| Title: | Iterative Extrapolation of Species' Haplotype Accumulation Curves for Genetic Diversity Assessment |
|---|---|
| Description: | Performs iterative extrapolation of species' haplotype accumulation curves using a nonparametric stochastic (Monte Carlo) optimization method for assessment of specimen sampling completeness based on the approach of Phillips et al. (2015) <doi:10.1515/dna-2015-0008>, Phillips et al. (2019) <doi:10.1002/ece3.4757> and Phillips et al. (2020) <doi: 10.7717/peerj-cs.243>. 'HACSim' outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species' haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide substitution. A Shiny app is also available. |
| Authors: | Jarrett D. Phillips [aut, cre], Steven H. French [ctb], Navdeep Singh [ctb] |
| Maintainer: | Jarrett D. Phillips <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.6 |
| Built: | 2025-03-26 05:34:08 UTC |
| Source: | https://github.com/jphill01/hacsim.r |
HACSim (Haplotype Accumulation Curve Simulator) employs a novel nonparametric stochastic (Monte Carlo) optimization method of iteratively generating species' haplotype accumulation curves through extrapolation to assess sampling completeness based on the approach outlined in Phillips et al. (2015) <doi:10.1515/dna-2015-0008>, Phillips et al. (2019) <doi:10.1002/ece3.4757> and Phillips et al. (2020) <doi: 10.7717/peerj-cs.243>. HACSim outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species' haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide substitution. A Shiny app is also available.
The DESCRIPTION file:
| Package: | HACSim |
| Type: | Package |
| Title: | Iterative Extrapolation of Species' Haplotype Accumulation Curves for Genetic Diversity Assessment |
| Version: | 1.0.6 |
| Date: | 2022-05-23 |
| Author: | Jarrett D. Phillips [aut, cre], Steven H. French [ctb], Navdeep Singh [ctb] |
| Maintainer: | Jarrett D. Phillips <[email protected]> |
| Description: | Performs iterative extrapolation of species' haplotype accumulation curves using a nonparametric stochastic (Monte Carlo) optimization method for assessment of specimen sampling completeness based on the approach of Phillips et al. (2015) <doi:10.1515/dna-2015-0008>, Phillips et al. (2019) <doi:10.1002/ece3.4757> and Phillips et al. (2020) <doi: 10.7717/peerj-cs.243>. 'HACSim' outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species' haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide substitution. A Shiny app is also available. |
| License: | GPL-3 |
| URL: | <https://github.com/jphill01/HACSim.R> <https://github.com/jphill01/HACSim-RShiny-App> <https://jphill01.shinyapps.io/HACSim> |
| NeedsCompilation: | yes |
| Imports: | ape (>= 5.3), data.table (>= 1.12.8), graphics (>= 3.6.1), matrixStats (>= 0.56.0), pegas (>= 0.13), Rcpp (>= 1.0.3), shiny (>= 1.6.0), stats (>= 3.6.1), utils (>= 3.6.1) |
| LinkingTo: | Rcpp, RcppArmadillo |
| RoxygenNote: | 6.1.1 |
| Packaged: | 2019-10-23 16:00:58 UTC; jarrettphillips |
| Config/pak/sysreqs: | make zlib1g-dev |
| Repository: | https://jphill01.r-universe.dev |
| RemoteUrl: | https://github.com/jphill01/hacsim.r |
| RemoteRef: | HEAD |
| RemoteSha: | f13af60f2ce9ebd8f06104bbbcd0380d33980ba6 |
Index of help topics:
HAC.sim Internal R code
HAC.simrep Run a simulation of haplotype accumulation
curves for hypothetical or real species
HACClass Internal R code
HACHypothetical Set up an object to simulate haplotype
accumulation curves for a hypothetical species
HACReal Set up an object to simulate haplotype
accumulation curves for a real species
HACSim-package Iterative Extrapolation of Species' Haplotype
Accumulation Curves for Genetic Diversity
Assessment
accumulate Internal C++ code
envr Simulation variable storage environment
launchApp Launch HACSim R Shiny web app
sim.seqs Simulate DNA sequences according to DNA
substitution models
Jarrett D. Phillips [aut, cre], Steven H. French [ctb], Navdeep Singh [ctb]
Maintainer: Jarrett D. Phillips <[email protected]>
Phillips, J.D., Gwiazdowski, R.A., Ashlock, D. and Hanner, R. (2015). An exploration of sufficient sampling effort to describe intraspecific DNA barcode haplotype diversity: examples from the ray-finned fishes (Chordata: Actinopterygii). DNA Barcodes, 3: 66-73.
Phillips, J.D., Gillis, D.J. and Hanner, R.H. (2019). Incomplete estimates of genetic diversity within species: Implications for DNA barcoding. Ecology and Evolution, 9(5): 2996-3010.
Phillips, J.D., Gillis, D.J. and Hanner, R.H. (2020). HACSim: An R package to estimate intraspecific sample sizes for genetic diversity assessment using haplotype accumulation curves. PeerJ Computer Science
## Simulate hypothetical species ## N <- 100 # total number of sampled individuals Hstar <- 10 # total number of haplotypes probs <- rep(1/Hstar, Hstar) # equal haplotype frequency distribution HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, filename = "output") # outputs a CSV # file called "output.csv" ## Simulate hypothetical species - subsampling ## HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, perms = 1000, p = 0.95, subsample = TRUE, prop = 0.25, conf.level = 0.95, filename = "output") ## Simulate hypothetical species and all paramaters changed - subsampling ## HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.95, filename = "output") HAC.simrep(HACSObj) # runs a simulation ## Simulate real species ## ## Not run: ## Simulate real species ## # outputs file called "output.csv" HACSObj <- HACReal(filename = "output") ## Simulate real species - subsampling ## HACSObj <- HACReal(subsample = TRUE, prop = 0.15, conf.level = 0.95, filename = "output") ## Simulate real species and all parameters changed - subsampling ## HACSObj <- HACReal(perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.99, filename = "output") # user prompted to select appropriate FASTA file HAC.simrep(HACSObj) ## End(Not run) ## Not run: ## Simulate DNA sequences ## num.seqs <- 100 # number of DNA sequences num.haps <- 15 # number of haplotypes length.seqs <- 658 # length of DNA sequences count.haps <- c(60, rep(10, 2), rep(5, 2), rep(1, 5)) # haplotype frequency distribution nucl.freqs <- rep(0.25, 4) # nucleotide frequency distribution subst.model <- "JC69" # desired nucleotide substitution model mu.rate <- 1e-3 # mutation rate transi.rate <- NULL # transition rate transv.rate <- NULL # transversion rate sim.seqs(num.seqs = num.seqs, num.haps = num.haps, length.seqs = length.seqs, nucl.freqs = nucl.freqs, count.haps = count.haps, subst.model = subst.model, transi.rate = transi.rate, transv.rate = transv.rate) # outputs file called "output.csv" HACSObj <- HACReal(filename = "output") ## Simulate DNA sequences - subsampling ## HACSObj <- HACReal(subsample = TRUE, prop = 0.15, conf.level = 0.95, filename = "output") ## Simulate DNA sequences and all parameters changed - subsampling ## HACSObj <- HACReal(perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.99, filename = "output") # user prompted to select appropriate FASTA file HAC.simrep(HACSObj) ## End(Not run)## Simulate hypothetical species ## N <- 100 # total number of sampled individuals Hstar <- 10 # total number of haplotypes probs <- rep(1/Hstar, Hstar) # equal haplotype frequency distribution HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, filename = "output") # outputs a CSV # file called "output.csv" ## Simulate hypothetical species - subsampling ## HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, perms = 1000, p = 0.95, subsample = TRUE, prop = 0.25, conf.level = 0.95, filename = "output") ## Simulate hypothetical species and all paramaters changed - subsampling ## HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.95, filename = "output") HAC.simrep(HACSObj) # runs a simulation ## Simulate real species ## ## Not run: ## Simulate real species ## # outputs file called "output.csv" HACSObj <- HACReal(filename = "output") ## Simulate real species - subsampling ## HACSObj <- HACReal(subsample = TRUE, prop = 0.15, conf.level = 0.95, filename = "output") ## Simulate real species and all parameters changed - subsampling ## HACSObj <- HACReal(perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.99, filename = "output") # user prompted to select appropriate FASTA file HAC.simrep(HACSObj) ## End(Not run) ## Not run: ## Simulate DNA sequences ## num.seqs <- 100 # number of DNA sequences num.haps <- 15 # number of haplotypes length.seqs <- 658 # length of DNA sequences count.haps <- c(60, rep(10, 2), rep(5, 2), rep(1, 5)) # haplotype frequency distribution nucl.freqs <- rep(0.25, 4) # nucleotide frequency distribution subst.model <- "JC69" # desired nucleotide substitution model mu.rate <- 1e-3 # mutation rate transi.rate <- NULL # transition rate transv.rate <- NULL # transversion rate sim.seqs(num.seqs = num.seqs, num.haps = num.haps, length.seqs = length.seqs, nucl.freqs = nucl.freqs, count.haps = count.haps, subst.model = subst.model, transi.rate = transi.rate, transv.rate = transv.rate) # outputs file called "output.csv" HACSObj <- HACReal(filename = "output") ## Simulate DNA sequences - subsampling ## HACSObj <- HACReal(subsample = TRUE, prop = 0.15, conf.level = 0.95, filename = "output") ## Simulate DNA sequences and all parameters changed - subsampling ## HACSObj <- HACReal(perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.99, filename = "output") # user prompted to select appropriate FASTA file HAC.simrep(HACSObj) ## End(Not run)
accumulate comprises internal C++ code employed by
HAC.sim. It is not directly called by the user.
envr is a new (initially empty) environment that is created
when HACSim is loaded.
When a simulation is run via HAC.simrep, envr will contain
26 elements as follows:
ci.type |
Type of confidence interval to compute and plot. Default is
|
conf.level |
The desired confidence level. Default is
|
d |
A dataframe with |
df.out |
A dataframe with |
filename |
The name of the file where results are to be saved. Default is NULL. |
Hstar |
Number of unique species' haplotypes |
input.seqs |
Should DNA sequences be inputted? Default is FALSE. |
iters |
The number of iterations required to reach convergence |
N |
The starting sample size used to initialize the algorithm |
Nstar |
The final (extrapolated) sample size |
Nstar.high |
The upper endpoint of the desired level confidence interval for the 'true' required sample size |
Nstar.low |
The lower endpoint of the desired level confidence interval for the 'true' required sample size |
num.iters |
Number of iterations to compute. |
p |
The user-specified level of haplotype recovery. Default is
|
perms |
The user-specified number of permutations (replications). Default
is |
probs |
Haplotype frequency distribution vector |
progress |
Should iteration results be outputted to the console? Default is TRUE. |
prop.haps |
If |
prop.seqs |
If |
ptm |
A timer to track progress of the algorithm in seconds |
R |
The proportion of haplotypes recovered by the algorithm |
R.low |
The lower endpoint of the desired level confidence interval for the 'true' fraction of haplotypes captured |
R.up |
The upper endpoint of the desired level confidence interval for the 'true' fraction of haplotypes captured |
subset.haps |
Should a subsample of haplotype labels be taken? Default is FALSE. |
subset.seqs |
Should a subsample of DNA sequences be taken? Default is FALSE. |
X |
Mean number of specimens not sampled |
# Returns the frequencies of each haplotype in the extrapolated sample max(envr$d$specs) * envr$probs # Returns the extrapolated sample size corresponding to the dotted line # in the last iteration plot envr$d[which(envr$d$means >= envr$p * envr$Hstar), ][1, 1]# Returns the frequencies of each haplotype in the extrapolated sample max(envr$d$specs) * envr$probs # Returns the extrapolated sample size corresponding to the dotted line # in the last iteration plot envr$d[which(envr$d$means >= envr$p * envr$Hstar), ][1, 1]
HAC.sim comprises internal R code used by HAC.simrep
and is not directly called by the user.
Runs the HACSim algorithm by successively calling
HAC.sim to iteratively extrapolate haplotype accumulation curves to
determine likely specimen sample sizes for hypothetical or real species
The algorithm employs the following iterative methods when calculating the "Measures of Sampling Closeness":
Mean number of haplotype sampled:
Mean number of haplotypes not sampled
Proportion of haplotypes sampled:
Proportion of haplotypes not sampled:
Mean value of :
Mean number of specimens not sampled:
where is stochastically-determined through sampling from
probs, the observed species' haplotype frequency distribution vector.
As the algorithm proceeds, will approach asymptotically
(and hence, will converge to ), but will likely fluctuate
randomly from one iteration to the next. However, estimates of found
at each iteration will be monotonically-increasing.
HAC.simrep(HACSObject)HAC.simrep(HACSObject)
HACSObject |
object containing the desired simulation parameters |
Iteration results are outputted to the console and graphs displayed in
the plot window. Plots depict haplotype accumulation (along with shaded
confidence intervals for the mean number of haplotypes found). Dashed lines
correspond to the endpoint of the curve and reflect haplotype recovery for a
user-defined cutoff (default p = 0.95, 95% haplotype diversity). Output
from the first iteration is useful for judging levels of haplotype diversity
and recovery found in observed intraspecific sequence datasets, reflecting
current sampling depth. The required sample size is displayed in the second-
last iteration. All other information corresponding to the extrapolated sample
size can be found in the last iteration. Iteration results can optionally be
saved to a CSV file. Subsampled DNA sequences are automatically saved to a
FASTA file.
When simulating real species via HACReal(...), a pop-up window will appear prompting the user to select an intraspecific FASTA file of
aligned/trimmed DNA sequences. The alignment must not contain missing or
ambiguous nucleotides (i.e., it should only contain A, C, G or T); otherwise, haplotype diversity may be overestimated. Excluding sequences or alignment
sites with missing/ambiguous data is an option.
## Simulate hypothetical species ## N <- 100 # total number of sampled individuals Hstar <- 10 # total number of haplotypes probs <- rep(1/Hstar, Hstar) # equal haplotype frequency distribution HACSObj <- HACHypothetical(N = N, Hstar = Hstar , probs = probs, filename = "output") # outputs a CSV file called "output.csv" ## Simulate hypothetical species - subsampling ## HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, perms = 1000, p = 0.95, subsample = TRUE, prop = 0.25, conf.level = 0.95, filename = "output") ## Simulate hypothetical species and all paramaters changed - subsampling ## HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.95, filename = "output") try(HAC.simrep(HACSObj)) # runs a simulation ## Simulate real species ## ## Not run: ## Simulate real species ## # outputs file called "output.csv" HACSObj <- HACReal(filename = "output") ## Simulate real species - subsampling ## HACSObj <- HACReal(subsample = TRUE, prop = 0.15, conf.level = 0.95, filename = "output") ## Simulate real species and all parameters changed - subsampling ## HACSObj <- HACReal(perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.99, filename = "output") # user prompted to select appropriate FASTA file try(HAC.simrep(HACSObj)) ## End(Not run)## Simulate hypothetical species ## N <- 100 # total number of sampled individuals Hstar <- 10 # total number of haplotypes probs <- rep(1/Hstar, Hstar) # equal haplotype frequency distribution HACSObj <- HACHypothetical(N = N, Hstar = Hstar , probs = probs, filename = "output") # outputs a CSV file called "output.csv" ## Simulate hypothetical species - subsampling ## HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, perms = 1000, p = 0.95, subsample = TRUE, prop = 0.25, conf.level = 0.95, filename = "output") ## Simulate hypothetical species and all paramaters changed - subsampling ## HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.95, filename = "output") try(HAC.simrep(HACSObj)) # runs a simulation ## Simulate real species ## ## Not run: ## Simulate real species ## # outputs file called "output.csv" HACSObj <- HACReal(filename = "output") ## Simulate real species - subsampling ## HACSObj <- HACReal(subsample = TRUE, prop = 0.15, conf.level = 0.95, filename = "output") ## Simulate real species and all parameters changed - subsampling ## HACSObj <- HACReal(perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.99, filename = "output") # user prompted to select appropriate FASTA file try(HAC.simrep(HACSObj)) ## End(Not run)
HACClass comprises internal R code used to generate an
object used by HAC.simrep. It is not directly called by the user.
Helper function which creates an object containing necessary information to run a simulation of haplotype accumulation for a hypothetical species of interest
HACHypothetical(N, Hstar, probs, perms = 10000, p = 0.95, conf.level = 0.95, ci.type = "quantile", subsample = FALSE, prop = NULL, progress = TRUE, num.iters = NULL, filename = NULL)HACHypothetical(N, Hstar, probs, perms = 10000, p = 0.95, conf.level = 0.95, ci.type = "quantile", subsample = FALSE, prop = NULL, progress = TRUE, num.iters = NULL, filename = NULL)
N |
Number of individuals |
Hstar |
Number of unique species' haplotypes |
probs |
Haplotype frequency distribution vector |
perms |
Number of permutations (replications) |
p |
Proportion of haplotypes to recover |
conf.level |
Desired confidence level for graphical output and interval estimation |
ci.type |
Type of confidence interval for graphical output. Choose from "quantile" or "asymptotic" |
subsample |
Is a subsample of haplotype labels desired? |
prop |
If subsample = TRUE, the proportion of haplotype labels to subsample |
num.iters |
Number of iterations to compute |
progress |
Should iteration output be printed to the R console? |
filename |
Name of file where simulation results are to be saved |
A list object of class "HAC" with 13 elements that can be passed to
HAC.simrep as follows:
input.seqs |
Should a FASTA file of aligned/trimmed DNA sequences be inputted? Default is FALSE |
subset.seqs |
Should a subsample of DNA sequences be taken? Default is FALSE |
prop.seqs |
Proportion of DNA sequences to subsample. Default is NULL |
prop.haps |
Proportion of haplotype labels to subsample. Default is NULL (can be altered by user) |
subset.haps |
Should a subsample of haplotype labels be taken? Default is NULL (can be altered by user) |
N |
Number of individuals. NA by default (provided by user) |
Hstar |
Number of unique species' haplotypes. NA by default (provided by user) |
probs |
Haplotype frequency distribution vector. NA by default (provided by user) |
p |
Proportion of haplotypes to recover. |
perms |
Number of permutations (replications). |
conf.level |
Desired confidence level for graphical output and interval estimation. |
ci.type |
Type of confidence interval for graphical output.
|
num.iters |
Number of iterations to compute. |
progress |
Should iteration output be printed to the R console? Default is TRUE |
filename |
Name of file where simulation results are to be saved. |
N must be greater than 1 and greater than or equal to Hstar.
Hstar must be greater than 1.
probs must have a length equal to Hstar and its elements must sum
to 1.
## Simulate hypothetical species ## N <- 100 # total number of sampled individuals Hstar <- 10 # total number of haplotypes probs <- rep(1/Hstar, Hstar) # equal haplotype frequency distribution # outputs a CSV file called "output.csv" HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, filename = "output") ## Simulate hypothetical species - subsampling ## # subsamples 25% of haplotype labels HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, perms = 1000, p = 0.95, subsample = TRUE, prop = 0.25, conf.level = 0.95, filename = "output") ## Simulate hypothetical species and all paramaters changed - subsampling ## HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.95, num.iters = 1, filename = "output")## Simulate hypothetical species ## N <- 100 # total number of sampled individuals Hstar <- 10 # total number of haplotypes probs <- rep(1/Hstar, Hstar) # equal haplotype frequency distribution # outputs a CSV file called "output.csv" HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, filename = "output") ## Simulate hypothetical species - subsampling ## # subsamples 25% of haplotype labels HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, perms = 1000, p = 0.95, subsample = TRUE, prop = 0.25, conf.level = 0.95, filename = "output") ## Simulate hypothetical species and all paramaters changed - subsampling ## HACSObj <- HACHypothetical(N = N, Hstar = Hstar, probs = probs, perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.95, num.iters = 1, filename = "output")
Helper function which creates an object containing necessary information to run a simulation of haplotype accumulation for a real species of interest
HACReal(perms = 10000, p = 0.95, conf.level = 0.95, ci.type = "quantile", subsample = FALSE, prop = NULL, progress = TRUE, num.iters = NULL, filename = NULL)HACReal(perms = 10000, p = 0.95, conf.level = 0.95, ci.type = "quantile", subsample = FALSE, prop = NULL, progress = TRUE, num.iters = NULL, filename = NULL)
perms |
Number of permutations (replications) |
p |
Proportion of haplotypes to recover |
conf.level |
Desired confidence level for graphical output and interval estimation |
ci.type |
Type of confidence interval for graphical output. Choose from "quantile" or "asymptotic" |
subsample |
Is a subsample of DNA sequences desired? |
prop |
If subsample = TRUE, the proportion of DNA sequences to subsample |
num.iters |
Number of iterations to compute |
progress |
Should iteration output be printed to the R console? |
filename |
Name of file where simulation results are to be saved |
A list object of class "HAC" with 13 elements that can be passed to
HAC.simrep as follows:
input.seqs |
Should a FASTA file of aligned/trimmed DNA sequences be inputted? Default is TRUE |
subset.seqs |
Should a subsample of DNA sequences be taken? Default is FALSE (can be altered by user) |
prop.seqs |
Proportion of DNA sequences to subsample. Default is NA (can be altered by user) |
prop.haps |
Proportion of haplotype labels to subsample. Default is NULL |
subset.haps |
Should a subsample of haplotype labels be taken? Default is NULL |
N |
Number of individuals. NA by default (computed automatically by algorithm) |
Hstar |
Number of unique species' haplotypes. NA by default (computed automatically by algorithm) |
probs |
Haplotype frequency distribution vector. NA by default (computed automatically by algorithm) |
p |
Proportion of haplotypes to recover. |
perms |
Number of permutations (replications). |
conf.level |
Desired confidence level for graphical output and interval estimation. |
ci.type |
Type of confidence interval for graphical output.
|
num.iters |
Number of iterations to compute. |
progress |
Should iteration output be printed to the R console? Default is TRUE |
filename |
Name of file where simulation results are to be saved. |
## Simulate real species ## # outputs file called "output.csv" HACSObj <- HACReal(filename = "output") ## Simulate real species - subsampling ## # subsamples 25% of DNA sequences HACSObj <- HACReal(subsample = TRUE, prop = 0.25, conf.level = 0.95, filename = "output") ## Simulate real species and all parameters changed - subsampling ## HACSObj <- HACReal(perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.99, num.iters = 1, filename = "output")## Simulate real species ## # outputs file called "output.csv" HACSObj <- HACReal(filename = "output") ## Simulate real species - subsampling ## # subsamples 25% of DNA sequences HACSObj <- HACReal(subsample = TRUE, prop = 0.25, conf.level = 0.95, filename = "output") ## Simulate real species and all parameters changed - subsampling ## HACSObj <- HACReal(perms = 10000, p = 0.90, subsample = TRUE, prop = 0.15, conf.level = 0.99, num.iters = 1, filename = "output")
Launch HACSim R Shiny web app locally on a user's R session
launchApp()launchApp()
Simulates DNA sequences according to various DNA substitution models:
Jukes-Cantor (1969)
Kimura (1980)
Felsenstein (1981)
Hasegawa-Kishino-Yano (1985)
sim.seqs(num.seqs, num.haps, length.seqs, count.haps, nucl.freqs, codon.tbl = c("standard", "vertebrate mitochondrial", "invertebrate mitochondrial"), subst.model = c("JC69", "K80", "F81", "HKY85"), mu.rate, transi.rate, transv.rate)sim.seqs(num.seqs, num.haps, length.seqs, count.haps, nucl.freqs, codon.tbl = c("standard", "vertebrate mitochondrial", "invertebrate mitochondrial"), subst.model = c("JC69", "K80", "F81", "HKY85"), mu.rate, transi.rate, transv.rate)
num.seqs |
Number of simulated DNA sequences |
num.haps |
Number of simulated unique species' haplotypes |
length.seqs |
Basepair length of DNA sequences |
count.haps |
Haplotype frequency distribution vector |
nucl.freqs |
Nucleotide frequency distribution vector of A, C, G, and T respectively |
codon.tbl |
Codon table |
subst.model |
Model of DNA substitution |
mu.rate |
Overall nucleotide mutation rate/site/generation |
transi.rate |
Nucleotide transition rate/site/generation |
transv.rate |
Nucleotide transversion rate/site/generation |
A FASTA file of DNA sequences
num.seqs must be greater than or equal to num.haps.
Both num.seqs and num.haps must be greater than 1.
nucl.freqs must have a length of four and its elements must sum to 1.
count.haps must have a length of num.haps and its elements must
sum to num.seqs.
subst.model must be one of "JC69" (Jukes Cantor corrected p-distance),
"K80" (Kimura-2-Parameter (K2P), "F81" (Felenstein) or "HKY85"
(Hasegawa-Kishino-Yano)
mu.rate must be specified for both "JC69" and "F81" models
transi.rate and transv.rate must be specified for both "K80"
and "HKY85" models
All elements nucl.freqs must be equal to 0.25 when subst.model
is either "JC69" or "K80"
All elements nucl.freqs must differ from 0.25 when subst.model
is either "F81" or "HKY85"
## Not run: # Simulate DNA sequences from the 5'-COI DNA barcode region under a Jukes # Cantor nucleotide substitution model num.seqs <- 100 # number of DNA sequences num.haps <- 10 # number of haplotypes length.seqs <- 658 # length of DNA sequences count.haps <- c(60, rep(10, 2), rep(5, 2), rep(1, 5)) # haplotype frequency distribution nucl.freqs <- rep(0.25, 4) # nucleotide frequency distribution codon.tbl <- "vertebrate mitochondrial" subst.model <- "JC69" # desired nucleotide substitution model mu.rate <- 1e-3 # mutation rate transi.rate <- NULL # transition rate transv.rate <- NULL # transversion rate sim.seqs(num.seqs = num.seqs, num.haps = num.haps, length.seqs = length.seqs, count.haps = count.haps, nucl.freqs = nucl.freqs, subst.model = subst.model, codon.tbl = codon.tbl, transi.rate = transi.rate, transv.rate = transv.rate) ## End(Not run)## Not run: # Simulate DNA sequences from the 5'-COI DNA barcode region under a Jukes # Cantor nucleotide substitution model num.seqs <- 100 # number of DNA sequences num.haps <- 10 # number of haplotypes length.seqs <- 658 # length of DNA sequences count.haps <- c(60, rep(10, 2), rep(5, 2), rep(1, 5)) # haplotype frequency distribution nucl.freqs <- rep(0.25, 4) # nucleotide frequency distribution codon.tbl <- "vertebrate mitochondrial" subst.model <- "JC69" # desired nucleotide substitution model mu.rate <- 1e-3 # mutation rate transi.rate <- NULL # transition rate transv.rate <- NULL # transversion rate sim.seqs(num.seqs = num.seqs, num.haps = num.haps, length.seqs = length.seqs, count.haps = count.haps, nucl.freqs = nucl.freqs, subst.model = subst.model, codon.tbl = codon.tbl, transi.rate = transi.rate, transv.rate = transv.rate) ## End(Not run)