An R package for Plasmodium vivax molecular correction via statistical genetic inference of
- Relapse
- Recrudescence
- Reinfection
The core function, compute_posterior(), computes per-person posterior probabilities of relapse, recrudescence, and reinfection (recurrence states) using P. vivax genetic data on two or more episodes. To fully understand the core function, in addition to reading this README in its entirety and the pre-print cited below, we recommend reading the vignette("demonstrate-usage", "Pv3Rs") and Understand posterior probabilities.
Two other important features:
plot_data()visualises genetic data for molecular correction, regardless of the analytical method (e.g., Plasmodium falciparum data intended for analysis using a WHO match-counting algorithm).plot_simplex()can be used to visualise per-recurrence probabilities of relapse, recrudescence, and reinfection, or any other probability triplet summing to one.
Please be aware of the following points!
The Pv3Rs R package is not yet peer-reviewed and thus liable to modification. The model is described in the preprint Taylor, Foo & White, 2022, building on a prototype in Taylor & Watson et al. 2019.
Prior considerations:
Genetic data are modelled using a Bayesian model, whose prior is ideally informative (in [2] priors were generated by a time-to-event model built by James Watson) because the cause of recurrent P. vivax malaria is not always identifiable from genetic data alone: when the data are consistent with recurrent parasites that are relatively unrelated to those in all preceding infections, both reinfection and relapse are plausible; meanwhile, when the data are compatible with recurrent parasites that are clones of those in the preceding infection, both recrudescence and relapse are plausible.
The main Pv3Rs function,
compute_posterior(), could be applied to P. falciparum by setting the prior probability of relapse to zero, but genotyping errors, which are not accounted for under the current Pv3Rs model, are liable to lead to the misclassification of recrudescence as reinfection when the prior probability of relapse is zero (and of recrudescence as relapse when the prior probability of relapse exceeds zero).
Notable assumptions and limitations:
As with any model, Pv3Rs makes various assumptions that limit its capabilities in some settings.
Mutually exclusive recurrent states
Recurrence states are modelled as mutually exclusive, suitable for studies where participants are actively followed up frequently and where all detected infections are treated to the extent that parasitaemia drops below some detectable level before recurrence, if recurrence occurs. In studies with untreated or accumulated infections, outputs may not be meaningful.
Unmodelled complexities
We do not model all the complexities around molecular correction. For example, population structure, including household effects; failure to capture low-density clones in a blood sample of limited volume [Snounou & Beck, 1998]; and hidden biomass the spleen and bone marrow [Markus, 2019]. Users must interpret outputs in context of the study and its methods. For example, we expect Pv3Rs to output probable relapse if a person is reinfected by a new mosquito but with parasites that are recently related to those that caused a previous infection, as might happen in household transmission chains.
Sibling misspecification
Relapsing parasites that are siblings of parasites in previous infections can be meiotic, parent-child-like, regular or half siblings, but we model all sibling parasites as regular siblings via the following assumptions:
- Allele inheritance is independent (not true of meiotic siblings)
- Aibling relationships are transitive (not true of parent-child-like trios or some half-sibling trios)
- Alleles of a sibling cluster are drawn from at most two parental alleles (not true of half siblings)
In our experience, half sibling misspecification leads to some misclassification of relapses as reinfections; see Understand half-sibling misspecification. A descriptive study to explore the extent of half-sibling misspecification is recommended (an example will be provided in an upcoming manuscript).
Observation errors and de novo mutations
We do not model undetected alleles, genotyping errors, or de novo mutations. Recrudescent parasites are modelled as perfect clones under Pv3Rs. As such, the posterior probability of recrudescence is rendered zero by errors and mutations. This becomes more likely when there are data on more markers. Sensitivity analyses that explore the impact of errors and mutations on recurrence state probabilities are merited.
Interpreting probable reinfection and recrudescence
When data are not sufficiently informative to distinguish between recrudescence and relapse (or reinfection and relapse), the posterior probabilities of recrudescence and relapse (or reinfection and relapse) are heavily influenced by a model assumption over relationship graphs; see Understand graph-prior ramifications. The development of a more biologically-principled generative model on parasite relationships is merited.
| Limitation | Reason |
|---|---|
| Possible misclassification of persistent and/or accumulated states | Modelling recurrent states as mutually exclusive |
| Possible inconsistency with data on more-and-more markers | Not modelling errors |
| Possible misclassification of relapse | Half-sibling misspecification and not modelling errors |
| Possible misclassification of recrudescence | Not modelling errors |
| Possible misclassification of reinfection | Not modelling population structure |
| Strong prior impact on posterior | Recurrent states are not always identifiable from genetic data alone |
Computational limits:
Pv3Rs scales to hundreds of markers but not whole-genome sequence (WGS) data.
We do not recommend running
compute_posterior()for data whose total genotype count (sum of per-episode multiplicities of infection) exceeds eight. If the total genotype counts exceeds eight but there are multiple recurrences, it might be possible to compute posterior probabilities by analysing episodes pairwise (this approach was used in [2] and we’re working currently on an improved version).The per-marker allele limit of
compute_posterior()is untested. Very high marker cardinalities could lead to very small allele frequencies and thus some underflow problems.
Population-level allele frequencies:
In addition to P. vivax allelic data on two or more episodes, compute_posterior() requires as input population-level allele frequencies. To minimise bias due to within-host selection of recrudescent parasites, we recommend using only enrolment episodes to estimate population-level allele frequencies, and ideally enrolment episodes from study participants selected at random, not only study participants who experience recurrence. That said, if most recurrences are either reinfections or relapses, both of which are draws from the mosquito population (albeit a delayed draw in the case of a relapse), assuming there is no systematic within-patient selection (as might occur when infections encounter lingering drug pressure), estimates based on all episodes should be unbiased and more precise than those based on enrolment episodes only.
Installation
#===============================================================================
# First try installing Pv3Rs from CRAN (available soon if not already):
#===============================================================================
install.packages("Pv3Rs")
#===============================================================================
# If Pv3Rs is not available on CRAN:
#===============================================================================
# Install or update devtools from CRAN
install.packages("devtools")
# Install Pv3Rs from GitHub
# We recommend doing this in RStudio: RStudio installs pandoc, required for
# vignette building. If not, you might need to install pandoc and check its
# path; otherwise set build_vignettes = FALSE
devtools::install_github("aimeertaylor/Pv3Rs", build_vignettes = TRUE)
#===============================================================================
# Getting started after installation:
#===============================================================================
# Load and attach Pv3Rs
library(Pv3Rs)
# List links to all available documentation
help(package = "Pv3Rs")
# List links to vignettes
vignette(package = "Pv3Rs")
# View function documentation including examples, e.g.,
?compute_posterior