snpR is an R package for analyzing call Single Nucleotide Polymorphism (SNP) genotypes containing most basic stats including pairwise LD, gaussian sliding window analysis tools, plotting options, clustering analysis, colony interface, Ne estimation, formatting, filtering, and more! It is built primarily to be user-friendly and handle many levels of SNP and sample metadata without the need for complicated file or object management. Please see the example below!
snpR can be installed from GitHub:
# install.packages("remotes")
remotes::install_github("hemstrow/snpR")
To install the vignettes as well (recommended for new users), instead use:
remotes::install_github("hemstrow/snpR", build_vignettes = T) # linux
remotes::install_github("hemstrow/snpR", build_vignettes = T, build_opts = c("--no-resave-data", "--no-manual")) # windows
If you wish to try out the latest features or bug fixes, the dev version can be installed from GitHub as well:
# install.packages("remotes")
remotes::install_github("hemstrow/snpR", ref = "dev")
A CRAN version should be available soon.
import.snpR.data()
: generic read function, takes many file types by
extension or R data.frames()
.read_structure()
: Reads STRUCTURE “.str” files.read_vcf()
: Reads VCF “.vcf” files.read_FSTAT()
: Reads FSTAT “.fstat” files.read_ms()
: Reads ms “.ms” files.read_delimited_snps()
: Reads tab-delimited “NN” or “0000” data.read_genepop()
: Reads genepop “.genepop” files.read_plink()
: Reads PLINK! “.bed”, “.fam”, and “.bim” files.convert_genlight()
: Converts adegenet
genlight
class objects.convert_genind()
: Converts adegenet
genind
class objects.convert_vcfR()
: Converts vcfR
class objects.filter_snps()
: Filter data.format_snps()
: Format data into other export formats.summarize_facets()
: Summarized available facets.citations()
: Fetch citations for all methods used in calculations
for a specific snpRdata
object.check_duplicates()
: Check data for potentially duplicated samples.gap_snps()
: Select a SNP every n bases (simple physical LD
filtering).nsnps()
and nrow()
: Get the number of SNPs in an object.nsamps()
and ncol()
: Get the number of samples in an object.dim()
: Get number of SNPs and samples in an object.get.snpR.stats()
: Fetch any calculated statistics from an object.genotypes()
: Fetch genotypes.sample.meta()
: Fetch (or reasign with <-
) sample metadata.snp.meta()
: Fetch (or reasign with <-
) SNP metadata.[
: The usual bracket operator. Subset by SNP or sample index, or
by facet.subset_snpR_data()
: Wrapper for the bracket operator.calc_pi()
: Nucleotide diversity.calc_ho()
: Observed heterozygosity.calc_he()
: Expected heterosygosity.calc_hwe()
: Hardy-Weinburg equilibrium (HWE).calc_hs()
: Standardized individual heterozygosity.calc_het_hom_ratios()
: Alternative, raw heterozygote/homozygote
ratios within individuals.calc_ne()
: Effective population size.calc_prop_poly()
: The proportion of polymorphic loci.calc_maf()
: Minor allele frequencies, calculated automatically
when any facet operations are performed.calc_private()
: Rarefaction-corrected detection of private alleles
across facet levels.calc_seg_sites()
: Rarefaction-corrected estiamtes of the number of
segregating sites per facet level.calc_allelic_richness():
: Rarefaction-corrected estiamtes of
allele counts per locus per facet level.calc_genetic_distances()
: Genetic distances between individuals.calc_fis()
: $F_{IS}$ (inbreeding coefficients).calc_pairwise_fst()
: Pairwise $F_{ST}$ between facet levels.calc_global_fst()
: Global $F_{ST}$ across facet levels.calc_pairwise_ld()
: Pairwise LD between SNPs.calc_abba_baba()
: ABBA/BABA tests.calc_association()
: Association testing against a phenotype.run_random_forest()
: Run a random forest prediction/association
test against a phenotype.run_random_forest()
: Run genomic prediction against a phenotype.cross_validate_genomic_prediction()
: Bare-bones cross-validation
for genomic predictions.calc_sfs()
: Generate a 1 or 2d site frequency spectra.make_sfs()
: Wrapper function that uses an external dadi
formatted file to generate an sfs.calc_directionality()
: Peter and Slatkin’s directionality index.calc_isolation_by_distance()
: Run an IBD mantel test.calc_tree()
: Generate a tree based on individual or facet-level
relatedness.tabulate_allele_frequency_matrix()
: Generate an allele frequency
matrix.calc_smoothed_averages()
: Core function to do sliding window
analysis using a gaussian smoothing kernal.calc_tajimas_d()
: Tajima’s D across sliding windows.do_bootstraps()
: Core function to generate bootstrapped
significance values for smoothed windows (elevation or reduction vs
genomic background).calc_p_from_bootstraps()
: Calc p-values from bootstraps. Run
automatically by do_boostraps()
.plot_clusters()
: PCA, UMAP, and tSNE plots.plot_structure()
: Run STRUCTURE or several alternatives OR read in
existing “q” files and generate plots.plot_structure_map()
: Plots plot_structure()
or parsed in q file
results on a map given coordinates for populations.plot_diagnostic()
: A suite of useful diagnostic plots.plot_manhattan()
: Manhattan plots from calculated statistics or a
data.frame()
. Excellent for visualizing most statistics genome-wide
(not just association tests!)plot_qq()
: Quantile-quantile (qq) plots from calculated association
test results.plot_pairwise_fst_heatmap()
: Heatmap of FST scores between facet
levels.plot_pairwise_ld_heatmap()
: Heatmap of LD scores between SNPs.run_colony()
: All-in-one function to make a colony import file,
run colony, and parse results.write_colony()
, call_colony()
, parse_colony()
: Write input
files, call colony, and parse results as seperate functions.run_sequoia()
: Run a basic parentage assessment with the sequoia
package.snpR is focused on ease-of-use. Primarily, it achieves this via the use of facets, which describe sample or SNP metadata. snpR is built to automatically split up analysis by facet. For example, calculating observed heterozygosity for each population or family, or for each population/family combination is easy!
library(snpR)
## basic example code
x <- calc_ho(stickSNPs, facets = c("pop")) # split by pop (stickSNPs is an example dataset included in snpR)
x <- calc_ho(x, facets = c("fam")) # split by family
x <- calc_ho(x, facets = c("pop.fam")) # split by combinations of family and pop
snpR also facilitates ease-of-use by being overwrite safe. As above, new analyses are added to an existing object. Results can be fetched using the get.snpR.stats handler.
res <- get.snpR.stats(x, facets = "pop", stats = "ho")
Functions in snpR are consistently named: functions that calculate
statistics are prefixed calc_
, functions that do plots are prefixed
plot_
, and functions that run external tools (like COLONY), are named
run_
. Typing snpR::calc
into the console on Rstudio will bring up a
helpful list of all of the statistical functions!
For a full introduction, check the snpR_introduction vignette.
# remotes::install_github("hemstrow/snpR", build_vignettes = T, build_opts = c("--no-resave-data", "--no-manual"))
vignette("snpR_introduction")