diff --git a/docs/articles/index.html b/docs/articles/index.html index 4f1a4ad2..41604d03 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -23,7 +23,8 @@ - + + @@ -51,10 +52,28 @@
plot.phylo(upgma(xdis))
plot.phylo(upgma(xdis))
Therefore, we might want to consider “new”, “mut” and, “C” to be the same Multilocus Lineage (MLL). In the next section, you will see how to collapse multilocus genotypes by genetic distance.
citation(package = "poppr")
##
## To cite poppr in publications or presentations, please specify
-## poppr version 2.5.0 and with the following citation:
+## poppr version 2.6.0 and with the following citation:
##
## Kamvar ZN, Tabima JF, Grünwald NJ. (2014) Poppr: an R package
## for genetic analysis of populations with clonal, partially
@@ -166,7 +184,7 @@
To install from GitHub, you do not need to download the tarball since there is a package called devtools that will download and install the package for you directly from GitHub. After you have installed all dependencies (see above section), you should download devtools:
install.packages("devtools")
Now you can execute the command install_github()
with the user and repository name:
-devtools::install_github("grunwaldlab/poppr")
+devtools::install_github("grunwaldlab/poppr")
## This is poppr version 2.5.0. To get started, type package?poppr
+## This is poppr version 2.6.0. To get started, type package?poppr
## OMP parallel support: available
x <- getfile()
A pop up window will appear like this1:
@@ -1378,8 +1396,8 @@
## @pop: population of each individual (group size range: 1-646)
## @strata: a data frame with 17 columns ( accession, length, host, segment, subtype, country, ... )
## @other: a list containing: x xy epid
-If we need to store the number of MLGs as a variable, we can simply run the mlg()
command.
H3N2_mlg <- mlg(H3N2)
If we need to store the number of MLGs as a variable, we can simply run the mlg()
command.
H3N2_mlg <- mlg(H3N2)
## #############################
## # Number of Individuals: 1903
## # Number of MLG: 752
@@ -1508,7 +1526,7 @@
## // Observed heterozygosity: 0
library("vegan")
H.year <- mlg.table(H3N2, plot = FALSE)
-rarecurve(H.year, ylab="Number of expected MLGs", sample=min(rowSums(H.year)),
+rarecurve(H.year, ylab="Number of expected MLGs", sample=min(rowSums(H.year)),
border = NA, fill = NA, font = 2, cex = 1, col = "blue")
Poppr utilizes ggplot2 to produce many of its graphs. One advantage it gives the user is the ability to manipulate these graphs. With base R graphs, the only manipulation that can be performed is by adding elements to the graph. It is a static image. The ggplot graphs are actually represented as objects in your R environment. We can use the function last_plot()
from ggplot2 to be able to grab the plot that was plotted last in our window. Let’s illustrate this using a MLG bar graph from the Aeut data set.
Poppr utilizes ggplot2 to produce many of its graphs. One advantage it gives the user is the ability to manipulate these graphs. With base R graphs, the only manipulation that can be performed is by adding elements to the graph. It is a static image. The ggplot graphs are actually represented as objects in your R environment. We can use the function last_plot()
from ggplot2 to be able to grab the plot that was plotted last in our window. Let’s illustrate this using a MLG bar graph from the Aeut data set.
library("poppr")
library("ggplot2")
data(Aeut)
Aeut.tab <- mlg.table(Aeut)
p <- last_plot()
We’ve captured our plot using last_plot()
and now we can manipulate it. One common need is to change the title. We can easily do that with the function ggtitle()
. Let“s say we wanted to label it”Aphanomyces euteiches multilocus genotype distribution“. We would use ggtitle(Aphanomyces euteiches multilocus genotype distribution)
. Unfortunately, we need italics for a latin binomial. One way to acheive this is by using the expression()
function and declaring which text needs to be italicized.
p <- last_plot()
We’ve captured our plot using last_plot()
and now we can manipulate it. One common need is to change the title. We can easily do that with the function ggtitle()
. Let“s say we wanted to label it”Aphanomyces euteiches multilocus genotype distribution“. We would use ggtitle(Aphanomyces euteiches multilocus genotype distribution)
. Unfortunately, we need italics for a latin binomial. One way to acheive this is by using the expression()
function and declaring which text needs to be italicized.
myTitle <- expression(paste(italic("Aphanomyces euteiches"),
" multilocus genotype distribution"))
(pt <- p +
- ggtitle(myTitle) +
- xlab("Multilocus genotype")) # We can label the x axis, too
Let“s say we wanted to remove the grey background. We could use the theme()
function to do this, or we could use a theme already implemented in ggplot2 called theme_bw()
.
(ptt <- pt + theme_bw())
Let“s say we wanted to remove the grey background. We could use the theme()
function to do this, or we could use a theme already implemented in ggplot2 called theme_bw()
.
(ptt <- pt + theme_bw())
Uh-oh. The x axis labels are now horizontal when they should be vertical. Since it“s the overall distribution we’re interested in, we don’t really need them anyways. We can remove them with axis.text.x
and axis.ticks.x
(we’ll also remove the x axis gridlines because they’re ugly).
(ptta <- ptt + theme(axis.text.x = element_blank()) +
- theme(axis.ticks.x = element_blank()) +
- theme(panel.grid.major.x = element_blank()))
(ptta <- ptt + theme(axis.text.x = element_blank()) +
+ theme(axis.ticks.x = element_blank()) +
+ theme(panel.grid.major.x = element_blank()))
And, if for some bizarre reason, you liked the color gradient in poppr version 1, you can get that back by adding the fill aesthetic:
-(ptttaf <- ptta + aes(fill = count))
(ptttaf <- ptta + aes(fill = count))
This allows you to produce publication quality graphs directly in R. Please see Hadley Wickham“s ggplot2 package for more details (Wickham 2009). Note that if you don”t like using ggplot2, you can access the data in the ggplot2 object and plot the data yourself:
head(p$data)
## # A tibble: 6 x 4
-## Population MLG count order
-## <fctr> <chr> <int> <fctr>
-## 1 Athena MLG.20 9 1
-## 2 Athena MLG.66 5 2
-## 3 Athena MLG.14 3 3
-## 4 Athena MLG.35 3 4
-## 5 Athena MLG.13 2 5
-## 6 Athena MLG.16 2 6
+## Population MLG count order
+## <fctr> <chr> <int> <fctr>
+## 1 Athena MLG.20 9 1
+## 2 Athena MLG.66 5 2
+## 3 Athena MLG.14 3 3
+## 4 Athena MLG.35 3 4
+## 5 Athena MLG.13 2 5
+## 6 Athena MLG.16 2 6
ggplot2 is a fantastic package that poppr uses to produce graphs for the mlg.table()
, poppr()
, and ia()
functions. Saving a plot with ggplot2 is performed with one command after your plot has rendered:
data(nancycats) # Load the data set.
poppr(nancycats, sample = 999) # Produce a single plot.
-ggsave("nancycats.pdf")
Note that you can name the file anything, and ggsave
will save it in that format for you. The details are in the documentation and you can access it by typing help(ggsave)
in your R console. The important things to note are that you can set a width
, height
, and unit
. The only downside to this function is that you can only save one plot at a time. If you want to be able to save multiple plots, read on to the next section.
Kamvar ZN, Tabima JF and Grünwald NJ (2014). +“\textit{Poppr}: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction.” +PeerJ, 2, pp. e281. +ISSN 2167-8359, doi: 10.7717/peerj.281, http://dx.doi.org/10.7717/peerj.281. +
+@Article{kamvar2014poppr, + title = {\textit{Poppr}: an {R} package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction.}, + author = {Zhian N. Kamvar and Javier F. Tabima and Niklaus J. Grünwald}, + year = {2014}, + month = {3}, + volume = {2}, + pages = {e281}, + keywords = {population genetics, clonality, genotypic diversity, index of association, Bruvo's distance, clone correction, minimum spanning networks, hierarchy, bootstrap, permutation}, + issn = {2167-8359}, + url = {http://dx.doi.org/10.7717/peerj.281}, + doi = {10.7717/peerj.281}, + journal = {PeerJ}, +}+
Kamvar ZN, Brooks JC and Grünwald NJ (2015). +“Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality.” +Front. Genet., 6, pp. 208. +doi: 10.3389/fgene.2015.00208, http://dx.doi.org/10.3389/fgene.2015.00208. +
+@Article{kamvar2015novel, + title = {{Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality}}, + author = {Zhian N. Kamvar and Jonah C. Brooks and Niklaus J. Grünwald}, + year = {2015}, + month = {6}, + volume = {6}, + pages = {208}, + keywords = {clonality, population genomics, bootstrap, index of association, hierarchical analysis, sliding window}, + url = {http://dx.doi.org/10.3389/fgene.2015.00208}, + doi = {10.3389/fgene.2015.00208}, + journal = {Front. Genet.}, +}
Zhian N. Kamvar. Maintainer, author. -
+Javier F. Tabima. Author. -
+Sydney E. Everhart. Contributor, data contributor. -
+Sydney E. Everhart. Contributor, data contributor.
+
0000-0002-5773-1280
Jonah C. Brooks. Author. @@ -101,7 +160,7 @@
Stacy A. Krueger-Hadfield. Contributor. -
+Erik Sotka. Contributor. @@ -109,11 +168,11 @@
Brian J. Knaus. Contributor. -
+Niklaus J. Grunwald. Thesis advisor. -
+Niklaus J. Grunwald. Thesis advisor.
+
0000-0003-1656-7602
This release will contain bug fixes and new, documented, and stable features that will be included in future releases. Note: if you don’t have LaTeX installed, you should set build_vignettes = FALSE
.
devtools::install_github(repo = "grunwaldlab/poppr", build_vignettes = TRUE)
+devtools::install_github(repo = "grunwaldlab/poppr", build_vignettes = TRUE)
library("poppr")
All new features in testing will be released on different branches. These features will be in various stages of development and may or may not be documented. Install with caution. The below command would install features on the the branch called “devel”. Note that these branches might be out of date from the master branch.
-devtools::install_github(repo = "grunwaldlab/poppr@devel", build_vignettes = TRUE)
+devtools::install_github(repo = "grunwaldlab/poppr@devel", build_vignettes = TRUE)
library("poppr")
vignette("algo", "poppr")
vignette("algo", "poppr")
vignette("poppr_manual", "poppr")
vignette("poppr_manual", "poppr")
vignette("mlg", "poppr")
vignette("mlg", "poppr")
boot.ia()
is conceptually similar to resample.ia()
, except it resamples with replacement.resample.ia()
now can resample individuals weighted by their Psex value.imsn()
where custom MLGs would result in an error was fixed. See https://github.com/grunwaldlab/poppr/issues/155 for details.plot_poppr_msn()
where setting scale.leg = FALSE
would result in a very small MSN plot was fixed.mlg()
now works properly for snpclone and genlight objects. See https://github.com/grunwaldlab/poppr/issues/155 for details.plot_poppr_msn()
so additional legends can be added if necessary.test_replen()
and fix_replen()
has been fixed. See https://github.com/grunwaldlab/poppr/issues/136 for details.jack.ia()
will randomly jackknife your sample to a specified n (default is the number of MLG), and calculate the index of association over multiple iterations, giving a distribution of possible values at a given sample size.mlg.table()
gains new parameters, “color” and “background”. The “color” parameter will create a single barplot with colors representing populations while the “background” parameter will create a background plot showing the abundance of MLGs across populations within the facets.win.ia()
will now take into consideration chromosomal coordinates when constructing windows. It has additionally acquired a new parameter chromosome_buffer
, which allows the user to specify whether or not the window should be limited to within chromosomes.poppr()
now correctly identifies the substitute function as diversity_stats()
and not diversity table (see https://github.com/grunwaldlab/poppr/issues/123).bitwise.dist()
clarifies the role of the differences_only
flag (see https://github.com/grunwaldlab/poppr/issues/119).R_CheckUserInterrupt()
. The benefit is that long-running calculations are interrupted near instantly, but at the cost of a few more milliseconds of computation time. (see https://github.com/grunwaldlab/poppr/issues/86)bootgen2genind()
will help users take advantage of bootstrapping distance functions from other packages that require genind objects. For details, see https://github.com/grunwaldlab/poppr/issues/112 and https://github.com/grunwaldlab/poppr/issues/111
plot
parameter for the genotype curve to enable or suppress plotting.options(poppr.debug = TRUE)
.ia()
and poppr()
will now show estimated time. This is from dplyr’s progress_estimated()
.hist
argument in the ia()
is deprecated in favor of plot
.genotype_curve()
plot is now numeric, allowing you to fit a smoothing function over the points without having to use the hack geom_smooth(aes(group = 1))
. This is thanks to Kara Woo for pointing this out on twitter (https://twitter.com/kara_woo/status/783336540407685120).poppr.amova
now contains a note about significance testing with the ade4 function randtest.amova
.mlg.table()
was fixed so that the plots now show the maximum value.imsn()
imsn()
incomp()
will check your data to see if there are any incomparable samples.filter_stats()
(see https://github.com/grunwaldlab/poppr/issues/94)%>%
) is now exported from magrittr to make chaining commands easier.recode_polyploids()
can now take haplodiploid data.
imsn()
code output was fixed (see https://github.com/grunwaldlab/poppr/issues/93)mlg.filter()
assignment method was using nei.dist()
instead of diss.dist()
when no distance was specified.mll.reset()
did not reset non-MLG class objects in the mlg slot was fixed.mlg.filter()
was clarified and updated with more examples.imsn()
now has collapsible side panelsrraf()
now gives options for minor allele correction encompassed in the internal function rare_allele_correction()
. This extends also to pgen()
and psex()
, which must correct minor allele frequencies by default. See https://github.com/grunwaldlab/poppr/issues/81 for details.
mlg.filter()
now defaults to using diss.dist()
@@ -349,9 +414,9 @@ filter_stats()
now returns invisibly when plot = TRUE; see https://github.com/grunwaldlab/poppr/issues/87 for details.clonecorrect()
will default to strata = NA
.genotype_curve
has been implemented in C for a 10x increase in speed.poppr.msn
, bruvo.msn
, and plot_poppr_msn
gain the ability to take character vectors for color palettes. See issue #55 (https://github.com/grunwaldlab/poppr/issues/55) for details.poppr.amova
can now perform amova using the pegas implementation.
rrmlg
will calculate round-robin multilocus genotypes for each locus.poppr.amova
no longer references the “hierarchy” slot.read.genalex
. This was brought up in issue #58 (Thanks to Nick Wong for spotting it).informloci
where the MAF argument wasn’t being applied to P/A data has been fixed.imsn
(issue #41)read.genealex
can now correctly import missing data for diploids (issue #42)
index = "rbarD"
, default) or the classic index of association (index = "Ia"
). If the user uses the function ia
with the argument valuereturn = TRUE
, then the resulting object can be plotted with the plot function.poppr
will now plot all populations in a single faceted plot instead of one plot per population.genind2genalex
gains the ability to selectively write different strata.
mlg.filter
will contract multilocus genotypes given a genetic distance and threshold using one of three algorithms. It can report statistics such as the multilocus genotypes returned, the number of samples within each multilocus genotype, the thresholds at which multilocus genotypes were collapsed, and the genetic distance matrix that represents the new multilocus genotypes.fix_negative_branch
when only one branch had a negative edge.diss.dist
where a single locus would return an error.plot_poppr_msn
to allow for easier manipulation of node sizes and of labelingrecode_polyploids
info_table
will print a discrete scale as opposed to colorbar when type = “ploidy”recode_polyploids
for details.genclone
object is a new extension of the genind
object from adegenet. This object contains slots containing population hierarchies and multilocus genotype definitions and will work with all analyses in adegenet and poppr.genclone
object utilizing hierarchical formulae as arguments for simplification.poppr
will no longer return rounded results, but rather is printed with three significant digits.bruvo.boot
function was not shuffling the repeat lengths for each locus resulting in potentially erroneous bootstrap support values. This has been fixed by implementing an internal S4 class that will allow direct bootstrapping of the data and repeat lengths together.bruvo.boot
or bruvo.dist
fixed.bruvo.boot
allow for ever so slightly faster bootstrapping.bruvo.boot
on UPGMA trees has been fixed.(1994) normalization for NJ trees.
informloci
will remove phylogenetically uninformative loci.poppr_manual
now has cross-references to different sections.getfile
has a new argument, “combine”, which will automatically add the path to the list of files, so they can be read without switching working directory.read.genalex
can now correctly recognize regional formatting without an extra column.
read.genalex
will now be able to take in a file that is formatted with both regional and geographic data.poppr.msn
will draw a minimum spanning network for any distance matrix derived from your data set.poppr.msn
, diss.dist
, greycurve
, and a section discussing how to export graphics.diss.dist
will produce a distance matrix based on discreet distances.greycurve
will produce a grey scale adjusted to user-supplied parameters. This will be useful for future minimum spanning network functions.
bruvo.msn
can now adjust the edge grey level to be weighted toward either closely or distantly weighted individuals.Grunwald, NJ and Hoheisel, G.A. 2006. Hierarchical Analysis of Diversity, Selfing, and Genetic Differentiation in Populations of the Oomycete Aphanomyces euteiches. Phytopathology 96:1134-1141 - doi: 10.1094/PHYTO-96-1134
+ doi: 10.1094/PHYTO-96-1134+-not_run({ - # These examples will simply show you what you can do with these - set.seed(5000) - (x <- sample(10, 20, replace = TRUE)) - (m <- new("MLG", x)) - - # Visibility ------------------------------ - visible(m) # original - visible(m) <- "contracted" - m # shows contracted MLGS - - # Conversion to data frame ---------------- - MLG2df(m) # Grab the internal data frame - - # Distance function handling -------------- - distname(m) # nei.dist - distargs(m) # list() - distalgo(m) # farthest - cutoff(m) - - distname(m) <- substitute("diss.dist") - distargs(m) <- list(percent = TRUE) - distalgo(m) <- "average" - cutoff(m)["contracted"] <- 0.2 - -})
This is the data set from - http://dx.doi.org/10.5281/zenodo.13007. It has been converted to the + http://dx.doi.org/10.5281/zenodo.13007. It has been converted to the genclone object as of poppr version 2.0. It contains 729 samples of the Sudden Oak Death pathogen Phytophthora ramorum genotyped over five microsatellite loci (Kamvar et. al., 2015). 513 samples were collected from @@ -109,16 +128,16 @@
Zhian N. Kamvar, Meg M. Larsen, Alan M. Kanaskie, Everett M. Hansen, & Niklaus J. Grünwald. 2014. Sudden_Oak_Death_in_Oregon_Forests: Spatial and temporal population dynamics of the sudden oak death epidemic in Oregon Forests. ZENODO, doi: - 10.5281/zenodo.13007 - Goss, E. M., Larsen, M., Chastagner, G. A., Givens, D. R., and Grünwald, N. + 10.5281/zenodo.13007
+Goss, E. M., Larsen, M., Chastagner, G. A., Givens, D. R., and Grünwald, N. J. 2009. Population genetic analysis infers migration pathways of Phytophthora ramorum in US nurseries. PLoS Pathog. 5:e1000583. doi: - 10.1371/journal.ppat.1000583
+ 10.1371/journal.ppat.1000583strata
. This argument is useful for when
you want to bootstrap by populations from a genind
object. When you specify strata, the genind object will be converted to
@@ -217,72 +237,72 @@ This function will perform the index of association on a bootstrapped data +set multiple times to create a distribution, showing the variation of the +index due to repeat observations.
+ + +boot.ia(gid, how = "partial", reps = 999, quiet = FALSE, ...)+ +
gid | +a genind or genclone object |
+
---|---|
how | +method of bootstrap. The default |
+
reps | +an integer specifying the number of replicates to perform. +Defaults to 999. |
+
quiet | +a logical. If |
+
... | +options passed on to |
+
a data frame with the index of association and standardized index of +association in columns. Number of rows represents the number of reps.
+ +This function is experimental. Please do not use this unless you know +what you are doing.
+ ++data(Pinf) +boot.ia(Pinf, reps = 99)#> |=== | 6% ~1 s remaining |===================== | 40% ~0 s remaining |=============================== | 58% ~0 s remaining |================================== | 65% ~0 s remaining |========================================= | 77% ~0 s remaining |================================================ | 89% ~0 s remaining#> Ia rbarD +#> 1 0.6430049 0.07032330 +#> 2 0.5705043 0.06235351 +#> 3 0.5774512 0.06340112 +#> 4 0.4942091 0.05420019 +#> 5 0.5472028 0.05998142 +#> 6 0.4984726 0.05433324 +#> 7 0.5416468 0.05935526 +#> 8 0.5967886 0.06488672 +#> 9 0.4339896 0.04743141 +#> 10 0.5141543 0.05641144 +#> 11 0.6170436 0.06754049 +#> 12 0.4998641 0.05385653 +#> 13 0.6042486 0.06611924 +#> 14 0.5600186 0.06113536 +#> 15 0.5835625 0.06363184 +#> 16 0.5228783 0.05722241 +#> 17 0.5205593 0.05693365 +#> 18 0.5461380 0.05993234 +#> 19 0.5588778 0.06113747 +#> 20 0.6305912 0.06908314 +#> 21 0.4909923 0.05367362 +#> 22 0.4739178 0.05188297 +#> 23 0.6103433 0.06682156 +#> 24 0.5379774 0.05874505 +#> 25 0.5061633 0.05533963 +#> 26 0.5396905 0.05905644 +#> 27 0.4806558 0.05248958 +#> 28 0.6151786 0.06757321 +#> 29 0.4335466 0.04728035 +#> 30 0.4933449 0.05332767 +#> 31 0.6078057 0.06662869 +#> 32 0.5500342 0.05932856 +#> 33 0.7257946 0.07979999 +#> 34 0.5113453 0.05593226 +#> 35 0.4466625 0.04872534 +#> 36 0.6157122 0.06730255 +#> 37 0.4266863 0.04616235 +#> 38 0.5159784 0.05619517 +#> 39 0.5893432 0.06428785 +#> 40 0.4646576 0.05093684 +#> 41 0.5495715 0.05993456 +#> 42 0.5433963 0.05940172 +#> 43 0.6236436 0.06806983 +#> 44 0.5384033 0.05875549 +#> 45 0.4917198 0.05386497 +#> 46 0.4974847 0.05428708 +#> 47 0.5542234 0.06060369 +#> 48 0.4605334 0.05034601 +#> 49 0.5140504 0.05618118 +#> 50 0.5329199 0.05745862 +#> 51 0.5116279 0.05527963 +#> 52 0.6646074 0.07304059 +#> 53 0.5325549 0.05828971 +#> 54 0.4789113 0.05255876 +#> 55 0.6612666 0.07237145 +#> 56 0.5426882 0.05929139 +#> 57 0.5247684 0.05729215 +#> 58 0.5265107 0.05769502 +#> 59 0.6370701 0.06977082 +#> 60 0.5042891 0.05538151 +#> 61 0.5141095 0.05622390 +#> 62 0.6900194 0.07559322 +#> 63 0.5218233 0.05692904 +#> 64 0.6690933 0.07219604 +#> 65 0.5643592 0.06151021 +#> 66 0.6691513 0.07315409 +#> 67 0.6005453 0.06566287 +#> 68 0.6345124 0.06946644 +#> 69 0.6464778 0.07062159 +#> 70 0.6113094 0.06693187 +#> 71 0.5891767 0.06433114 +#> 72 0.5354134 0.05852739 +#> 73 0.5387522 0.05898549 +#> 74 0.4992497 0.05467257 +#> 75 0.4728859 0.05182273 +#> 76 0.5452343 0.05951221 +#> 77 0.5063640 0.05538641 +#> 78 0.4660624 0.05082088 +#> 79 0.6071398 0.06553669 +#> 80 0.5530057 0.06050200 +#> 81 0.5385460 0.05897976 +#> 82 0.5328065 0.05841098 +#> 83 0.5190647 0.05678561 +#> 84 0.5399363 0.05917154 +#> 85 0.5099951 0.05576780 +#> 86 0.5371601 0.05868084 +#> 87 0.5340221 0.05815897 +#> 88 0.5641660 0.06203835 +#> 89 0.5323601 0.05802892 +#> 90 0.5664948 0.06195424 +#> 91 0.4953500 0.05423228 +#> 92 0.5885264 0.06455814 +#> 93 0.4580656 0.05004591 +#> 94 0.4791013 0.05231217 +#> 95 0.4831603 0.05289606 +#> 96 0.5431675 0.05917681 +#> 97 0.5442619 0.05956733 +#> 98 0.5394552 0.05910518 +#> 99 0.5681755 0.06209450
Virtual Class "gen"
.
bruvo.dist
, nancycats
,
- upgma
, nj
, boot.phylo
,
- nodelabels
, tab
,
+ upgma
, nj
, boot.phylo
,
+ nodelabels
, tab
,
missingno
.
mean(c(1:9, NA), na.rm = TRUE)
) if all alleles are missing. See the
- next section for other cases.minimum.spanning.tree
. The resultant
graph produced can be plotted using igraph functions, or the entire object
can be plotted using the function plot_poppr_msn
, which will
- give the user a scale bar and the option to layout your data.This function originally appeared in - DOI: 10.5281/zenodo.17424. + DOI: 10.5281/zenodo.17424. This is a bit of a blunt instrument.
ZN Kamvar, JC Brooks, and NJ Grünwald. 2015. Supplementary Material for Frontiers Plant Genetics and Genomics 'Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality'. -DOI: 10.5281/zenodo.17424
+DOI: 10.5281/zenodo.17424Kamvar ZN, Brooks JC and Grünwald NJ (2015) Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Front. Genet. 6:208. doi: -10.3389/fgene.2015.00208
+10.3389/fgene.2015.00208if n.boot
is less than 2, bootstrapping is performed by
sampling N samples from a multinomial distribution weighted by the
proportion of each MLG in the data.
This function originally appeared in - DOI: 10.5281/zenodo.17424
+ DOI: 10.5281/zenodo.17424Kamvar ZN, Brooks JC and Grünwald NJ (2015) Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Front. Genet. 6:208. doi: - 10.3389/fgene.2015.00208
+ 10.3389/fgene.2015.00208+filter_stats(Pinf, distance = bruvo.dist, replen = pinfreps, plot = TRUE, threads = 1L)data(Pinf) pinfreps <- fix_replen(Pinf, c(2, 2, 6, 2, 2, 2, 2, 2, 3, 3, 2)) -filter_stats(Pinf, distance = bruvo.dist, replen = pinfreps, plot = TRUE, threads = 1L)
This function is modified from the version used in - http://dx.doi.org/10.5281/zenodo.13007. Before being fed into the + http://dx.doi.org/10.5281/zenodo.13007. Before being fed into the algorithm to calculate Bruvo's distance, the amplicon length is divided by the repeat unit length. Because of the amplified primer sequence attached to sequence repeat, this division does not always result in an integer and @@ -141,12 +160,12 @@
Zhian N. Kamvar, Meg M. Larsen, Alan M. Kanaskie, Everett M. Hansen, & Niklaus J. Grünwald. Sudden_Oak_Death_in_Oregon_Forests: Spatial and temporal population dynamics of the sudden oak death epidemic in Oregon - Forests. ZENODO, http://doi.org/10.5281/zenodo.13007, 2014. - Kamvar, Z. N., Larsen, M. M., Kanaskie, A. M., Hansen, E. M., & Grünwald, + Forests. ZENODO, http://doi.org/10.5281/zenodo.13007, 2014.
+Kamvar, Z. N., Larsen, M. M., Kanaskie, A. M., Hansen, E. M., & Grünwald, N. J. (2015). Spatial and temporal analysis of populations of the sudden oak death pathogen in Oregon forests. Phytopathology 105:982-989. - doi: 10.1094/PHYTO-12-14-0350-FI - Ruzica Bruvo, Nicolaas K. Michiels, Thomas G. D'Souza, and Hinrich + doi: 10.1094/PHYTO-12-14-0350-FI
+Ruzica Bruvo, Nicolaas K. Michiels, Thomas G. D'Souza, and Hinrich Schulenburg. A simple method for the calculation of microsatellite genotype distances irrespective of ploidy level. Molecular Ecology, 13(7):2101-2106, 2004.
diff --git a/docs/reference/genclone-class.html b/docs/reference/genclone-class.html index 30218411..848007df 100644 --- a/docs/reference/genclone-class.html +++ b/docs/reference/genclone-class.html @@ -23,7 +23,8 @@ - + + @@ -51,10 +52,28 @@The genclone and snpclone classes will allow for more optimized - methods of clone correction. - Previously for genind and genlight objects, + methods of clone correction.
+Previously for genind and genlight objects,
multilocus genotypes were not retained after a data set was subset by
population. The new mlg
slot allows us to assign the
multilocus genotypes and retain that information no matter how we subset
@@ -135,25 +154,24 @@
+not_run({ - - # genclone objects can be created from genind objects - # - data(partial_clone) - partial_clone - (pc <- as.genclone(partial_clone)) - - # snpclone objects can be created from genlight objects - # - set.seed(999) - (gl <- glSim(100, 0, n.snp.struc = 1e3, ploidy = 2, parallel = FALSE)) - (sc <- as.snpclone(rbind(gl, gl, parallel = FALSE), parallel = FALSE)) - # - # Use mlg.filter to create a distance threshold to define multilocus genotypes. - mlg.filter(sc, threads = 1L) <- 0.25 - sc # 82 mlgs - -})
# NOT RUN { +# genclone objects can be created from genind objects +# +data(partial_clone) +partial_clone +(pc <- as.genclone(partial_clone)) + +# snpclone objects can be created from genlight objects +# +set.seed(999) +(gl <- glSim(100, 0, n.snp.struc = 1e3, ploidy = 2, parallel = FALSE)) +(sc <- as.snpclone(rbind(gl, gl, parallel = FALSE), parallel = FALSE)) +# +# Use mlg.filter to create a distance threshold to define multilocus genotypes. +mlg.filter(sc, threads = 1L) <- 0.25 +sc # 82 mlgs + +# }
prevosti.dist
and provesti.dist
are the same function,
provesti.dist
is a spelling error and exists for backwards
- compatibility.
- These distances were adapted from the adegenet function
+ compatibility.
+These distances were adapted from the adegenet function
dist.genpop
to work with genind
objects.
+not_run({ - data(nancycats) - genind2genalex(nancycats, "~/Documents/nancycats.csv", geo=TRUE) -})
# NOT RUN { +data(nancycats) +genind2genalex(nancycats, "~/Documents/nancycats.csv", geo=TRUE) +# }
+nan_geno <- genotype_curve(nancycats)data(nancycats) -nan_geno <- genotype_curve(nancycats)#> Calculating genotypes for 1/8 loci. Completed iterations: 1% Calculating genotypes for 1/8 loci. Completed iterations: 2% Calculating genotypes for 1/8 loci. Completed iterations: 3% Calculating genotypes for 1/8 loci. Completed iterations: 4% Calculating genotypes for 1/8 loci. Completed iterations: 5% Calculating genotypes for 1/8 loci. Completed iterations: 6% Calculating genotypes for 1/8 loci. Completed iterations: 7% Calculating genotypes for 1/8 loci. Completed iterations: 8% Calculating genotypes for 1/8 loci. Completed iterations: 9% Calculating genotypes for 1/8 loci. Completed iterations: 10% Calculating genotypes for 1/8 loci. Completed iterations: 11% Calculating genotypes for 1/8 loci. Completed iterations: 12% Calculating genotypes for 1/8 loci. Completed iterations: 13% Calculating genotypes for 1/8 loci. Completed iterations: 14% Calculating genotypes for 1/8 loci. 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Completed iterations: 100%not_run({ - - # Marker Type Comparison -------------------------------------------------- - # With AFLP data, it is often necessary to include more markers for resolution - data(Aeut) - Ageno <- genotype_curve(Aeut) - - # Many microsatellite data sets have hypervariable markers - data(microbov) - mgeno <- geotype_curve(microbov) - - # Adding a trendline ------------------------------------------------------ - - # Trendlines: you can add a smoothed trendline with geom_smooth() - library("ggplot2") - p <- last_plot() - p + geom_smooth() - - # Producing Figures for Publication --------------------------------------- - - # This data set has been pre filtered - data(monpop) - mongeno <- genotype_curve(monpop) - - # Here, we add a curve and a title for publication - p <- last_plot() - mytitle <- expression(paste("Genotype Accumulation Curve for ", - italic("M. fructicola"))) - p + geom_smooth() + - theme_bw() + - theme(text = element_text(size = 12, family = "serif")) + - theme(title = element_text(size = 14)) + - ggtitle(mytitle) -})
+# Write results to a file in that directory. +setwd(y$path) +write.csv(yfiles) +# }not_run({ - - x <- getfile() - poppr(x$files) +# NOT RUN { +x <- getfile() +poppr(x$files) - y <- getfile(multi=TRUE, pattern="^.+?dat$") - #useful for reading in multiple FSTAT formatted files. +y <- getfile(multi=TRUE, pattern="^.+?dat$") +#useful for reading in multiple FSTAT formatted files. - yfiles <- poppr.all(y$files) +yfiles <- poppr.all(y$files) - # Write results to a file in that directory. - setwd(y$path) - write.csv(yfiles) -})
# Normal grey curve with an adjustment of 3, an upper limit of 0.8, and # weighted towards smaller values. -greycurve()not_run({ - # 1:1 relationship grey curve. - greycurve(gadj=1, glim=1:0) +greycurve()# NOT RUN { +# 1:1 relationship grey curve. +greycurve(gadj=1, glim=1:0) - # Grey curve weighted towards larger values. - greycurve(gweight=2) +# Grey curve weighted towards larger values. +greycurve(gweight=2) - # Same as the first, but the limit is 1. - greycurve(glim=1:0) +# Same as the first, but the limit is 1. +greycurve(glim=1:0) - # Setting the lower limit to 0.1 and weighting towards larger values. - greycurve(glim=c(0.1,0.8), gweight=2) -})
reps | -an integer specifying the number of replicates to perform. + | an integer specifying the number of replicates to perform. Defaults to 999. |
+
---|---|---|
use_psex | +a logical. If |
|
... | -arguments to be passed on to resample.ia |
+ arguments passed on to |
The calculation for the distance between two individuals at a single locus with a allelic states and a ploidy of k is as follows (except for Presence/Absence data): $$ d = \displaystyle \frac{k}{2}\sum_{i=1}^{a} \mid A_{i} - B_{i}\mid $$ To find the total number of differences between two individuals over all loci, you just take d over m - loci, a value we'll call D: - $$D = \displaystyle \sum_{i=1}^{m} d_i $$ - These values are calculated over all possible combinations of individuals + loci, a value we'll call D:
+$$D = \displaystyle \sum_{i=1}^{m} d_i $$
+These values are calculated over all possible combinations of individuals in the data set, \({n \choose 2}\) after which you end up with \({n \choose 2}\cdot{}m\) values of d and \({n \choose 2}\) values of D. Calculating the observed variances is fairly straightforward (modified from Agapow and - Burt, 2001): - $$ V_O = \frac{\displaystyle \sum_{i=1}^{n \choose 2} D_{i}^2 - + Burt, 2001):
+$$ V_O = \frac{\displaystyle \sum_{i=1}^{n \choose 2} D_{i}^2 - \frac{(\displaystyle\sum_{i=1}^{n \choose 2} D_{i})^2}{{n \choose 2}}}{{n - \choose 2}}$$ - Calculating the expected variance is the sum of each of the variances of + \choose 2}}$$
+Calculating the expected variance is the sum of each of the variances of the individual loci. The calculation at a single locus, j is the same as the previous equation, substituting values of D for - d: - $$ var_j = \frac{\displaystyle \sum_{i=1}^{n \choose 2} d_{i}^2 - + d:
+$$ var_j = \frac{\displaystyle \sum_{i=1}^{n \choose 2} d_{i}^2 - \frac{(\displaystyle\sum_{i=1}^{n \choose 2} d_i)^2}{{n \choose 2}}}{{n - \choose 2}} $$ - The expected variance is then the sum of all the variances over all - m loci: - $$ V_E = \displaystyle \sum_{j=1}^{m} var_j $$ - Agapow and Burt showed that \(I_A\) increases steadily with the + \choose 2}} $$
+The expected variance is then the sum of all the variances over all + m loci:
+$$ V_E = \displaystyle \sum_{j=1}^{m} var_j $$
+Agapow and Burt showed that \(I_A\) increases steadily with the number of loci, so they came up with an approximation that is widely used, \(\bar r_d\). For the derivation, see the manual for - multilocus. - $$ \bar r_d = \frac{V_O - V_E} {2\displaystyle + multilocus.
+$$ \bar r_d = \frac{V_O - V_E} {2\displaystyle \sum_{j=1}^{m}\displaystyle \sum_{k \neq j}^{m}\sqrt{var_j\cdot{}var_k}} $$
@@ -299,10 +324,10 @@Paul-Michael Agapow and Austin Burt. Indices of multilocus linkage disequilibrium. Molecular Ecology Notes, 1(1-2):101-102, - 2001 - A.H.D. Brown, M.W. Feldman, and E. Nevo. Multilocus structure of natural - populations of Hordeum spontaneum. Genetics, 96(2):523-536, 1980. - J M Smith, N H Smith, M O'Rourke, and B G Spratt. How clonal are bacteria? + 2001
+A.H.D. Brown, M.W. Feldman, and E. Nevo. Multilocus structure of natural + populations of Hordeum spontaneum. Genetics, 96(2):523-536, 1980.
+J M Smith, N H Smith, M O'Rourke, and B G Spratt. How clonal are bacteria? Proceedings of the National Academy of Sciences, 90(10):4384-4388, 1993.
+not_run({ - - # Set up some data - library("poppr") - library("magrittr") - data(monpop) - splitStrata(monpop) <- ~Tree/Year/Symptom - summary(monpop) - monpop_ssr <- c(CHMFc4 = 7, CHMFc5 = 2, CHMFc12 = 4, - SEA = 4, SED = 4, SEE = 2, SEG = 6, - SEI = 3, SEL = 4, SEN = 2, SEP = 4, - SEQ = 2, SER = 4) - t26 <- monpop %>% setPop(~Tree) %>% popsub("26") %>% setPop(~Year/Symptom) - t26 - imsn() # select Bruvo's distance and enter "monpop_ssr" into the Repeat Length field. - - # It is also possible to run this from github if you are connected to the internet. - # This allows you to access any bug fixes that may have been updated before a formal - # release on CRAN - - shiny::runGitHub("grunwaldlab/poppr", subdir = "inst/shiny/msn_explorer") - - # You can also use your own distance matrices, but there's a small catch. - # in order to do so, you must write a function that will subset the matrix - # to whatever populations are in your data. Here's an example with the above - # data set: - - mondist <- bruvo.dist(monpop, replen = monpop_ssr) - myDist <- function(x, d = mondist){ - dm <- as.matrix(d) # Convert the dist object to a square matrix - xi <- indNames(x) # Grab the sample names that exist - return(as.dist(dm[xi, xi])) # return only the elements that have the names - # in the data set - } - # After executing imsn, choose: - # Distance: custom - # myDist - imsn() -})
# NOT RUN { +# Set up some data +library("poppr") +library("magrittr") +data(monpop) +splitStrata(monpop) <- ~Tree/Year/Symptom +summary(monpop) +monpop_ssr <- c(CHMFc4 = 7, CHMFc5 = 2, CHMFc12 = 4, + SEA = 4, SED = 4, SEE = 2, SEG = 6, + SEI = 3, SEL = 4, SEN = 2, SEP = 4, + SEQ = 2, SER = 4) +t26 <- monpop %>% setPop(~Tree) %>% popsub("26") %>% setPop(~Year/Symptom) +t26 +imsn() # select Bruvo's distance and enter "monpop_ssr" into the Repeat Length field. + +# It is also possible to run this from github if you are connected to the internet. +# This allows you to access any bug fixes that may have been updated before a formal +# release on CRAN + +shiny::runGitHub("grunwaldlab/poppr", subdir = "inst/shiny/msn_explorer") + +# You can also use your own distance matrices, but there's a small catch. +# in order to do so, you must write a function that will subset the matrix +# to whatever populations are in your data. Here's an example with the above +# data set: + +mondist <- bruvo.dist(monpop, replen = monpop_ssr) +myDist <- function(x, d = mondist){ + dm <- as.matrix(d) # Convert the dist object to a square matrix + xi <- indNames(x) # Grab the sample names that exist + return(as.dist(dm[xi, xi])) # return only the elements that have the names + # in the data set +} +# After executing imsn, choose: +# Distance: custom +# myDist +imsn() +# }
Calculate a dissimilarity distance matrix for SNP data.
Bootstrap the index of association
&
-
+ MLG definitions based on genetic distance
nmll
&
-
+ Access and manipulate multilocus lineages.
Define custom multilocus lineages
Shuffle individuals in a genclone
or
+
Shuffle individuals in a genclone
or
genind
object independently over each locus.
Missing data is accounted for on a per-population level. - Ploidy is accounted for on a per-individual level.
Regarding counts of missing data: Each count represents the number @@ -184,8 +204,8 @@
data(nancycats) -nancy.miss <- info_table(nancycats, plot = TRUE, type = "missing")data(Pinf) -Pinf.ploid <- info_table(Pinf, plot = TRUE, type = "ploidy")+nancy.miss <- info_table(nancycats, plot = TRUE, type = "missing")data(Pinf) +Pinf.ploid <- info_table(Pinf, plot = TRUE, type = "ploidy")
a genind
or genclone
+
a genind or genclone object.
Select the populations to be analyzed. This is the
-parameter sublist
passed on to the function popsub
.
+parameter sublist
passed on to the function popsub()
.
Defaults to "ALL"
.
a table with 4 columns indicating the Number of alleles/genotypes - observed, Diversity index chosen, Nei's 1978 gene diversity (expected - heterozygosity), and Evenness.
+a table with 4 columns indicating the Number of alleles/genotypes +observed, Diversity index chosen, Nei's 1978 gene diversity (expected +heterozygosity), and Evenness.
The calculation of Hexp
is \((\frac{n}{n-1}) 1 - \sum_{i =
- 1}^k{p^{2}_{i}}\) where p is the allele
- frequencies at a given locus and n is the number of observed alleles (Nei,
- 1978) in each locus and then returning the average. Caution should be
- exercised in interpreting the results of Hexp with polyploid organisms with
- ambiguous ploidy. The lack of allelic dosage information will cause rare
- alleles to be over-represented and artificially inflate the index. This is
- especially true with small sample sizes.
If lev = "genotype"
, then all statistics reflect genotypic diversity
+within each locus. This includes the calculation for Hexp
, which turns
+into the unbiased Simpson's index.
Jari Oksanen, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre, Peter - R. Minchin, R. B. O'Hara, Gavin L. Simpson, Peter Solymos, M. Henry H. - Stevens, and Helene Wagner. vegan: Community Ecology Package, 2012. R - package version 2.0-5.
-Niklaus J. Gr\"unwald, Stephen B. Goodwin, Michael G. Milgroom, and William - E. Fry. Analysis of genotypic diversity data for populations of - microorganisms. Phytopathology, 93(6):738-46, 2003 - J.A. Ludwig and J.F. Reynolds. Statistical Ecology. A Primer on Methods and - Computing. New York USA: John Wiley and Sons, 1988. - E.C. Pielou. Ecological Diversity. Wiley, 1975. - J.A. Stoddart and J.F. Taylor. Genotypic diversity: estimation and - prediction in samples. Genetics, 118(4):705-11, 1988. - Masatoshi Nei. Estimation of average heterozygosity and genetic distance - from a small number of individuals. Genetics, 89(3):583-590, 1978.
+Jari Oksanen, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre, Peter +R. Minchin, R. B. O'Hara, Gavin L. Simpson, Peter Solymos, M. Henry H. +Stevens, and Helene Wagner. vegan: Community Ecology Package, 2012. R +package version 2.0-5.
+Niklaus J. Gr"unwald, Stephen B. Goodwin, Michael G. Milgroom, and William +E. Fry. Analysis of genotypic diversity data for populations of +microorganisms. Phytopathology, 93(6):738-46, 2003
+J.A. Ludwig and J.F. Reynolds. Statistical Ecology. A Primer on Methods and +Computing. New York USA: John Wiley and Sons, 1988.
+E.C. Pielou. Ecological Diversity. Wiley, 1975.
+J.A. Stoddart and J.F. Taylor. Genotypic diversity: estimation and +prediction in samples. Genetics, 118(4):705-11, 1988.
+Masatoshi Nei. Estimation of average heterozygosity and genetic distance +from a small number of individuals. Genetics, 89(3):583-590, 1978.
Claude Elwood Shannon. A mathematical theory of communication. Bell Systems - Technical Journal, 27:379-423,623-656, 1948
+Technical Journal, 27:379-423,623-656, 1948These methods provide a way to deal with systematic missing data and
to give a wrapper for adegenet
's tab
function.
- ALL OF THESE ARE TO BE USED WITH CAUTION.
- Using this function with polyploid data (where missing data is coded as "0")
- may give spurious results.
Using this function with polyploid data (where missing data is coded as "0") + may give spurious results.
+diversity_stats
to calculate
the Shannon-Weaver index (H), Stoddart and Taylor's
-index (aka inverse Simpson's index; G), Simpson's index (lambda), and evenness (E5).df = TRUE
A long form data frame with the
columns: MLG, Population, Count. Useful for graphing with ggplot2
Multilocus genotypes are the unique combination of alleles across
- all loci. For details of how these are calculated see NA
. In short, for genind and genclone objects, they are
+ all loci. For details of how these are calculated see vignette("mlg",
+ package = "poppr")
. In short, for genind and genclone objects, they are
calculated by using a rank function on strings of alleles, which is
sensitive to missing data. For genlight and snpclone objects, they are
calculated with distance methods via bitwise.dist
and
@@ -269,7 +291,7 @@
SE Everhart, H Scherm, (2015) Fine-scale genetic structure of Monilinia fructicola during brown rot epidemics within individual peach tree canopies. Phytopathology 105:542-549 doi: - 10.1094/PHYTO-03-14-0088-R
+ 10.1094/PHYTO-03-14-0088-RPgen is the probability of a given genotype occuring in a population - assuming HWE. Thus, the value for diploids is - $$P_{gen} = \left(\prod_{i=1}^m p_i\right)2^h$$ - where \(p_i\) are the allele frequencies and h is the count of the + assuming HWE. Thus, the value for diploids is
+$$P_{gen} = \left(\prod_{i=1}^m p_i\right)2^h$$
+where \(p_i\) are the allele frequencies and h is the count of the number of heterozygous sites in the sample (Arnaud-Haond et al. 2007; Parks and Werth, 1993). The allele frequencies, by default, are calculated using a round-robin approach where allele frequencies at a particular locus are - calculated on the clone-censored genotypes without that locus. - To avoid issues with numerical precision of small numbers, this function + calculated on the clone-censored genotypes without that locus.
+To avoid issues with numerical precision of small numbers, this function calculates pgen per locus by adding up log-transformed values of allele frequencies. These can easily be transformed to return the true value (see examples).
@@ -179,39 +198,39 @@This function originally appeared in - DOI: 10.5281/zenodo.17424
+ DOI: 10.5281/zenodo.17424ZN Kamvar, JC Brooks, and NJ Grünwald. 2015. Supplementary Material for Frontiers Plant Genetics and Genomics 'Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality'. -DOI: 10.5281/zenodo.17424
+DOI: 10.5281/zenodo.17424Kamvar ZN, Brooks JC and Grünwald NJ (2015) Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Front. Genet. 6:208. doi: -10.3389/fgene.2015.00208
+10.3389/fgene.2015.00208plot_poppr_msn(x, poppr_msn, gscale = TRUE, gadj = 3, mlg.compute = "original", glim = c(0, 0.8), gweight = 1, - wscale = TRUE, nodebase = 1.15, nodelab = 2, inds = "ALL", - mlg = FALSE, quantiles = TRUE, cutoff = NULL, palette = NULL, - layfun = layout.auto, beforecut = FALSE, pop.leg = TRUE, - scale.leg = TRUE, ...)+ wscale = TRUE, nodescale = 10, nodebase = NULL, nodelab = 2, + inds = "ALL", mlg = FALSE, quantiles = TRUE, cutoff = NULL, + palette = NULL, layfun = layout.auto, beforecut = FALSE, + pop.leg = TRUE, size.leg = TRUE, scale.leg = TRUE, ...)