| Type: | Package |
| Title: | Assess the Diagnostic Power of Genomic Marker Combinations |
| Version: | 2.1.1 |
| Description: | Population genetics package for designing diagnostic panels. Candidate markers, marker combinations, and different panel sizes are assessed for how well they can predict the source population of known samples. Requires a genotype file of candidate markers in STRUCTURE format. Methods for population cross-validation are described in Jombart (2008) <doi:10.1093/bioinformatics/btn129>. |
| License: | MIT + file LICENSE |
| Contact: | Kim Vertacnik <kim.vertacnik@mailbox.org> or Julian Dupuis <julian.dupuis@uky.edu> |
| URL: | https://github.com/OksanaVe/snpAIMeR |
| Depends: | R (≥ 2.10) |
| Imports: | adegenet, doParallel, dplyr, forcats, foreach, ggplot2, graphics, magrittr, parallel, readr, tidyr, utils, withr, yaml |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.1 |
| NeedsCompilation: | no |
| Packaged: | 2024-02-20 20:01:29 UTC; kim |
| Author: | Kim Vertacnik 
[cre, aut],
Oksana Vernygora
[aut],
Julian Dupuis
[aut] |
| Maintainer: | Kim Vertacnik <kim.vertacnik@mailbox.org> |
| Repository: | CRAN |
| Date/Publication: | 2024-02-20 20:20:02 UTC |
Assess the Diagnostic Power of Genomic Marker Combinations
Description
Population genetics package for optimizing diagnostic panels. User-selected candidate markers are assessed individually and in combination for how well they can predict the source population of known samples. Requires a genotype file in STRUCTURE format.
Usage
snpAIMeR(run_mode, config_file = NULL, verbose = TRUE)
Arguments
run_mode |
Modes are "interactive", "non-interactive", or "example"; mode must be in quotes. |
config_file |
Yaml file required for "non-interactive" mode; filename/path must be in quotes. |
verbose |
Default is TRUE. |
Details
Yaml file format for "non-interactive" mode (do not include bullet points):
min_range: <minimum panel size>
max_range: <maximum panel size; we recommend no more than 15 markers>
assignment_rate_threshold: <value from 0 to 1>
cross_validation_replicates: <we recommend 100 minimum>
working_directory: <path name in quotes>
structure_file: <path name in quotes>
number_of_individuals: <same as adegenet's "n.ind">
number_of_loci: <same as adegenet's "n.loc">
one_data_row_per_individual: <TRUE or FALSE>
column_sample_IDs: <column number>
column_population_assignments: <column number>
column_other_info: <column number>
row_markernames: <row number>
no_genotype_character: <default is "-9">
optional_population_info: <optional>
genotype_character_separator: <optional>
Minimizing run time: Because of the number of possible combinations, we recommend testing no more than 15 markers. For example, testing 15 markers in panel sizes of 1 to 15 (32,767 total combinations) with 1,000 cross-validation replicates on a system with 48 processor cores took about 5 hours and 20 GB RAM. Reducing the number of cross-validation replicates will reduce run time, however, we recommend no less than 100 replicates.
Value
Cross-validation assignment rates for individual markers, marker combinations, and panel sizes. Outputs three .csv and two .pdf files to a user-specified directory.
See Also
https://github.com/OksanaVe/snpAIMeR
Examples
if (requireNamespace("adegenet", quietly = TRUE)) {
data(nancycats, package = "adegenet")
snpAIMeR("example", verbose = TRUE)
}