien Plutniak. Last updated 5 days ago.

archaeological-objects archaeological-science archaeology fragmentation network-analysis

5.6 match 20 stars 6.48 score 10 scripts 1 dependents

storeylab

popkin:Estimate Kinship and FST under Arbitrary Population Structure

Provides functions to estimate the kinship matrix of individuals from a large set of biallelic SNPs, and extract inbreeding coefficients and the generalized FST (Wright's fixation index). Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.

Maintained by Alejandro Ochoa. Last updated 6 months ago.

5.1 match 20 stars 6.11 score 65 scripts

thijsjanzen

GenomeAdmixR:Simulate Admixture of Genomes

Individual-based simulations forward in time, simulating how patterns in ancestry along the genome change after admixture. Full description can be found in Janzen (2021) <doi:10.1111/2041-210X.13612>.

Maintained by Thijs Janzen. Last updated 1 years ago.

cpp

5.9 match 5 stars 5.24 score 14 scripts

thijsjanzen

junctions:The Breakdown of Genomic Ancestry Blocks in Hybrid Lineages

Individual based simulations of hybridizing populations, where the accumulation of junctions is tracked. Furthermore, mathematical equations are provided to verify simulation outcomes. Both simulations and mathematical equations are based on Janzen (2018, <doi:10.1101/058107>) and Janzen (2022, <doi:10.1111/1755-0998.13519>).

Maintained by Thijs Janzen. Last updated 19 days ago.

cpp

5.9 match 2 stars 4.71 score 17 scripts

evolecolgroup

tidypopgen:Tidy Population Genetics

We provide a tidy grammar of population genetics, facilitating the manipulation and analysis of data on biallelic single nucleotide polymorphisms (SNPs). `tidypopgen` scales to very large genetic datasets by storing genotypes on disk, and performing operations on them in chunks, without ever loading all data in memory.

Maintained by Andrea Manica. Last updated 10 days ago.

openblas zlib cpp openmp

3.5 match 4 stars 5.84 score 8 scripts

leospeidel

twigstats:twigstats

This package takes Relate genealogies as input to compute time-stratified f-statistics.

Maintained by Leo Speidel. Last updated 25 days ago.

cpp

3.3 match 13 stars 3.93 score 12 scripts

bioc

LEA:LEA: an R package for Landscape and Ecological Association Studies

LEA is an R package dedicated to population genomics, landscape genomics and genotype-environment association tests. LEA can run analyses of population structure and genome-wide tests for local adaptation, and also performs imputation of missing genotypes. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). In addition, LEA computes values of genetic offset statistics based on new or predicted environments (genetic.gap, genetic.offset). LEA is mainly based on optimized programs that can scale with the dimensions of large data sets.

Maintained by Olivier Francois. Last updated 21 days ago.

software statistical method clustering regression openblas

1.9 match 6.63 score 534 scripts

ytutsunomiya

GHap:Genome-Wide Haplotyping

Haplotype calling from phased marker data. Given user-defined haplotype blocks (HapBlock), the package identifies the different haplotype alleles (HapAllele) present in the data and scores sample haplotype allele genotypes (HapGenotype) based on HapAllele dose (i.e. 0, 1 or 2 copies). The output is not only useful for analyses that can handle multi-allelic markers, but is also conveniently formatted for existing pipelines intended for bi-allelic markers. The package was first described in Bioinformatics by Utsunomiya et al. (2016, <doi:10.1093/bioinformatics/btw356>). Since the v2 release, the package provides functions for unsupervised and supervised detection of ancestry tracks. The methods implemented in these functions were described in an article published in Methods in Ecology and Evolution by Utsunomiya et al. (2020, <doi:10.1111/2041-210X.13467>). The source code for v3 was modified for improved performance and inclusion of new functionality, including analysis of unphased data, runs of homozygosity, sampling methods for virtual gamete mating, mixed model fitting and GWAS.

Maintained by Yuri Tani Utsunomiya. Last updated 6 months ago.

cpp

1.8 match 5 stars 6.74 score 44 scripts

tpook92

MoBPS:Modular Breeding Program Simulator

Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. The package is also used as the background simulator for our a web-based interface <http:www.mobps.de>. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.

Maintained by Torsten Pook. Last updated 3 years ago.

4.5 match 2.35 score 45 scripts

cran

neuromplex:Neural Multiplexing Analysis

Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at <arXiv:1911.04387>.

Maintained by Surya Tokdar. Last updated 4 years ago.

8.9 match 1.00 score

eriqande

gscramble:Simulating Admixed Genotypes Without Replacement

A genomic simulation approach for creating biologically informed individual genotypes from empirical data that 1) samples alleles from populations without replacement, 2) segregates alleles based on species-specific recombination rates. 'gscramble' is a flexible simulation approach that allows users to create pedigrees of varying complexity in order to simulate admixed genotypes. Furthermore, it allows users to track haplotype blocks from the source populations through the pedigrees.

Maintained by Eric C. Anderson. Last updated 1 years ago.

noaa-omics-software

1.8 match 4.35 score 15 scripts

mpcox

adwave:Wavelet Analysis of Genomic Data from Admixed Populations

Implements wavelet-based approaches for describing population admixture. Principal Components Analysis (PCA) is used to define the population structure and produce a localized admixture signal for each individual. Wavelet summaries of the PCA output describe variation present in the data and can be related to population-level demographic processes. For more details, see J Sanderson, H Sudoyo, TM Karafet, MF Hammer and MP Cox. 2015. Reconstructing past admixture processes from local genomic ancestry using wavelet transformation. Genetics 200:469-481 <doi:10.1534/genetics.115.176842>.

Maintained by Murray Cox. Last updated 7 years ago.

6.5 match 1 stars 1.00 score 4 scripts

cran

CLONETv2:Clonality Estimates in Tumor

Analyze data from next-generation sequencing experiments on genomic samples. 'CLONETv2' offers a set of functions to compute allele specific copy number and clonality from segmented data and SNPs position pileup. The package has also calculated the clonality of single nucleotide variants given read counts at mutated positions. The package has been developed at the laboratory of Computational and Functional Oncology, Department of CIBIO, University of Trento (Italy), under the supervision of prof Francesca Demichelis. References: Prandi et al. (2014) <doi:10.1186/s13059-014-0439-6>; Carreira et al. (2014) <doi:10.1126/scitranslmed.3009448>; Romanel et al. (2015) <doi:10.1126/scitranslmed.aac9511>.

Maintained by Yari Ciani. Last updated 3 years ago.

3.5 match 1.00 score