NEWS.md

In hemstrow/snpR: Whole-Genome Analysis Tools for Use with Single Nucleotide Polymorphism Data

snpR 1.2.9.2 hotfix 3

• Fixed bugs in plot_pairwise_fst_heatmap() when runing multiple facets without facet orders or when significance had been calcualted for some but not all facets.
• Fixed a bug where plot_pairwise_fst_heatmap() where plotting was not always strictly on the upper diagonal.
• Fixed NAs mean_fst's when some SNPs returned NA.

snpR 1.2.9.2 hotfix 2

• Added an informative error message when attempting to run calc_ne() with a space in the NeEstimator_path variable.

snpR 1.2.9.1 hotfix 1

Bug Fixes:

• Fixed a few bugs with do_boostraps() and bootstrap fetching with get.snpR.stats(), mostly stemming from the changes to window behavior in 1.2.9. These involved either "_" in subfacet names or a few other oddities. "...." now used as a internal seperator, which, since "." is restricted anyway, should be OK. Added tests to catch.

Known Bugs

• get.snpR.stats() has issues returning an allele frequency matrix alongside other statistics. It returns just an allele frequency matrix perfectly fine.

snpR 1.2.9

Features

Major

• Added calc_allelic_richness() to calculate allelic richness via rarefaction according to Hurlburt 1971.
• Adjusted calc_private() to detect private alleles with rarefaction according to Smith and Grassle 1977 by default. This behavior can be controlled with the rarefaction argument if the raw private alleles are desired instead.
• Added calc_seg_sites() to calculate the number of segregating sites, optionally via rarefaction by an in-house approach (that will probably be published in a small note if we cannot find it documented elsewhere.)
• Added calc_global_fst() to calculate $F{ST}$ globally across all facet subfacets.
• Added local window-smoothed pairwise LD calculation to calc_pairwise_ld(). This is a fast alternative to global LD if the user is interested only local LD fluctuations.
• Added LD pruning to filter_snps() to remove loci out of LD within windows.
• Changed the behavior of any windowing functions slightly. Previously, the final window center would be positioned no further than the end of the chromosome. Window centers can now be positioned within one $\sigma$ (or $3\sigma$ if the triple_sigma argument is set) beyond the end of the chromosome. This will ensure that SNPs at the ends of chromosomes are properly within windows, although it will cause truncated windows. This is a trade-off, but doing things this way ensures proper filtering when using filter_snps() to do LD pruning.

Minor

• Updated the warning messages returned when importing with potentially problematic metadata to be more descriptive.
• Checking facet requests for hybrid facets (like pop.fam or pop.chr) will now double check for any duplicates and error if detected. This is a bit slower but will be safer.
• Added the facet.order argument to plot_pairwise_fst_heatmap() function to control the order of subfacet plotting.
• Using the facets argument with format_snps(format = "vcf") will now write a file with each possible metadata level.
• Added a cleanup argument to calc_association() to allow the intermediate files from GMMAT to be retained.
• To save on memory use, plot_clusters(), plot_manhattan(), and plot_pairwise_ld_heatmap() now have the simplify_output argument which can be used to return only the ggplot objects, not the data used to produce them. The data otherwise in the $data is redundant, since ggplot2 objects already contain the data in $data. The default behavior remains the same since otherwise some old scripts would need to be revised following this change.
• plot_clusters() with simplify_output = FALSE will now also return all of the PCA loadings (for all PCs) in $pca_loadings provided a PCA was requested.
• calc_pairwise_fst() and calc_global_fst() now both return raw, un-weighted $F_{ST}$ averages as well as the usual `$n_{k}$ weighted averages. While weighted averages should perform better with any missing data, some users may desire the classical solution.

Documentation

• Updated the readme to be a bit cleaner.
• Added sections and details to the documentation for filter_snps()
• Fixed a few typos and formatting issues.

Bug Fixes

• Fixed format_snps() to avoid any scientific notation. Added contig flags to the headers printed with format = "vcf".
• Fixed a bug in plot_pairwise_ld_heatmap() when comparing only two snps on a contig/chr/etc.
• Fixed a bug in subset_snpR_data() due to a typo in stats passing that occasionally caused issues.
• Tiny bug fix in calc_association() when using a formula but snpR was expecting a character for splitting reasons.
• Fixed a bug that would cause ranger to fail if using the par arg to run_random_forest().
• Fixed a bug where plot_structure() wouldn't properly say its citation information.
• Changed the behavior of calc_smoothed_averages() to not throw an error if requesting both single and pairwise stats with stats.type if only one of the two has been calculated.
• Fixed a bug where calling an mgc filter with filter_snps() with data with only one option for a heterozygous genotype across all loci would produce an error due to matrix dropping.
• Added a warning when loci are removed during snpRdata object creation due to not being bi-allelic.

Known Bugs

• get.snpR.stats() has issues returning an allele frequency matrix alongside other statistics. It returns just an allele frequency matrix perfectly fine.

snpR 1.2.8

Features

Major

• Added support for the temporal method into calc_ne().
• Added internal filter tracking for easy look-up and reporting of applied filters. Applied filters can be returned using filters(). format_snps() with vcf output option will now automatically append these filters to the vcf header. Added two internal options to import.snpR.data to allow for this and fixed a bit of un-needed redundancy in the process.

Minor

• Added the inds_first argument to filter_snps() to allow users to filter on individuals prior to loci. The default remains to filter on loci first for consistancy.
• Added the remove_garbage argument to filter_snps() to allow users to quickly remove very poorly sequenced loci and individuals (jointly, so neither first) prior to applying all of the other filters. This could remove bias caused by very bad loci or samples causing other loci or samples to appear more poorly sequenced than they actually are.
• Adjusted the behavior of read_structure() to omit non-biallelic loci instead of erroring. This will be changed later to optionally allow them once non-biallelic support is fully added.
• Added back in some old behavior to subset_snpR_data() to allow for naming the facet and subfacets to subset with using the .facets, .subfacets, .snp.facets, and .snp.subfacets arguments. These were previously used instead of the current syntax but were more cumbersome and replaced. They have been re-added because they are useful when the facet is stored as an object and then called (due to pipeline scripts, etc.), which is otherwise not easily done. See the documentation for subset_snpR_data() for examples. This older syntax is not available via the bracket operator ([).
• Added expected heterozygosity to the single-SNP statistics run by do_bootstraps().
• Added $F_{IS}$ to calc_basic_snp_stats().
• Added bootstrap-by-loci support to calc_fis().
• Changed summarize_facets() to return a table of counts of options when provided sample facets.

Documentation

• Fixed typos in the documentation for the keep_components argument of calc_fis().
• Fixed a cutt-off description of the facet argument for calc_tree().

Bug fixes

• Fixed a bug in plot_manhattan() where facet specification was not working correctly for data plotted from a data.frame rather than a snpRdata object directly.
• Fixed a bug where plot_pairwise_ld() in parallel with the CLD option would error after completion.
• Fixed an error in dartR object import, which should have been natively handled by convert_genlight() due to the use of %in% class() instead of methods::is(). Fixed a few other uses of %in% class() to stave off future errors.
• Added an mDat argument to read_structure() to support slighly unorthodox structure files with different missing data identifiers. Note that most structure datasets will have -9, which is still the default.
• Added an na.omit() call to marker name parsing with read_structure() to support oddly formatted data with tabs or spaces separating locus names.
• Fixed a bug in calc_ne() where errors midway through computation would sometime result in the working directory being changed to ./NeEstimator.
• Fixed a bug where complex facets (locus and sample joint facets) weren't being handled correctly by calc_pairwise_fst(). The fix isn't perfectly efficient if multiple facets referring to the same sample facet are all passed at once due to bootstrapping needs.
• Fixed a bug in the sorting of output VCF files from format_snps(). snpR sorts internally by position first (since the chromosome column doesn't have a fixed name and is supplied as a facet to functions), but VCF files should sort by #CHROM.
• Fixed a bug with run_random_forest() where non-polymorphic SNPs would cause an error.
• Fixed some logic on the dapc plot_clusters() option sanity checks.
• Fixed a bug where read_genepop() would error with space-delimited genepop files.

Errata

• Removed dartR dependency in favor of re-distribution of gl2gi() function for reading in genlight objects, with author permission.

snpR 1.2.7.1 -- hotfix

Bug fixes

• Changed import.snpR.data() (and process_genlight()) to rely on dartR's gl2gi() function to import genlight objects, since these can be a bit variable. This does mean adding a suggests for dartR, unfortunately. Also fixed typos in both process_genlight() and process_genind() that were causing the wrappers to fail.

snpR 1.2.7

Features

Minor

• Changed the behavior of calc_fis() to take the ratio of average variance components instead of the average of ratios following the recommendations of Bahtia et al. 2013 and in line with the new behavior of calc_pairwise_fst().
• To aid in calculating weighted mean $F_{IS}$ values for cases where the dataset is too big to run at once with snpR (as may be the case for large WGS data sets, for example), the keep_components argument was added to calc_fis() to return the "b" and "c" variance components for each locus for later processing. Brief instructions were added to the documentation for calc_fis() to explain this process. This brings calc_fis() behavior fully in line with calc_pairwise_fst() for bi-allelic markers, although it still needs to be fixed for poly-allelic markers (which are not yet supported on the front-end).
• Changed the internal behavior of sample.meta()<- (setting new sample meta) to intelligently update snpRdata() objects by removing only calculated statistics and summary tabulations that applied to any changed facets instead of simply re-importing the entire dataset as before. This should substantially speed up this function for large data sets.
• Added the smart_PCA option to plot_clusters(). This will use Patterson et al. (2006)'s methods (with Price et al. (2006)'s allele frequency estimation) for centering and scaling genotypic data for PCA/tSNE/umap construction. This generally doesn't change much unless there is a lot of missing data, since this approach avoids imputation.
• Incorporated update of sequoia V 2.5.3 which adds 'Year.last' - a cutoff for an individuals reproductive window into format_snps(). Returned sequoia to 'suggests'. Incorporated sequoia function GetMaybeRel in the run_sequoia() wrapper. Note that this still needs unit tests, and so should be treated as in development.
• Changed plot_structure_map() to take additional ggplot2 layers directly and plot them prior to the pie charts instead of taking sf objects and trying to guess what the user wanted to do with them. This makes things considerably more flexible and makes it much easier to do things like plot precipitation/etc under the pie charts, although it means the user needs to be a bit more savy. Updated documentation to reflect.
• Added nsnps argument to calc_ne() to do automatic subsetting to run with less SNPs while still merging results into the original dataset. Note that this isn't terribly quick at the moment since it passes to the still somewhat inefficient subset operator [. With reasonably small numbers of SNPs, like what is usually suggested for LDNe, it should be fine.
• Added a few aliases to get.snpR.stats() for IBD (ibd) and Tajima's D (tsd, d) to make it easier to fetch the correct values. Also adjusted the requested statistics to send everything to lower case, so things like LD, D, or He will still work.
• Added the global argument to calc_tajimas_d() for calculating global, non-windowed Tajima's D values across the entire genome.
• Added the mgc option to filter_snps() to filter on the minimum number of individuals with a minor allele (regardless of genotype). This is particularly useful if you want to remove alleles present in only one individual instead of straight singletons.

Documentation

• Clarified documentation for the mac argument for filter_snps() to note that it will remove loci where the minor allele count is less that or equal to the specified integer. So mac = 1 will remove singletons.

Bug fixes

• Fixed a weird bug where metadata class conversion during statistic calculation could result in merge errors.
• Fixed an un-informative error message when nothing remains after merging in merge_snpRdata().
• Fixed an issue where having no matching column names during merging in merge_snpRdata() would produce weird results.
• Fixed an issue where having matching but not merged by column names during merging in merge_snpRdata() would produce an error.
• Fixed a niche bug in small data where attempting to build geno.table data during snpRdata facet tabulation when a facet level has no non-missing genotypes would error.
• Fixed a bug where running the .base facet alongside other facets with calc_pairwise_ld() with the CLD option would cause an error during merging.
• Fixed a bug where running the .base facet alongside other facets with different snp level facets during calc_smoothed_averages() would cause errors.
• Fixed a bug where running calc_pairwise_ld() would fail to return a proximity table if there were NA values in the sample metadata.
• Fixed a bug where SFS construction by calc_sfs() and other SFS functions would error with some but not all data sets due to issues when adding anc and ref columns without using snp.meta(x)<-. Existing tests didn't catch this because it didn't occur with the stickSNPs test data or other test data sets.
• Fixed a bug where plot_diagnostic() would fail if run with a new facet and the maf plot option but not the fis plot option.
• Fixed a bug where an inappropriate error would be returned if plot_structure() was run with facet = ".base", which should be treated like facet = NULL.
• plot_diagnostic() no longer plots anything for missingness if there is no missing data!
• Added a sanity check to any window functions to throw an error when the position value for a SNP is NA.
• Added a more informative error when calc_pairwise_fst() is run with a facet with only one level.
• Fixed an error when running format_snps() with the output = "rafm" option without facets.
• Fixed the the SNP metadata position column being named pos insted of position from read_plink().

snpR 1.2.6.1 -- hotfix

bug fixes

• Fixed a bug where calc_smoothed_averages() and calc_tajimas_d() would do a step 100 times larger than expected if the default was used! Thus the hotfix.

snpR 1.2.6

Features

Minor

• Added he/ho plotting to plot_diagnostic() and added manual control of which plots to generate. SFS skipped by default. Improved documentation and testing a bit.
• Changed the behavior of calc_pairwise_fst() to take the ratio of average variance components instead of the average of ratios following the recommendations of Bahtia et al. 2013.
• To aid in calculating weighted mean $F_{ST}$ values for cases where the dataset is too big to run at once with snpR (as may be the case for large WGS data sets, for example), the keep_components argument was added to calc_pairwise_fst() to return the "a", "b", and "c" variance components for each locus for later processing. Brief instructions were added to the documentation for calc_pairwise_fst() to explain this process.

snpR 1.2.5

Features

Major

• Rework to allow some features to be used with microsatellite or other non-biallelic data types such as microhaplotypes is underway and will be enabled once basic filtering, diversity calculations, and plotting function support is finished.
• Added support for DAPC to plot_clusters() via interface to adegenet and some code pulled in from (the thankfully GPL-v3) ade4 package. Licence note adjusted to reflect ade4 code.
• Improved memory efficiency of snpRdata objects by removing some duplicated information. Old objects should still work fine, but new objects will be considerably smaller. This also improves snpRdata object creation times!
• Added merge_snpRdata() to merge snpRdata objects using syntax equivalent to base R's merge() function. This can still be made more computationally efficient in the future by avoiding some internal summary tabulation.

Minor

• Reworked calc_smoothed_averages() to be more memory efficient (but slightly slower) when working with large datasets. Added triple_sigma and gaussian arguments that determine if $\sigma$ is tripled to have windows with a full size of 6 x sigma and determine if gaussian smoothing is actually used. Added more info to get.snpR.stats() window returns.
• Minor rework to importing and facet creation to be more memory efficient (but slightly slower) when working with large datasets.
• calc_tajimas_d() will now also return the number of raw segregating sites per window (which was already internally calculated, since it is a part of Watterson's Theta, but not returned).
• Changed the defaults for smoothing and tajima's D to 2*sigma (non-overlapping windows)
• Added the mac argument to filter_snps() to filter by minor allele count instead of minor allele frequency. Currently doesn't support faceting, which will probably be added later depending on user need. The singletons argument is now depreciated.
• Added the hwe_excess_side argument ot filter_snps(), enabling users to only remove SNPs out of HWE that have either het or hom excesses. They default behavior is still to do both.
• Added $ZF_{ST}$ and $F_{ST}/(1-F_{ST})$ to calc_pairwise_fst().
• Added the explicit option to recode chromosome names as simple numeric values to format_snps() with the plink option. For some cases with many scaffolds/etc, this may be necessary. Before this was the default behavior, not an option. To account for this, also added checks to ensure that, if this option is not used, that no chromosome names start in numbers (leading numbers will be replaced with a character equivalent -- 0 -> A, 1 -> B, 9 -> I).
• Changed the second facets used in plot_clusters() to use different shapes instead of different fill/color combos as long as there are less than 25 levels (the number of unique point shapes). This makes for substantially easier to interpret levels in plots! Which facet gets shapes is controlled by the shape_has_more_levels argument.
• Added a verbose option to check_duplicates(). It's still a slow function and could use some work or parallelization.
• Added lambda_gc_correction arguments to plot_qq() and plot_manhattan() to generate $\lambda_{GC}$ genomic stratification measures and correct for them in plots (see this paper).
• Added an informational shout-out on launch directing users towards the issues page and here.

Documentation

• Changed "sample metadata" to "SNP metadata" in the description of the header_cols argument to import.snpR.data().
• Edited the warning message when importing structure data without setting header_cols properly to more explicitly reference that problem.
• Updated the calc_tajimas_d() examples to use the updated get.snpR.stats() syntax.
• Added warnings/messages to calc_ne when it is run without the chr argument or with more than 5,000 SNPs.
• Added a more informative error message to calc_ne that shows if no output files are generated.
• Added a note to the documentation for calc_prop_poly to note that calc_tajimas_d will also calculate the number of segragating sites (and the number of snps) in a window, which can be used to easily calculate the prop_poly per window.
• Extended the error message when importing finds a bad genotype format to suggest removing SNP meta data from your genotypes.

Bug fixes

• Changed import.snpR.data() to return an error if the genotypic dimensions are not correct for the provided SNP and sample meta data instead of proceeding and returning a bogus result.
• Fixed a bug where not using a pop facet with plot_structure() with the method = "structure" option would cause a bug due to an incorrectly set "LOCISPOP" flag (which is checked even if not using the locprior option).
• Fixed a bug where PSIXct data in the sample meta data could cause issues with calc_hs().
• Fixed a bug where vcf files with missing data would occasionally get read in as "."s instead of NAs by vcfR, which meant that they weren't being properly accounted for as missing data by snpR. Most functions actually still work fine, but a few, like plot_structure() were unhappy.
• Added a check to tab-delimited input for a column of NAs at the end (automatically remove if found). Common when importing ANGSD outputs.
• Added normalizePath() to calc_ne to normalize the neestimator path.
• Added automatic facet checking for bad characters when doing facet operations. This is a bit slower (when there are many SNPs or individuals), but will return an informative error instead of an uninformative one and so improves usability.
• Fixed bugs that would occur when NeEstimator is one with only one pcrit and when only one pcrit + one population (both during parsing).
• Fixed a bug in the coan option for NeEstimator.
• Fixed a bug where asking to filter by the .base facet with filter_snps() would cause an error and a spurious warning.
• Standardized the defaults of both facet arguments to filter_snps() to use NULL as a default.

snpR 1.2.4 -- hot-fix

Features

Minor

• Added the gradient_colors argument to plot_pairwise_ld_heatmap() to allow for custom sets of colors for the scale.
• Updated the readme for github with a function table of contents.
• Added another sanity check to calc_ne() to catch full file paths given to outfile.

Bug fixes

• Fixed a nasty bug during imputation of sn data where the wrong maf was found. Shouldn't directly bias clustering, but good to fix asap.
• Swapped the geom used in plot_pairwise_ld_heatmap() to geom_bin2d() to prevent points from disappearing if too many loci are plotted on a chr.
• Fixed read_structure() (and import.snpR.data()) to never assume sample names, just loci names (which is the standard) if noted.
• Added better error checking for invalid NeEstimator exe files to calc_ne().
• Fixed a bug where vcf files with missing data would occasionally get read in as "."s instead of NAs by vcfR, which meant that they weren't being properly accounted for as missing data by snpR. Most functions actually still work fine, but a few, like plot_structure() were unhappy.

snpR 1.2.3

Features

Major

• Added calc_prop_poly() to calculate proportion of polymorphic loci for a given pop.
• Renamed plot_tree() to calc_tree() and removed automatic plot generation, since ggtree, which that depended on, can behave a bit oddly sometimes. An example for generating a plot with ggtree was added to the examples section of calc_tree()'s documentation. ggtree is no longer a suggested dependency.
• Added summarize_facets(), which provides information on either the possible facets or provided specific facets for a given snpRdata object.

Minor

• Added support for FWE (multiple comparisons p-value adjustment) to filter_snps() HWE filtering.
• Added singleton filtering to filter_snps().
• Added iPCA ncp and ncp.max options to run_random_forest() and run_genomic_prediction(). Previously, selecting the iPCA option would work, but run with the default iPCA options and thus determine ncp internally, which is pretty slow.
• Added rug plotting to plot_manhattan() to allow for easy plotting of gene positions, etc under plots. Both ribbon-style and classic rug style supported.
• Moved code for run_sequoia() and plot_structure_map() to a secondary github repo, since both of these use CRAN unfriendly dependencies (sequoia and sf, respectively). These functions now source this code (after asking for permission) in order to run, allowing the dependencies to be dropped. This should not change the user experience whatsoever.
• Set get.snpR.stats() to return an informative warning if an empty list is returned.
• Added GenAlEx outfile support to format_snps(). openxlsx added to the Suggests field of the DESCRIPTION as needed for .xlsx file creation.

Documentation

• Fixed some outdated documentation in calc_ne().
• Fixed some typos in the documentation.
• Fixed some parameter input descriptions in the documentation for Colony.
• Cleaned up some documentation for FWE correction across multiple functions.
• Fixed spelling issues throughout package.
• Added a note on import format guessing, cleaned up the file format descriptions to more clearly note the extension snpR expects, and added the missing STRUCTRE import file description to the documentation for import.snpR.data().

Bug fixes

• Fixed a bug with calc_ne() where pop names were not being properly handled and het/coan method results were not being returned by get.snpR.stats().
• Fixed a rare internal bug where .get.task.list() would sometimes add a space between facet levels when pasting due to weird t() bug. To the user, this might have occasionally resulted in weird behavior when using multiple SNP facets with mixed numeric and character classes.
• Fixed a bug with RefManageR apparently introduced recently where bibentry objects wouldn't correctly write. Eliminated RefManageR dependency, now just uses rbibutils for reading and writing, and, when calling citations(), just spits out the full citation rather than the inline for the "Citation: " line. Not ideal, but doesn't need RefManageR.
• Fixed a bug with calc_genetic_distances() where the "Nei" method wouldn't work (due to a typo in the code).
• Fixed an issue where the ID column wasn't written to vcf files.
• Fixed interpolation not checking for missMDA installation or reporting iPCA as an option if an invalid method provided.
• Fixed a bug where the subfacet and facet columns in the stats slot of a snpRdata object would get flipped in order during calc_association(), resulting in the addition of a bunch of empty data rows when something else was merged in. This would produce a downstream error during calc_pairwise_fst (and potentially elsewhere) due to attempting to cbind the stats slot to the facet_meta slot. Note that this wouldn't cause any bad stats to be calculated or returned anywhere due to the way that get.snpR.stats() functions to fetch results!
• Fixed a bug where trying to calculate windowed averages on top of previously calculated Tajima's D would throw a merge error.
• Fixed a bug where plot_manhattan() wouldn't correctly plot Tajima's D (didn't look for it in the right place)
• Fixed a bug where plot_manhattan() would display the facet name even if there was only one possible level (for example, if the sample facet was the .base level).
• Fixed a bug where importing VCF files with read_vcf() would fail due to a typo in a sanity check.
• Fixed a bug where reading in files with, for example, "GC" AND "CG" genotypes somewhere would cause tabulated genotypes to treat these as two different genotypes. Not a common bug except where reading in, for example, VCF files.
• Fixed a bug where asking to return fst for a facet that it hasn't been calculated would return an uniformative error instead of an empty list and a warning.
• Not so much a bug, but added a warning when reading in genepop/fstat/etc file formats that do not specify the allelic identity (ATCG), sine snpR assumes A/C SNPs in this case. This could potentially cause downstream issues with other packages or with stuff like calc_sfs() with fold = FALSE and calc_abba_baba().

snpR 1.2.2

UI

Features

Major

• Added read_structure() to read STRUCTURE formatted files. Added auto-read of .str files to import.snpR.data().
• Added calc_abba_baba() to do ABBA/BABA tests, including block jackknifing for significance.
• Added calc_diagnostic() to plot basic diagnostic plots for snpRdata objects.

Minor

• Now redistributing part of Roy Francis's pophelper package, since it's not on CRAN. Switched to GPL license to allow this. The package is still cited automatically when generating a citation during plot_structure() or plot_structure_map(). Added a dependency on stats to allow for the re-packaging.
• Cleanup option added to calc_ne().
• Removed dependencies: stringi, pkgcond, stringr, tidyr, CATT. All had only one or a few used functions that were not time intensive, and so could be home-brewed easily to avoid the dependency.
• Adjusted stickSNPs to be smaller--only 100 loci and 100 samples now.
• Added a verbose argument to calc_ne().

Documentation

• Removed the snpR_association vignette, since it required the GMMAT, BGLR, and ranger R packages to be installed, but they are only suggested, not required. This could cause vignette building to fail. May re-tool later to just use the internally implemented association tests.
• Updated documentation for sfs related functions to more explicitly talk about ref and anc columns for folding.

Bug fixes

• Fixed a bug where an incorrect armitage stat would be calculated with calc_association if the major allele differed between the case and control.
• calc_ne() and run_colony() actually cleanup now if asked on Windows.
• Fixed the verbose argument on run_colony() to actually work on Windows.
• Fixed a bug trying to merge new and old data with the same columns in different orders could cause NAs to be filled by the wrong numbers. Internal only problem that only effected the new calc_abba_baba() function.

snpR 1.2.1

UI

• Added a verbose option to many functions to avoid putting quite so much noise onto the console.

Features

Major

• Added calc_he() for traditional HE = 2pq calculation. Note that this produces results almost identical to calc_pi().
• Added calc_hs() for Coltman et al (1999)'s individual heterozygosity.

Minor

• Renamed calc_SFS() and calc_pairwise_LD_heatmap() to lowercase for consistency.
• Got rid of sfs arguments in plot_sfs() and calc_direcitonality(). They now just take provided sfs objects as x, consistent with other functions.
• Reworked calc_pairwise_fst() bootstrapping to be more memory efficient.
• Added a chr_order argument to plot_manhattan() to allow for manual resorting of chromosomes (since factors are coerced away in snpRdata objects).
• Added a highlight_style argument to plot_manhattan() to allow for coloring SNPs instead of labeling them if highlighted.
• Added a verbose option (defaulting to TRUE) for filter_snps() to suppress all of the filtering reports.

Documentation

• Added a NEWS.md file to track changes to the package.
• Cleaned up the association testing vignette.

Bug fixes

• Fixed a bug in run_random_forest() where formulas would be incorrectly specified the first time a line of code was run due to a weird environment scope issue.
• Fixed a bug in get.snpR.stats() when requesting fst values from both a facet with and without fst calculated would throw an error during fst matrix construction. Implemented a test.
• Added ggtree citation to plot_tree.
• Fixed a bug where running no formula but generating importance estimates would throw an error in run_random_forest().
• Fixed a bug where filtering a snpRdata object with filter_snps() such that no individuals or SNPs remained would result in an uninformative error. Added a test to check error messages here and in susbet_snpR_data() for this.
• Fixed a bug in format_snps() to allow for BYmax and BYmin columns to get translated to BY.max and BY.min, since periods aren't allowed in snpRdata metadata columns.
• Fixed a bug in calc_het_hom_ratio() complex facets would throw an error.

hemstrow/snpR documentation built on March 20, 2024, 7:03 a.m.