knitr::opts_chunk$set( fig.width = 7, fig.height = 5 )
This is an R package for performing genetic stock identification (GSI) and associated tasks. Additionally, it includes a method designed to diagnose and correct a bias recently documented in genetic stock identification. The bias occurs when mixture proportion estimates are desired for groups of populations (reporting units) and the number of populations within each reporting unit are uneven.
The functions for conducting genetic mixture analysis and for doing simulation assessment to predict the accuracy of a set of genetic markers for genetic stock identification require that genetic data be input as a data frame in a specific format:
sample_type
: a column telling whether the sample is a reference
sample or a mixture
sample.repunit
: the reporting unit that an individual/collection belongs to. This is required if sample_type is
reference
. And if sample_type is mixture
then repunit must be NA
.collection
: for reference samples, the name of the population that the individual is from. For mixture
samples, this is the name of the particular sample (i.e. stratum or port that is to be treated together in
space and time). This must be a character, not a factor.indiv
a character vector with the ID of the fish. These must be unique.rubias
, we intended to allow both the repunit
and the collection
columns to be
either character vectors or factors. Having them as factors might be desirable if, for example, a certain
sort order of the collections or repunits was desired. However at some point it became clear to Eric that,
given our approach to converting all the data to a C++ data structure of integers, for rapid analyis,
we would be exposing ourselves to greater opportunities for bugginess
by allowing repunit
and collection
to be factors. Accordingly, they must be character vectors. If they
are not, rubias
will throw an error. Note: if you do have a specific sort order for your collections or
repunits, you can always change them into factors after analysis with rubias
. Additionally, you can keep
extra columns in your original data frame (for example repunit_f
or collection_f
) in which the repunits or
the collections are stored as factors. See, for example the data file alewife
. Or you can just keep a character
vector that has the sort order you would like, so as to use it when changing things to factors
after rubias
analysis. (See, for instance, chinook_repunit_levels
.)At the request of the good folks at ADFG, I introduced a few hacks to allow the input to
include markers that are haploid (for example mtDNA haplotypes). To denote a marker as haploid
you still give it two columns of data in your data frame, but the second column of the haploid
marker must be entirely NAs. When rubias
is processing the data and it sees this, it assumes that
the marker is haploid and it treats it appropriately.
Note that if you have a diploid marker it typically does not make sense to mark one of the gene copies as missing and the other as non-missing. Accordingly, if you have a diploid marker that records just one of the gene copies as missing in any individual, it is going to throw an error. Likewise, if your haploid marker does not have every single individual with an NA at the second gene copy, then it's also going to throw an error.
Load our packages first:
library(rubias) library(tidyverse)
# this is what we actually evaluate. library(rubias) # all the following libraries can be loaded with "library(tidyverse)" # but then you have to put tidyverse in the Suggests because this is # in the vignette, and that is bad practice, so, load the packages separately... library(tibble) library(dplyr) library(tidyr) library(stringr) library(ggplot2)
Here are the meta data columns and the first two loci for eight individuals in the chinook
reference data set that comes with the
package:
head(chinook[, 1:8])
Here is the same for the mixture data frame that goes along with that reference data set:
head(chinook_mix[, 1:8])
Sometimes, for a variety of reasons, an individual's genotype might appear more than once in a data
set. rubias
has a quick and dirty function to spot pairs of individuals that share a large number of
genotypes. Clearly you only want to look at pairs that don't have a whole lot of missing data, so one
parameter is the fraction of loci that are non-missing in either fish. In our experience with Fluidigm
assays, if a fish is missing at > 10% of the SNPs, the remaining genotypes are likely to have
a fairly high error rate. So, to look for matching samples, let's require 85% of the genotypes
to be non-missing in both members of the pair. The last parameter is the
fraction of non-missing loci at which the pair has the same genotype. We will set that to 0.94 first.
Here we see it in action:
# combine small_chinook_ref and small_chinook_mix into one big data frame, # but drop the California_Coho collection because Coho all # have pretty much the same genotype at these loci! small_chinook_all <- bind_rows(small_chinook_ref, small_chinook_mix) %>% filter(collection != "California_Coho") # then toss them into a function. matchy_pairs <- close_matching_samples(D = small_chinook_all, gen_start_col = 5, min_frac_non_miss = 0.85, min_frac_matching = 0.94 ) # see that that looks like: matchy_pairs %>% arrange(desc(num_non_miss), desc(num_match))
Check that out. This reveals 7 pairs in the data set that are likely duplicate samples.
If we reduce the min_frac_matching
, we get more matches, but these are very unlikely
to be the same individual, unless genotyping error rates are very high.
# then toss them into a function. This takes half a minute or so... matchy_pairs2 <- close_matching_samples(D = small_chinook_all, gen_start_col = 5, min_frac_non_miss = 0.85, min_frac_matching = 0.80 ) # see that that looks like: matchy_pairs2 %>% arrange(desc(num_non_miss), desc(num_match))
A more principled approach would be to use the allele frequencies in each collection and take a likelihood based approach, but this is adequate for finding obvious duplicates.
In some cases, you might know (more or less unambiguously) the origin of some fish in a particular mixture sample. For example, if 10% of the individuals in a mixture carried coded wire tags, then you would want to include them in the sample, but make sure that their collections of origin were hard-coded to be what the CWTs said. Another scenario in which this might occur is when the genetic data were used for parentage-based tagging of the individuals in the mixture sample. In that case, some individuals might be placed with very high confidence to parents. Then, they should be included in the mixture as having come from a known collection. The folks at the DFO in Nanaimo, Canada are doing an amazing job with PBT and wondered if rubias could be modified to deal with the latter situation.
We've made some small additions to accommodate this. rubias does not do any actual inference of parentage, but if you
know the origin of some fish in the mixture, that can be included in the rubias analysis. The way you do this with
the function infer_mixture()
is to include a column called known_collection
in both the reference data frame
and the mixture data frame. In the reference data frame, known_collection
should just be a copy of the collection
column. However, in the mixture data frame each entry in known_collection
should be the collection that the individual is known to be from (i.e. using parentage inference or a CWT), or, if the individual is not known
to be from any collection, it should be NA. Note that the names of the collections in known_collection
must match
those found in the collection
column in the reference data set.
These modifications are not allowed for the parametric bootstrap (PB) and baseline resampling (BR) methods in infer_mixture()
.
This is done with the infer_mixture
function. In the example data
chinook_mix
our data consist of fish caught in three different fisheries, rec1
,
rec2
, and rec3
as denoted in the collection column. Each of those collections is
treated as a separate sample, getting its own mixing proportion estimate. This is how
it is run with the default options:
mix_est <- infer_mixture(reference = chinook, mixture = chinook_mix, gen_start_col = 5)
The result comes back as a list of four tidy data frames:
mixing_proportions
: the mixing proportions. The column pi
holds the estimated mixing proportion for each collection.indiv_posteriors
: this holds, for each individual, the posterior means of group membership in each
collection. Column PofZ
holds those values. Column log_likelihood
holds the log of the probability
of the individuals genotype given it is from the collection. Also included are n_non_miss_loci
and n_miss_loci
which are
the number of observed loci and the number of missing loci at the individual. A list column missing_loci
contains
vectors with the indices (and the names) of the loci that are missing in that individual. It also includes a column
z_score
which can be used to diagnose fish that don't belong to any samples in the reference data base (see below).mix_prop_traces:
MCMC traces of the mixing proportions for each collection. You will use these if you
want to make density estimates of the posterior distribution of the mixing proportions or if you want
to compute credible intervals. bootstrapped_proportions
: This is NULL in the above example, but if we had chosen
method = "PB"
then this would be a tibble of bootstrap-corrected reporting unit
mixing proportions.These data frames look like this:
lapply(mix_est, head)
In some cases there might be a reason to explicitly set the parameters of the
Dirichlet prior on the mixing proportions of the collections. For a contrived example,
we could imagine that we wanted a Dirichlet prior with all parameters equal to
1/(# of collections), except for the parameters for all the Central Valley Fall Run
populations, to which we would like to assign Dirichlet parameters of 2. That can be
accomplished with the pi_prior
argument to the infer_mixture()
function,
which will let you pass in a tibble in which
one column named "collection" gives the collection, and the other column, named "pi_param"
gives the desired parameter.
Here we construct that kind of input:
prior_tibble <- chinook %>% count(repunit, collection) %>% filter(repunit == "CentralValleyfa") %>% select(collection) %>% mutate(pi_param = 2) # see what it looks like: prior_tibble
Then we can run that in infer_mixture():
set.seed(12) mix_est_with_prior <- infer_mixture(reference = chinook, mixture = chinook_mix, gen_start_col = 5, pi_prior = prior_tibble)
and now, for fun, we can compare the results for the mixing proportions of different collections there with and without the prior for the mixture collection rec1:
comp_mix_ests <- list( `pi (default prior)` = mix_est$mixing_proportions, `pi (cv fall gets 2s prior)` = mix_est_with_prior$mixing_proportions ) %>% bind_rows(.id = "prior_type") %>% filter(mixture_collection == "rec1") %>% select(prior_type, repunit, collection, pi) %>% spread(prior_type, pi) %>% mutate(coll_group = ifelse(repunit == "CentralValleyfa", "CV_fall", "Not_CV_fall")) ggplot(comp_mix_ests, aes(x = `pi (default prior)`, y = `pi (cv fall gets 2s prior)`, colour = coll_group )) + geom_point() + geom_abline(slope = 1, intercept = 0, linetype = "dashed")
Yep, slightly different than before. Let's look at the sums of everything:
comp_mix_ests %>% group_by(coll_group) %>% summarise(with_explicit_prior = sum(`pi (cv fall gets 2s prior)`), with_default_prior = sum(`pi (default prior)`))
We see that for the most part this change to the prior changed the distribution of fish into different collections within the Central Valley Fall reporting unit. This is not suprising---it is very hard to tell apart fish from those different collections. However, it did not greatly change the estimated proportion of the whole reporting unit. This also turns out to make sense if you consider the effect that the extra weight in the prior will have.
This is a simple operation in the tidyverse:
# for mixing proportions rep_mix_ests <- mix_est$mixing_proportions %>% group_by(mixture_collection, repunit) %>% summarise(repprop = sum(pi)) # adding mixing proportions over collections in the repunit # for individuals posteriors rep_indiv_ests <- mix_est$indiv_posteriors %>% group_by(mixture_collection, indiv, repunit) %>% summarise(rep_pofz = sum(PofZ))
The full MCMC output for the mixing proportions is available by default in the
field $mix_prop_traces
. This
can be used to obtain an estimate of the posterior density of the mixing proportions.
Here we plot kernel density estimates for the 6 most abundant repunits from the
rec1
fishery:
# find the top 6 most abundant: top6 <- rep_mix_ests %>% filter(mixture_collection == "rec1") %>% arrange(desc(repprop)) %>% slice(1:6) # check how many MCMC sweeps were done: nsweeps <- max(mix_est$mix_prop_traces$sweep) # keep only rec1, then discard the first 200 sweeps as burn-in, # and then aggregate over reporting units # and then keep only the top6 from above trace_subset <- mix_est$mix_prop_traces %>% filter(mixture_collection == "rec1", sweep > 200) %>% group_by(sweep, repunit) %>% summarise(repprop = sum(pi)) %>% filter(repunit %in% top6$repunit) # now we can plot those: ggplot(trace_subset, aes(x = repprop, colour = repunit)) + geom_density()
Following on from the above example, we will use trace_subset
to compute
the equal-tail 95% credible intervals for the 6 most abundant reporting units in the
rec1
fishery:
top6_cis <- trace_subset %>% group_by(repunit) %>% summarise(loCI = quantile(repprop, probs = 0.025), hiCI = quantile(repprop, probs = 0.975)) top6_cis
Sometimes totally unexpected things happen. One situation we saw in the California Chinook fishery was samples coming to us that were actually coho salmon. Before we included coho salmon in the reference sample, these coho always assigned quite strongly to Alaska populations of Chinook, even though they don't really look like Chinook at all.
In this case, it is useful to look at the raw log-likelihood values computed for the
individual, rather than the scaled posterior probabilities. Because aberrantly low values
of the genotype log-likelihood can indicate that there is something wrong. However, the raw likelihood
that you get will depend on the number of missing loci, etc. rubias
deals with this
by computing a z-score for each fish. The Z-score is the Z statistic obtained
from the fish's log-likelihood (by subtracting from it the expected log-likelihood and
dividing by the expected standard deviation). rubias
's implementation of the z-score
accounts for the pattern of missing data, but it does this without all the simulation that
gsi_sim
does. This makes it much, much, faster---fast
enough that we can compute it be default for every fish and every population.
Here, we will look at the z-score computed for each fish to the population with the highest posterior. (It is worth noting that you would never want to use the z-score to assign fish to different populations---it is only there to decide whether it looks like it might not have actually come from the population that it was assigned to, or any other population in the reference data set.)
# get the maximum-a-posteriori population for each individual map_rows <- mix_est$indiv_posteriors %>% group_by(indiv) %>% top_n(1, PofZ) %>% ungroup()
If everything is kosher, then we expect that the z-scores we see will be roughly normally distributed. We can compare the distribution of z-scores we see with a bunch of simulated normal random variables.
normo <- tibble(z_score = rnorm(1e06)) ggplot(map_rows, aes(x = z_score)) + geom_density(colour = "blue") + geom_density(data = normo, colour = "black")
The normal density is in black and the distribution of our observed z_scores is in blue. They fit reasonably well, suggesting that there is not too much weird stuff going on overall. (That is good!)
The z_score statistic is most useful as a check for individuals. It is intended to be a quick way to identify aberrant individuals. If you see a z-score to the maximum-a-posteriori population for an individual in your mixture sample that is considerably less than z_scores you saw in the reference, then you might infer that the individual doesn't actually fit any of the populations in the reference well.
Here I include a small, contrived example. We use the small_chinook
data set so that it goes
fast.
First, we analyze the data with no fish in the mixture of known collection
no_kc <- infer_mixture(small_chinook_ref, small_chinook_mix, gen_start_col = 5)
And look at the results for the mixing proportions:
no_kc$mixing_proportions %>% arrange(mixture_collection, desc(pi))
Now, we will do the same analysis, but pretend that we know that the first 8 of the 36 fish in fishery rec1 are from the Deer_Cr_sp collection.
First we have to add the known_collection column to the reference.
# make reference file that includes the known_collection column kc_ref <- small_chinook_ref %>% mutate(known_collection = collection) %>% select(known_collection, everything()) # see what that looks like kc_ref[1:10, 1:8]
Then we add the known collection column to the mixture. We start out making it all NAs, and then we change that to Deer_Cr_sp for 8 of the rec1 fish:
kc_mix <- small_chinook_mix %>% mutate(known_collection = NA) %>% select(known_collection, everything()) kc_mix$known_collection[kc_mix$collection == "rec1"][1:8] <- "Deer_Cr_sp" # here is what that looks like now (dropping most of the genetic data columns) kc_mix[1:20, 1:7]
And now we can do the mixture analysis:
# note that the genetic data start in column 6 now with_kc <- infer_mixture(kc_ref, kc_mix, 6)
And, when we look at the estimated proportions, we see that for rec1 they reflect the fact that 8 of those fish were singled out as known fish from Deer_Cr_sp:
with_kc$mixing_proportions %>% arrange(mixture_collection, desc(pi))
The output from infer_mixture()
in this case can be used just like it was before
without known individuals in the baseline.
The default model in rubias
is a conditional model in which inference is done
with the baseline allele counts fixed. In a fully Bayesian version, fish from within
the mixture that are allocated (on any particular step of the MCMC) to one of the reference
samples have their alleles added to that reference sample, thus (one hopes) refining the
estimate of allele frequencies in that sample. This is more computationally intensive, and, is
done using parallel computation, by default running one thread for every core on your machine.
The basic way to invoke the fully Bayesian model is to use the infer_mixture
function with
the method
option set to "BR". For example:
full_model_results <- infer_mixture( reference = chinook, mixture = chinook_mix, gen_start_col = 5, method = "BR" )
More details about different options for working with the fully Bayesian model are available in the vignette about the fully Bayesian model.
A standard analysis in molecular ecology is to assign individuals in the reference
back to the collections in the reference using a leave-one-out procedure.
This is taken care of by the self_assign()
function.
sa_chinook <- self_assign(reference = chinook, gen_start_col = 5)
Now, you can look at the self assignment results:
head(sa_chinook, n = 100)
The log_likelihood
is the log probability of the fish's genotype given it is from the inferred_collection
computed using leave-one-out. The scaled_likelihood
is the posterior prob of assigning the fish to the
inferred_collection
given an equal prior on every collection in the reference. Other columns are as
in the output for infer_mixture()
. Note that the z_score
computed here can be used to
assess the distribution of the z_score
statistic for fish from known, reference populations. This can
be used to compare to values obtained in mixed fisheries.
The output can be summarized by repunit as was done above:
sa_to_repu <- sa_chinook %>% group_by(indiv, collection, repunit, inferred_repunit) %>% summarise(repu_scaled_like = sum(scaled_likelihood)) head(sa_to_repu, n = 200)
If you want to know how much accuracy you can expect given a set of genetic markers and
a grouping of populations (collection
s) into reporting units (repunit
s), there are two
different functions you might use:
assess_reference_loo()
: This function carries out simulation of mixtures using the leave-one-out
approach of Anderson et al. (2008).assess_reference_mc()
: This functions breaks the reference data set into different subsets, one of which
is used as the reference data set and the other the mixture. It is difficult to simulate very large mixture
samples using this method, because it is constrained by the number of fish in the reference data set.Both of the functions take two required arguments: 1) a data frame of reference genetic data, and 2) the number of the column in which the genetic data start.
Here we use the chinook
data to simulate 5 mixture samples (note, you will typically
want to do more than just 5, but we use low numbers here for illustration) of size 200 fish
using the default values (Dirichlet parameters of 1.5 for each reporting unit, and
Dirichlet parameters of 1.5 for each collection within a reporting unit...)
chin_sims <- assess_reference_loo(reference = chinook, gen_start_col = 5, reps = 5, mixsize = 200)
Here is what the output looks like:
chin_sims
The columns here are:
repunit_scenario
and integer that gives that repunit simulation parameters (see below about simulating
multiple scenarios).collections_scenario
and integer that gives that collection simulation paramters (see below about simulating
multiple scenarios).iter
the simulation number (1 up to reps
)repunit
the reporting unitcollection
the collectiontrue_pi
the true simulated mixing proportionn
the actual number of fish from the collection in the simulated mixture.post_mean_pi
the posterior mean of mixing proportion.mle_pi
the maximum likelihood of pi
obtained using an EM-algorithm.assess_reference_loo()
By default, each iteration, the proportions of fish from each reporting unit are simulated from a Dirichlet distribution with parameter (1.5,...,1.5). And, within each reporting unit the mixing proportions from different collections are drawn from a Dirichlet distribution with parameter (1.5,...,1.5).
The value of 1.5 for the Dirichlet parameter for reporting units can be changed using the
alpha_repunit
. The Dirichlet parameter for collections can be set using the alpha_collection
parameter.
Sometimes, however, more control over the composition of the simulated mixtures is desired. This is achieved
by passing a two-column data.frame to either alpha_repunit
or alpha_collection
(or both). If you are
passing the data.frame in for alpha_repunit
, the first column must be named repunit
and it must contain
a character vector specifying reporting units. In the data.frame for alpha_collection
the first column must be
named collection
and must hold a character vector specifying different collections. It is an error if
a repunit or collection is specified that does not exist in the reference. However, you do not need to
specify a value for every reporting unit or collection. (If they are absent, the value is assumed to be zero.)
The second column of the data frame must be one of count
, ppn
or dirichlet
. These specify, respectively,
ppn
values will be normalized to sum to one if they do not. As such, they can be regarded as weights. Let's say that we want to simulate data that roughly have proportions like what we saw in the
Chinook rec1
fishery. We have those estimates in the variable top6
:
top6
We could, if we put those repprop
values into a ppn
column, simulate mixtures with
exactly those proportions. Or if we wanted to simulate exact numbers of fish in a sample
of r sum(round(top6$repprop * 350))
fish, we could get those values like this:
round(top6$repprop * 350)
and then put them in a cnts
column.
However, in this case, we want to simulate mixtures that look similar to the one we
estimated, but have some variation. For that we will want to supply Dirichlet random variable
parameters in a column named dirichlet
. If we make the values proportional to the mixing proportions,
then, on average that is what they will be. If the values are large, then there will be little
variation between simulated mixtures. And if the the values are small there will be lots of variation.
We'll scale them so that they sum to 10---that should give some variation, but not too much. Accordingly the
tibble that we pass in as the alpha_repunit
parameter, which describes the variation in reporting unit
proportions we would like to simulate would look like this:
arep <- top6 %>% ungroup() %>% mutate(dirichlet = 10 * repprop) %>% select(repunit, dirichlet) arep
Let's do some simulations with those repunit parameters. By default, if we don't specify anything extra for the collections, they get dirichlet parameters of 1.5.
chin_sims_repu_top6 <- assess_reference_loo(reference = chinook, gen_start_col = 5, reps = 5, mixsize = 200, alpha_repunit = arep)
Now, we can summarise the output by reporting unit...
# now, call those repunits that we did not specify in arep "OTHER" # and then sum up over reporting units tmp <- chin_sims_repu_top6 %>% mutate(repunit = ifelse(repunit %in% arep$repunit, repunit, "OTHER")) %>% group_by(iter, repunit) %>% summarise(true_repprop = sum(true_pi), reprop_posterior_mean = sum(post_mean_pi), repu_n = sum(n)) %>% mutate(repu_n_prop = repu_n / sum(repu_n))
...and then plot it for the values we are interested in:
# then plot them ggplot(tmp, aes(x = true_repprop, y = reprop_posterior_mean, colour = repunit)) + geom_point() + geom_abline(intercept = 0, slope = 1) + facet_wrap(~ repunit)
Or plot comparing to their "n" value, which is the actual number of fish from each reporting unit in the sample.
ggplot(tmp, aes(x = repu_n_prop, y = reprop_posterior_mean, colour = repunit)) + geom_point() + geom_abline(intercept = 0, slope = 1) + facet_wrap(~ repunit)
Quite often you might be curious about how much you can expect to be able to trust
the posterior for individual fish from a mixture like this. You can retrieve all
the posteriors computed for the fish simulated in assess_reference_loo()
using the
return_indiv_posteriors
option. When you do this, the function returns a list
with components mixture_proportions
(which holds a tibble like chin_sims_repu_top6
in the previous section) and indiv_posteriors
, which holds all the posteriors (PofZ
s) for
the simulated individuals.
set.seed(100) chin_sims_with_indivs <- assess_reference_loo(reference = chinook, gen_start_col = 5, reps = 5, mixsize = 200, alpha_repunit = arep, return_indiv_posteriors = TRUE) # print out the indiv posteriors chin_sims_with_indivs$indiv_posteriors
In this tibble:
- indiv
is an integer specifier of the simulated individual
- simulated_repunit
is the reporting unit the individual was simulated from
- simulated_collection
is the collection the simulated genotype came from
- PofZ
is the mean over the MCMC of the posterior probability that the
individual originated from the collection.
Now that we have done that, we can see what the distribution of posteriors to the correct reporting unit is for fish from the different simulated collections. We'll do that with a boxplot, coloring by repunit:
# summarise things repu_pofzs <- chin_sims_with_indivs$indiv_posteriors %>% filter(repunit == simulated_repunit) %>% group_by(iter, indiv, simulated_collection, repunit) %>% # first aggregate over reporting units summarise(repu_PofZ = sum(PofZ)) %>% ungroup() %>% arrange(repunit, simulated_collection) %>% mutate(simulated_collection = factor(simulated_collection, levels = unique(simulated_collection))) # also get the number of simulated individuals from each collection num_simmed <- chin_sims_with_indivs$indiv_posteriors %>% group_by(iter, indiv) %>% slice(1) %>% ungroup() %>% count(simulated_collection) # note, the last few steps make simulated collection a factor so that collections within # the same repunit are grouped together in the plot. # now, plot it ggplot(repu_pofzs, aes(x = simulated_collection, y = repu_PofZ)) + geom_boxplot(aes(colour = repunit)) + geom_text(data = num_simmed, mapping = aes(y = 1.025, label = n), angle = 90, hjust = 0, vjust = 0.5, size = 3) + theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 9, vjust = 0.5)) + ylim(c(NA, 1.05))
Great. That is helpful.
By default, individuals are simulated in assess_reference_loo()
by resampling full
multilocus genotypes. This tends to be more realistic, because it includes as missing
in the simulations all the
missing data for individuals in the reference. However, as all the genes in
individuals that have been incorrectly placed in a reference stay together,
that individual might have a low value of PofZ to the population it was simulated
from. Due to the latter issue, it might also yield a more pessimistic assessment'
of the power for GSI.
An alternative is to resample over gene copies---the CV-GC method of Anderson et al. (2008).
Let us do that and see how the simulated PofZ results change. Here we do the simulations. Note, we do only 5 reps so that we don't take too long to generate this vignette. But you should do more reps, typically.
set.seed(101) # for reproducibility # do the simulation chin_sims_by_gc <- assess_reference_loo(reference = chinook, gen_start_col = 5, reps = 5, mixsize = 200, alpha_repunit = arep, return_indiv_posteriors = TRUE, resampling_unit = "gene_copies")
and here we process the output and plot it:
# summarise things repu_pofzs_gc <- chin_sims_by_gc$indiv_posteriors %>% filter(repunit == simulated_repunit) %>% group_by(iter, indiv, simulated_collection, repunit) %>% # first aggregate over reporting units summarise(repu_PofZ = sum(PofZ)) %>% ungroup() %>% arrange(repunit, simulated_collection) %>% mutate(simulated_collection = factor(simulated_collection, levels = unique(simulated_collection))) # also get the number of simulated individuals from each collection num_simmed_gc <- chin_sims_by_gc$indiv_posteriors %>% group_by(iter, indiv) %>% slice(1) %>% ungroup() %>% count(simulated_collection) # note, the last few steps make simulated collection a factor so that collections within # the same repunit are grouped together in the plot. # now, plot it ggplot(repu_pofzs_gc, aes(x = simulated_collection, y = repu_PofZ)) + geom_boxplot(aes(colour = repunit)) + geom_text(data = num_simmed_gc, mapping = aes(y = 1.025, label = n), angle = 90, hjust = 0, vjust = 0.5, size = 3) + theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 9, vjust = 0.5)) + ylim(c(NA, 1.05))
And in that, we find somewhat fewer fish that have low posteriors, but there are still some. This reminds us that with this dataset, (rather) occasionally it is possible to get individuals carrying genotypes that make it difficult to correctly assign them to reporting unit.
If you are simulating the reporting unit proportions or numbers, and want to
have more control over which collections those fish are simulated from, within
the reporting units, then the sub_ppn
and sub_dirichlet
settings are for you.
These are given as column names in the alpha_collection
data frame.
For example, let's say we want to simulate reporting unit proportions as before,
using arep
from above:
arep
But, now, let's say that within reporting unit we want specific weights for different collections. Then we could specify those, for example, like this:
arep_subs <- tribble( ~collection, ~sub_ppn, "Eel_R", 0.1, "Russian_R", 0.9, "Butte_Cr_fa", 0.7, "Feather_H_sp", 0.3 )
Collections that are not listed are given equal proportions within repunits that had no collections listed. However, if a collection is not listed, but other collections within its repunit are, then its simulated proportion will be zero. (Technically, it is not zero, but it is so small---like $10^{-8}$ that is is effectively 0...doing that made coding it up a lot easier...)
Now, we can simulate with that and see what the resulting proportion of fish from each collection is (once again with only 5 reps):
chin_sims_sub_ppn <- assess_reference_loo(reference = chinook, gen_start_col = 5, reps = 5, mixsize = 200, alpha_repunit = arep, alpha_collection = arep_subs, return_indiv_posteriors = FALSE) # don't bother returning individual posteriors
Now observe the average proportions of the collections and repunits that were simulated, and the average fraction, within reporting units of each of the collection
chin_sims_sub_ppn %>% group_by(repunit, collection) %>% summarise(mean_pi = mean(true_pi)) %>% group_by(repunit) %>% mutate(repunit_mean_pi = sum(mean_pi), fract_within = mean_pi / repunit_mean_pi) %>% mutate(fract_within = ifelse(fract_within < 1e-06, 0, fract_within)) %>% # anything less than 1 in a million gets called 0 filter(repunit_mean_pi > 0.0)
In the fisheries world, "100% simulations" have been a staple. In these simulations,
mixtures are simulated in which 100% of the individuals are from one collection (or
reporting unit, I suppose). Eric has never been a big fan of these since they don't
necessarily tell you how you might do inferring actual mixtures that you might encounter.
Nonetheless, since they have been such a mainstay in the field, it is worthwile showing
how to do 100% simulations using rubias
. Furthermore, when people asked for this feature
it made it clear that Eric had to provide a way to simulate multiple
different scenarios without re-processing the reference data set each time. So this is what
I came up with: the way we do it is to pass a list of
scenarios to the alpha_repunit
or alpha_collection
option in assess_reference_loo()
.
These can be named lists, if desired. So, for example, let's do 100% simulations for each
of the repunits in arep
:
arep$repunit
We will let the collections within them just be drawn from a dirichlet distribution with parameter 10 (so, pretty close to equal proportions).
So, to do this, we make a list of data frames with the proportions. We'll give it some names too:
six_hundy_scenarios <- lapply(arep$repunit, function(x) tibble(repunit = x, ppn = 1.0)) names(six_hundy_scenarios) <- paste("All", arep$repunit, sep = "-")
Then, we use it, producing only 5 replicates for each scenario:
repu_hundy_results <- assess_reference_loo(reference = chinook, gen_start_col = 5, reps = 5, mixsize = 50, alpha_repunit = six_hundy_scenarios, alpha_collection = 10) repu_hundy_results
Just to make sure that it is clear how to do this with collections (rather than reporting units) as well, lets do 100% simulations for a handful of the collections. Let's just randomly take 5 of them, and do 6 reps for each:
set.seed(10) hundy_colls <- sample(unique(chinook$collection), 5) hundy_colls
So, now make a list of those with 100% specifications in the tibbles:
hundy_coll_list <- lapply(hundy_colls, function(x) tibble(collection = x, ppn = 1.0)) %>% setNames(paste("100%", hundy_colls, sep = "_"))
Then, do it:
hundy_coll_results <- assess_reference_loo(reference = chinook, gen_start_col = 5, reps = 5, mixsize = 50, alpha_collection = hundy_coll_list) hundy_coll_results
These are obtained using method = "PB"
in infer_mixture()
. When invoked, this will return the
regular MCMC results as before, but also will population the bootstrapped_proportions
field
of the output. Doing so takes a little bit longer, computationally, because there is a good deal
of simulation involved, so this doesn't get evaluated in the vignette.
mix_est_pb <- infer_mixture(reference = chinook, mixture = chinook_mix, gen_start_col = 5, method = "PB")
And now we can compare the estimates, showing here the 10 most prevalent
repunits, in the rec1
fishery:
mix_est_pb$mixing_proportions %>% group_by(mixture_collection, repunit) %>% summarise(repprop = sum(pi)) %>% left_join(mix_est_pb$bootstrapped_proportions) %>% ungroup() %>% filter(mixture_collection == "rec1") %>% arrange(desc(repprop)) %>% slice(1:10)
You can give that a whirl and see that it gives us a result that we expect: no appreciable difference, because the reporting units are already very well resolved, so we don't expect that the parametric bootstrap procedure would find any benefit in correcting them.
Anderson, Eric C, Robin S Waples, and Steven T Kalinowski. 2008. βAn Improved Method for Predicting the Accuracy of Genetic Stock Identification.β Can J Fish Aquat Sci 65:1475β86.
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