Algorithms for Optimal Transport and Wasserstein Distances

Schrieber, Jörn

doi:10.53846/goediss-7314

Cited by 8 publications

(10 citation statements)

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“…With the DFE associated with the simulated data sets being known, the accuracy of estimated DFE was assessed by comparing them to the known DFE using Earth Mover's Distance (EMD) implemented in the transport package in R (Schuhmacher et al, 2019). Earth Mover's Distance quantifies the dissimilarity between two distributions as the ‘work’ required to change one distribution to the other, thus taking into account the amount of overlap.…”

Section: Methodsmentioning

confidence: 99%

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Inference of the distribution of fitness effects of mutations is affected by single nucleotide polymorphism filtering methods, sample size and population structure

Andersson

Wang

Haller

et al. 2023

Molecular Ecology Resources

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The distribution of fitness effects (DFE) of new mutations has been of interest to evolutionary biologists since the concept of mutations arose. Modern population genomic data enable us to quantify the DFE empirically, but few studies have examined how data processing, sample size and cryptic population structure might affect the accuracy of DFE inference. We used simulated and empirical data (from Arabidopsis lyrata) to show the effects of missing data filtering, sample size, number of single nucleotide polymorphisms (SNPs) and population structure on the accuracy and variance of DFE estimates. Our analyses focus on three filtering methods—downsampling, imputation and subsampling—with sample sizes of 4–100 individuals. We show that (1) the choice of missing‐data treatment directly affects the estimated DFE, with downsampling performing better than imputation and subsampling; (2) the estimated DFE is less reliable in small samples (<8 individuals), and becomes unpredictable with too few SNPs (<5000, the sum of 0‐ and 4‐fold SNPs); and (3) population structure may skew the inferred DFE towards more strongly deleterious mutations. We suggest that future studies should consider downsampling for small data sets, and use samples larger than 4 (ideally larger than 8) individuals, with more than 5000 SNPs in order to improve the robustness of DFE inference and enable comparative analyses.

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Section: Methodsmentioning

confidence: 99%

Section: Simulations In Slimmentioning

confidence: 99%

Inference of the distribution of fitness effects of mutations is affected by single nucleotide polymorphism filtering methods, sample size and population structure

Andersson

Wang

Haller

et al. 2023

Molecular Ecology Resources

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“…For the t RC estimates of the BEAST approach, K‐means clustering was conducted on the matrix of second‐order Wasserstein distance between genes, because Euclidean distance is not applicable for probability distributions. The statistical analyses were done in R. Specifically, Euclidean distance calculation and K‐means clustering were done with stats R package (R Core Team, 2020 ); Wasserstein distance calculation was done with transport R package (Schuhmacher et al, 2019 ); and handling and reformatting of the input data tables were done with tidyverse R package (Wickham et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Assessment of phylogenetic approaches to study the timing of recombination cessation on sex chromosomes

Zhang

Sigeman

Hansson

2022

J of Evolutionary Biology

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The evolution of sex chromosomes is hypothesized to be punctuated by consecutive recombination cessation events, forming “evolutionary strata” that ceased to recombine at different time points. The demarcation of evolutionary strata is often assessed by estimates of the timing of recombination cessation (tRC) along the sex chromosomes, commonly inferred from the level of synonymous divergence or with species phylogenies at gametologous (X‐Y or Z‐W) sequence data. However, drift and selection affect sequences unpredictably and introduce uncertainty when inferring tRC. Here, we assess two alternative phylogenetic approaches to estimate tRC; (i) the expected likelihood weight (ELW) approach that finds the most likely topology among a set of hypothetical topologies and (ii) the BEAST approach that estimates tRC with specified calibration priors on a reference species topology. By using Z and W gametologs of an old and a young evolutionary stratum on the neo‐sex chromosome of Sylvioidea songbirds, we show that the ELW and BEAST approaches yield similar tRC estimates, and that both outperform two frequently applied approaches utilizing synonymous substitution rates (dS) and maximum likelihood (ML) trees, respectively. Moreover, we demonstrate that both ELW and BEAST provide more precise tRC estimates when sequences of multiple species are included in the analyses.

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“…The distance matrix A ∈ R n×n indicates the precision of the simulation cohort, where A(i, j) is the Euclidean distance between CN profiles of simulation i and DNA-seq sample j. The optimal transport algorithm [31,32] then calculates the minimal distance from the simulation cohort to the data cohort, which we refer to as the bulk DNA distance (Figure 2b). A similar strategy is used to compute the scDNA distance (Figure 2c).…”

Section: Statistics For Copy Number Profiles and Cell Phylogeny From ...mentioning

confidence: 99%

Inference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data

Xiang,

Liu,

Dinh

2024

Preprint

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Aneuploidy is frequently observed in cancers and has been linked to poor patient outcome. Analysis of aneuploidy in DNA-sequencing (DNA-seq) data necessitates untangling the effects of the Copy Number Aberration (CNA) occurrence rates and the selection coefficients that act upon the resulting karyotypes. We introduce a parameter inference algorithm that takes advantage of both bulk and single-cell DNA-seq cohorts. The method is based on Approximate Bayesian Computation (ABC) and utilizes CINner, our recently introduced simulation algorithm of chromosomal instability in cancer. We examine three groups of statistics to summarize the data in the ABC routine: (A) Copy Number-based measures, (B) phylogeny tip statistics, and (C) phylogeny balance indices. Using these statistics, our method can recover both the CNA probabilities and selection parameters from ground truth data, and performs well even for data cohorts of relatively small sizes. We find that only statistics in groups A and C are well-suited for identifying CNA probabilities, and only group A carries the signals for estimating selection parameters. Moreover, phylogeny tip statistics in group B are frequently inferred incorrectly from phylogeny reconstruction algorithms such as MEDICC2, further limiting its applicability. As data from both bulk and single-cell DNA-sequencing techniques becomes increasingly available, our inference framework promises to facilitate the analysis of distinct cancer types, differentiation between selection and neutral drift, and prediction of cancer clonal dynamics.

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Algorithms for Optimal Transport and Wasserstein Distances

Cited by 8 publications

References 0 publications

Inference of the distribution of fitness effects of mutations is affected by single nucleotide polymorphism filtering methods, sample size and population structure

Inference of the distribution of fitness effects of mutations is affected by single nucleotide polymorphism filtering methods, sample size and population structure

Assessment of phylogenetic approaches to study the timing of recombination cessation on sex chromosomes

Inference of chromosome selection parameters and missegregation rate in cancer from DNA-sequencing data

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