In jmsigner/fpga: fast population genetic analysis

What is the package

• calculation of pop genetic measures

Which data are needed

• diploid only

Comparison to other packages

• optimized for speed at the cost of usabilitiy

• adegenet and gstudio: much more sophisticated representation of genetic data (we only use 3d arrays)

• both packages offer many more methods than we do
• because of their useability, there is some overhead interms of data checking, which we try to avoid here
• PopGenReport most user friendly

Example

Make sure to get the newest version of the package:

devtools::install_github("jmsigner/fpga")

Reading data

From scratch

It is relatively straight forward to create suitable datat structures from a data.frame using the fpga::df2fpga function.

# fpga::df2fpga # does not exist yet 

From files

Currently there only fstat is support:

dat1 <- fpga::read_fstat(system.file('sample-data/simulation_s1e2.dat', package='fpga'))

From other packages

genind objects from adegenet can be converted using:

dat <- adegenet::read.fstat(system.file('sample-data/simulation_s1e2.dat', package='fpga'))
dat2 <- fpga::genind2fpga(dat) # some more checks needed (some alleles are are switched)

similarely from gstudio

fpga::gstudio2fpga

Population genetic measures (simulated data, no missing data)

We use a simulated data set:

library(fpga)
dat0 <- fpga::read_fstat(system.file('sample-data/simulation_s1e2.dat', package='fpga'))
dat1 <- adegenet::read.fstat(system.file('sample-data/simulation_s1e2.dat', package='fpga'))

Number of allels

n_alls(dat0, rownames(dat0))
n_eff_alls(dat0, rownames(dat0))

Number of private allels

fpga::nprivate_alleles(dat0, rownames(dat0))

Observed and expected heterzygosity

fpga::het_exp(dat0)
fpga::het_obs(dat0)

In adegenet

summary(dat1) 

By population

fpga::het_exp(dat0, rownames(dat0))
fpga::het_obs(dat0, rownames(dat0))

$G_{ST}$

fpga::gst(dat0, rownames(dat0))
mmod::Gst_Nei(dat1)

$G_{ST}'$

fpga::gst_p(dat0, rownames(dat0))
mmod::Gst_Hedrick(dat1)

$D_{ST}'$

fpga::dest(dat0, rownames(dat0))
mmod::D_Jost(dat1)

Genetic differentiation (real data)

We use the nancycat data set from the adegenet package for illustration.

library(fpga)
data("nancycats", package = "adegenet")
dat <- genind2fpga(nancycats)
str(dat)

Population genetic measures

We use the nancycat data set from the adegenet package for illustration.

library(fpga)
data("nancycats", package = "adegenet")
dat <- genind2fpga(nancycats)
str(dat)

Number of allels

n_alls(dat, rownames(dat))
n_eff_alls(dat, rownames(dat))

Number of private allels

fpga::nprivate_alleles(dat, rownames(dat))

Observed and expected heterzygosity

fpga::het_exp(dat)
fpga::het_obs(dat)

In adegenet

adesum <- summary(nancycats) ## results differ at time, most likely due to missing values
summary(nancycats) ## results differ at time, most likely due to missing values

By population

fpga::het_exp(dat, rownames(dat))
fpga::het_obs(dat, rownames(dat))

Genetic differentiation

$G_{ST}$

fpga::gst(dat, rownames(dat))
mmod::Gst_Nei(nancycats)

$G_{ST}'$

fpga::gst_p(dat, rownames(dat))
mmod::Gst_Hedrick(nancycats)

$D_{ST}'$

fpga::dest(dat, rownames(dat))
mmod::D_Jost(nancycats)

jmsigner/fpga documentation built on May 19, 2019, 1:56 p.m.