GWAS Meta analysis Using GXwasR
Banabithi Bose
Northwestern UniversityUniversity of Colorado Anschutz Medical Campusbanabithi.bose@gmail.com
8 August 2025
Source:vignettes/meta_analysis.Rmd
meta_analysis.RmdMeta analysis using GXwasR
Here we will present a tutorial showing how to perform meta analysis utilizing the R package GXwasR.
The example data sets for performing the tutorial can be accessed from “data” folder of the package.
Example Datasets
Example datasets are provided with the package and can be accessed by
calling the data() function.
Load the example datasets to perform this tutorial.
- Two sets of summary GWAS summary statistics in .Rda files will be required to perform this tutorial.
- Summary_Stat_Ex1.Rda
- Summary_Stat_Ex2.Rda
Check one of the summary statistics data
data(Summary_Stat_Ex1)
## Visualize three rows and all the columns
Summary_Stat_Ex1[1:3, ]| CHR | SNP | BP | A1 | TEST | NMISS | BETA | SE | L95 | U95 | STAT | P | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | rs143773730 | 73841 | T | ADD | 125 | -0.0789 | 0.2643 | -0.5968 | 0.439 | -0.2986 | 0.7653 |
| 4 | 1 | rs147281566 | 775125 | T | ADD | 125 | -0.3959 | 1.2380 | -2.8230 | 2.031 | -0.3197 | 0.7492 |
| 6 | 1 | rs35854196 | 863863 | A | ADD | 125 | 1.0500 | 0.8858 | -0.6864 | 2.786 | 1.1850 | 0.2360 |
Among these 12 columns, some columns are mandatory for this tutorial, such as: ‘SNP’ (i.e., SNP identifier), ‘BETA’ (i.e., effect-size or logarithm of odds ratio), ‘SE’ (i.e., standard error of BETA), ‘P’ (i.e., p-values) and ‘NMISS’ (i.e., effective sample size). The other columns are, ‘CHR’ (i.e., chromosome code), ‘BP’ (i.e., base pair position), A1 (i.e., disease allele), TEST (i.e., association test type), L95 (i.e., lower limit of 95 percentile confidence interval), U95 (i.e., upper limit of 95 percentile confidence interval) and STAT (i.e., test statistic).
UniqueLoci.Rda: .Rda file with a single column containing SNP names. These could be LD clumped SNPs or any other list of chosen SNPs for Meta analysis.
SNPsPlot.Rda: .Rda file with a single column containing SNP names for the forest plots.
The function for meta analysis:
MetaGWAS()
help(MetaGWAS, package = GXwasR)
#> MetaGWAS: Combining summary-level results from two or more GWA studies
#> into a single estimate.
#>
#> Description:
#>
#> This function combine K sets of GWAS association statistics on
#> same (or at least similar) phenotype. This function employs
#> PLINK's (Purcell et al. 2007) inverse variance-based analysis to
#> run a number of models, including a) Fixed-effect model and b)
#> Random-effect model, assuming there may be variation between the
#> genuine underlying effects, i.e., effect size beta. 'This function
#> also calculates weighted Z-score-based p-values after METAL
#> (Willer et al. 2010). For more information about the algorithms,
#> please see the associated paper.
#>
#> Usage:
#>
#> MetaGWAS(
#> DataDir,
#> SummData = c(""),
#> ResultDir = tempdir(),
#> SNPfile = NULL,
#> useSNPposition = TRUE,
#> UseA1 = FALSE,
#> GCse = TRUE,
#> plotname = "Meta_Analysis.plot",
#> pval_filter = "R",
#> top_snp_pval = 1e-08,
#> max_top_snps = 6,
#> chosen_snps_file = NULL,
#> byCHR = FALSE,
#> pval_threshold_manplot = 1e-05
#> )
#>
#> Arguments:
#>
#> DataDir: A character string for the file path of the input files
#> needed for 'SummData' and 'SNPfile' arguments.
#>
#> SummData: Vector value containing the name(s) of the .Rda file(s) with
#> GWAS summary statistics, with ‘SNP’ (i.e., SNP identifier),
#> ‘BETA’ (i.e., effect-size or logarithm of odds ratio), ‘SE’
#> (i.e., standard error of BETA), ‘P’ (i.e., p-values), 'NMISS'
#> (i.e., effective sample size), 'L95' (i.e., lower limit of
#> 95% confidence interval) and 'U95' (i.e., upper limit of 95%
#> confidence interval) are in mandatory column headers. These
#> files needed to be in DataDir. If the numbers of cases and
#> controls are unequal, effective sample size should be 4 /
#> (1/<# of cases> + 1/<# of controls>). A smaller "effective"
#> sample size may be used for samples that include related
#> individuals, however simulations indicate that small changes
#> in the effective sample size have relatively little effect on
#> the final p-value (Willer et al. 2010). Columns, such as,
#> 'CHR' (Chromosome code), 'BP' (Basepair position), 'A1'
#> (First allele code), 'A2' (Second allele code) columns are
#> optional. If these are present, setting 'useSNPposition' to
#> 'FALSE', causes 'CHR', 'BP' and 'A1' to be ignored and
#> setting 'UseA1' to be 'FALSE' causes 'A1' to be ignored. If,
#> both these arguments are 'TRUE', this function takes care of
#> A1/A2 allele flips properly. Otherwise, A1 mismatches are
#> thrown out. Values of CHR/BP are allowed to vary.
#>
#> ResultDir: A character string for the file path where all output files
#> will be stored. The default is 'tempdir()'.
#>
#> SNPfile: Character string specifying the name of the plain-text file
#> with a column of SNP names. These could be LD clumped SNPs or
#> any other list of chosen SNPs for Meta analysis. This file
#> needs to be in 'DataDir'.
#>
#> useSNPposition: Boolean value, 'TRUE' or 'FALSE' for using 'CHR', 'BP',
#> and 'A1' or not. The default is 'FALSE.' Note: if this is
#> 'FALSE' then there will be no Manhattan and QQ plot will be
#> generated.
#>
#> UseA1: Boolean value, 'TRUE' or 'FALSE' for 'A1' to be used or not.
#> The default is 'FALSE'.
#>
#> GCse: Boolean value, 'TRUE' or 'FALSE' for applying study specific
#> genomic control to adjust each study for potential population
#> structure for all the SNPs. The default is 'TRUE'. If users
#> would want to apply genomic control separately for directly
#> genotyped and imputed SNPs prior using the function, set this
#> parameter as 'FALSE'.
#>
#> plotname: Character string, specifying the plot name of the file
#> containing forest plots for the SNPs. The default is
#> “Meta_Analysis.plot”.
#>
#> pval_filter: Character value as "R","F" or "W", specifying whether
#> p-value threshold should be chosen based on “Random”, “Fixed”
#> or “Weighted” effect model for the SNPs to be included in the
#> forest plots.
#>
#> top_snp_pval: Numeric value, specifying the threshold to be used to
#> filter the SNPs for the forest plots. The default is 1e-08.
#>
#> max_top_snps: Integer value, specifying the maximum number of top SNPs
#> (SNPs with the lowest p-values) to be ploted in the forest
#> plot file. The default is 6.
#>
#> chosen_snps_file: Character string specifying the name of the
#> plain-text file with a column of SNP names for the forest
#> plots. The default is NULL.
#>
#> byCHR: Boolean value, 'TRUE' or 'FALSE', specifying whether the meta
#> analysis will be performed chromosome wise or not. The
#> default is 'FALSE'.
#>
#> pval_threshold_manplot: Numeric value, specifying the p-value threshold
#> for plotting Manhattan plots.
#>
#> Value:
#>
#> A list object containing five dataframes. The first three
#> dataframes, such as Mfixed, Mrandom and Mweighted contain results
#> for fixed effect, random effect and weighted model. Each of these
#> dataframes can have maximum 12 columns, such as:
#>
#> • 'CHR' (Chromosome code)
#>
#> • 'BP' (Basepair position)
#>
#> • 'SNP' (SNP identifier)
#>
#> • 'A1' (First allele code)
#>
#> • 'A2' (Second allele code)
#>
#> • 'Q' (p-value for Cochrane's Q statistic)
#>
#> • 'I' (I^2 heterogeneity index (0-100))
#>
#> • 'P' (P-value from mata analysis)
#>
#> • 'ES' (Effect-size estimate from mata analysis)
#>
#> • 'SE' (Standard Error from mata analysis)
#>
#> • 'CI_L' (Lower limit of confidence interval)
#>
#> • 'CI_U' (Uper limit of confidence interval)
#>
#> The fourth dataframe contains the same columns 'CHR', 'BP', 'SNP',
#> 'A1', 'A2', 'Q', 'I'", with column 'N'' ( Number of valid studies
#> for this SNP), P (Fixed-effects meta-analysis p-value), and other
#> columns as 'Fx...' (Study x (0-based input file indices) effect
#> estimate, Examples: F0, F1 etc.).
#>
#> The fifth dataframe, ProblemSNP has three columns, such as
#>
#> • 'File' (file name of input data),
#>
#> • 'SNP' (Problematic SNPs that are thrown)
#>
#> • 'Problem' (Problem code)
#>
#> Problem codes are:
#>
#> • BAD_CHR (Invalid chromosome code)
#>
#> • BAD_BP (Invalid base-position code), BAD_ES (Invalid
#> effect-size)
#>
#> • BAD_SE (Invalid standard error), MISSING_A1 (Missing allele 1
#> label)
#>
#> • MISSING_A2 (Missing allele 2 label)
#>
#> • ALLELE_MISMATCH (Mismatching allele codes across files)
#>
#> A .pdf file comprising the forest plots of the SNPs is produced in
#> the ResultDir with Plotname as prefix. If 'useSNPposition' is set
#> 'TRUE', a .jpeg file with Manhattan Plot and Q-Q plot will be in
#> the 'ResultDir' with Plotname as prefix.
#>
#> References:
#>
#> Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender
#> D, Maller J, Sklar P, de Bakker PIW, Daly MJ, others (2007).
#> "PLINK: A Tool Set for Whole-Genome Association and
#> Population-Based Linkage Analyses." _The American Journal of Human
#> Genetics_, *81*(3), 559-575. doi:10.1086/519795
#> <https://doi.org/10.1086/519795>.
#>
#> Willer CJ, Li Y, Abecasis GR (2010). "METAL: fast and efficient
#> meta-analysis of genomewide association scans." _Bioinformatics_,
#> *26*(17), 2190-2191. doi:10.1093/bioinformatics/btq340
#> <https://doi.org/10.1093/bioinformatics/btq340>,
#> <http://www.ncbi.nlm.nih.gov/pubmed/20616382>.
#>
#> Examples:
#>
#> data("Summary_Stat_Ex1", package = "GXwasR")
#> data("Summary_Stat_Ex2", package = "GXwasR")
#> DataDir <- GXwasR:::GXwasR_data()
#> ResultDir <- tempdir()
#> SummData <- list(Summary_Stat_Ex1, Summary_Stat_Ex2)
#> SNPfile <- "UniqueLoci"
#> useSNPposition <- FALSE
#> UseA1 <- TRUE
#> GCse <- TRUE
#> byCHR <- FALSE
#> pval_filter <- "R"
#> top_snp_pval <- 1e-08
#> max_top_snps <- 10
#> chosen_snps_file <- NULL
#> pval_threshold_manplot <- 1e-05
#> plotname <- "Meta_Analysis.plot"
#> x <- MetaGWAS(
#> DataDir = DataDir, SummData = SummData, ResultDir = ResultDir,
#> SNPfile = NULL, useSNPposition = TRUE, UseA1 = UseA1, GCse = GCse,
#> plotname = "Meta_Analysis.plot", pval_filter, top_snp_pval, max_top_snps,
#> chosen_snps_file = NULL, byCHR, pval_threshold_manplot
#> )Running MetaGWAS
data(Summary_Stat_Ex1)
data(Summary_Stat_Ex2)
DataDir <- GXwasR:::GXwasR_data()
ResultDir <- tempdir()
SummData <- list(Summary_Stat_Ex1, Summary_Stat_Ex2)
SNPfile <- "UniqueLoci"
useSNPposition <- FALSE
UseA1 <- TRUE
GCse <- TRUE
byCHR <- FALSE
pval_filter <- "R"
top_snp_pval <- 1e-08
max_top_snps <- 10
chosen_snps_file <- NULL
pval_threshold_manplot <- 1e-05
plotname <- "Meta_Analysis.plot"
x <- MetaGWAS(DataDir = DataDir, SummData = SummData, ResultDir = ResultDir, SNPfile = NULL, useSNPposition = TRUE, UseA1 = UseA1, GCse = GCse, plotname = "Meta_Analysis.plot", pval_filter, top_snp_pval, max_top_snps, chosen_snps_file = NULL, byCHR, pval_threshold_manplot)
#> ℹ Processing file number 1
#> ℹ Processing file number 2
#> ℹ Applying study-specific genomic control.
#> ℹ Applying study-specific genomic control.
#> Processing chromosome
#> ✔ Forest plot files for Meta_Analysis.plot SNPs have been created.
#> ℹ You can find them in the directory: /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZ
#> ✔ Forest plot files for Meta_Analysis.plot SNPs have been created.
#> ℹ You can find them in the directory: /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpcRh9ZZRandom effect result
# Dataframe with the fixed effect result
x1 <- x$Resultrandom
x2 <- x1[order(x1$P), ]
knitr::kable(x2[1:10, ], caption = "Top ten associations from random effect model.")| CHR | BP | SNP | A1 | A2 | Q | I | P | ES | SE | CI_L | CI_U | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1054 | 23 | 4184349 | rs6529954 | A | ? | 0.1162 | 59.48 | 0.0000002 | -1.7980 | 0.3441459 | -2.4725260 | -1.1234740 |
| 1053 | 23 | 4137114 | rs5962098 | A | ? | 0.1753 | 45.56 | 0.0000010 | 1.1488 | 0.2351018 | 0.6880004 | 1.6095996 |
| 1043 | 23 | 3376304 | rs6420571 | A | ? | 0.3068 | 4.26 | 0.0000015 | -1.1667 | 0.2426946 | -1.6423814 | -0.6910186 |
| 1055 | 23 | 4214861 | rs12858640 | C | ? | 0.0725 | 69.00 | 0.0000062 | -1.7572 | 0.3887229 | -2.5190968 | -0.9953032 |
| 559 | 2 | 2579014 | rs10186455 | G | ? | 0.4490 | 0.00 | 0.0000476 | 1.2694 | 0.3121061 | 0.6576721 | 1.8811279 |
| 1052 | 23 | 4119808 | rs10521557 | A | ? | 0.8021 | 0.00 | 0.0000631 | 1.4884 | 0.3720121 | 0.7592563 | 2.2175437 |
| 6 | 1 | 1127860 | rs148527527 | G | ? | 0.5778 | 0.00 | 0.0008237 | 1.3978 | 0.4179142 | 0.5786881 | 2.2169119 |
| 558 | 2 | 2535670 | rs13430614 | C | ? | 0.6681 | 0.00 | 0.0010230 | 0.6358 | 0.1935981 | 0.2563478 | 1.0152522 |
| 1382 | 23 | 45563626 | rs1207312 | T | ? | 0.5388 | 0.00 | 0.0046670 | 0.6138 | 0.2169547 | 0.1885689 | 1.0390311 |
| 1383 | 23 | 45565805 | rs1780835 | A | ? | 0.5206 | 0.00 | 0.0064620 | 0.5404 | 0.1984308 | 0.1514757 | 0.9293243 |
Fixed effect result
# Dataframe with the fixed effect result
x1 <- x$Resultfixed
x2 <- x1[order(x1$P), ]
knitr::kable(x2[1:10, ], caption = "Top ten associations from fixed effect model.")| CHR | BP | SNP | A1 | A2 | Q | I | P | ES | SE | CI_L | CI_U | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1054 | 23 | 4184349 | rs6529954 | A | ? | 0.1162 | 59.48 | 0.0000000 | -1.7127 | 0.2025697 | -2.1097365 | -1.3156635 |
| 1055 | 23 | 4214861 | rs12858640 | C | ? | 0.0725 | 69.00 | 0.0000000 | -1.6364 | 0.1955735 | -2.0197240 | -1.2530760 |
| 1053 | 23 | 4137114 | rs5962098 | A | ? | 0.1753 | 45.56 | 0.0000000 | 1.1030 | 0.1618941 | 0.7856876 | 1.4203124 |
| 1043 | 23 | 3376304 | rs6420571 | A | ? | 0.3068 | 4.26 | 0.0000009 | -1.1647 | 0.2371161 | -1.6294476 | -0.6999524 |
| 559 | 2 | 2579014 | rs10186455 | G | ? | 0.4490 | 0.00 | 0.0000476 | 1.2694 | 0.3121061 | 0.6576721 | 1.8811279 |
| 1052 | 23 | 4119808 | rs10521557 | A | ? | 0.8021 | 0.00 | 0.0000631 | 1.4884 | 0.3720121 | 0.7592563 | 2.2175437 |
| 6 | 1 | 1127860 | rs148527527 | G | ? | 0.5778 | 0.00 | 0.0008237 | 1.3978 | 0.4179142 | 0.5786881 | 2.2169119 |
| 558 | 2 | 2535670 | rs13430614 | C | ? | 0.6681 | 0.00 | 0.0010230 | 0.6358 | 0.1935981 | 0.2563478 | 1.0152522 |
| 638 | 2 | 8181194 | rs7370955 | A | ? | 0.2403 | 27.48 | 0.0024130 | 0.7752 | 0.2555007 | 0.2744187 | 1.2759813 |
| 1382 | 23 | 45563626 | rs1207312 | T | ? | 0.5388 | 0.00 | 0.0046670 | 0.6138 | 0.2169547 | 0.1885689 | 1.0390311 |
Weighted effect result
# Dataframe with the fixed effect result
x1 <- x$Resultweighted
x2 <- x1[order(x1$P), ]
knitr::kable(x2[1:10, ], caption = "Top ten associations from weighted effect model.")| CHR | BP | SNP | A1 | A2 | Q | I | P | ES | SE | CI_L | CI_U | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1054 | 23 | 4184349 | rs6529954 | A | ? | 0.1162 | 59.48 | 0.0000000 | -8.340 | 0.0000000 | -8.3400000 | -8.340000 |
| 1055 | 23 | 4214861 | rs12858640 | C | ? | 0.0725 | 69.00 | 0.0000000 | -8.306 | 0.0000000 | -8.3060000 | -8.306000 |
| 1053 | 23 | 4137114 | rs5962098 | A | ? | 0.1753 | 45.56 | 0.0000000 | 6.741 | 1.0000820 | 4.7808392 | 8.701161 |
| 1043 | 23 | 3376304 | rs6420571 | A | ? | 0.3068 | 4.26 | 0.0000010 | -4.883 | 1.0000062 | -6.8430122 | -2.922988 |
| 559 | 2 | 2579014 | rs10186455 | G | ? | 0.4490 | 0.00 | 0.0000723 | 3.969 | 1.0000874 | 2.0088287 | 5.929171 |
| 1052 | 23 | 4119808 | rs10521557 | A | ? | 0.8021 | 0.00 | 0.0000955 | 3.902 | 1.0000837 | 1.9418359 | 5.862164 |
| 6 | 1 | 1127860 | rs148527527 | G | ? | 0.5778 | 0.00 | 0.0010750 | 3.270 | 0.9999618 | 1.3100749 | 5.229925 |
| 558 | 2 | 2535670 | rs13430614 | C | ? | 0.6681 | 0.00 | 0.0014260 | 3.189 | 0.9998946 | 1.2292065 | 5.148794 |
| 638 | 2 | 8181194 | rs7370955 | A | ? | 0.2403 | 27.48 | 0.0032420 | 2.944 | 1.0000649 | 0.9838728 | 4.904127 |
| 1382 | 23 | 45563626 | rs1207312 | T | ? | 0.5388 | 0.00 | 0.0048350 | 2.818 | 1.0000617 | 0.8578790 | 4.778121 |
Metadata of the meta analysis
# Dataframe with the metadata
x1 <- x$Metadata
x2 <- x1[order(x1$Q), ]
knitr::kable(x2[1:10, ], caption = "Metadata of the top ten associations based on Cochrane’s Q statistics.")| CHR | BP | SNP | A1 | A2 | N | Q | I | F0 | F1 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 812 | 2 | 21385538 | rs575905 | T | ? | 2 | 0.0010 | 90.73 | -0.5218 | 0.6472 |
| 1110 | 23 | 9277424 | rs2214279 | G | ? | 2 | 0.0015 | 90.09 | 0.5001 | -0.5365 |
| 655 | 2 | 9913645 | rs1106144 | A | ? | 2 | 0.0020 | 89.48 | 0.5909 | -0.8972 |
| 663 | 2 | 10210922 | rs11678624 | T | ? | 2 | 0.0021 | 89.45 | -0.3659 | 0.7374 |
| 1207 | 23 | 23909933 | rs2428144 | A | ? | 2 | 0.0031 | 88.55 | 0.5344 | -0.4081 |
| 1253 | 23 | 29189630 | rs225456 | C | ? | 2 | 0.0031 | 88.56 | 0.3208 | -1.1170 |
| 1248 | 23 | 28763567 | rs12008039 | G | ? | 2 | 0.0032 | 88.48 | -0.3187 | 0.5565 |
| 810 | 2 | 21275825 | rs12468735 | A | ? | 2 | 0.0036 | 88.19 | -0.5095 | 0.5175 |
| 664 | 2 | 10343419 | rs759347 | C | ? | 2 | 0.0041 | 87.88 | 0.9790 | -0.3894 |
| 1251 | 23 | 29104124 | rs16988439 | C | ? | 2 | 0.0042 | 87.81 | 0.3208 | -1.0730 |
Problematic SNPs
# Dataframe with the problematic SNPs
x1 <- x$ProblemSNP
knitr::kable(x1[1:10, ], caption = "Top ten problematic SNPs.")| File | SNP | Problem |
|---|---|---|
| /private/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T/RtmpcRh9ZZ/SNPdata_2 | rs2803333 | ALLELE_MISMATCH |
| /private/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T/RtmpcRh9ZZ/SNPdata_2 | rs672606 | ALLELE_MISMATCH |
| /private/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T/RtmpcRh9ZZ/SNPdata_2 | rs7519955 | ALLELE_MISMATCH |
| /private/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T/RtmpcRh9ZZ/SNPdata_2 | rs1538466 | ALLELE_MISMATCH |
| /private/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T/RtmpcRh9ZZ/SNPdata_2 | rs61777960 | ALLELE_MISMATCH |
| /private/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T/RtmpcRh9ZZ/SNPdata_2 | rs2865211 | ALLELE_MISMATCH |
| /private/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T/RtmpcRh9ZZ/SNPdata_2 | rs10917217 | ALLELE_MISMATCH |
| /private/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T/RtmpcRh9ZZ/SNPdata_2 | rs196402 | ALLELE_MISMATCH |
| /private/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T/RtmpcRh9ZZ/SNPdata_2 | rs6662038 | ALLELE_MISMATCH |
| /private/var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T/RtmpcRh9ZZ/SNPdata_2 | rs3845484 | ALLELE_MISMATCH |
Reproducibility
The GXwasR package (Bose, Blostein, Kim et al., 2025) was made possible thanks to:
- R (R Core Team, 2025)
- BiocStyle (Oleś, 2025)
- knitr (Xie, 2025)
- RefManageR (McLean, 2017)
- rmarkdown (Allaire, Xie, Dervieux et al., 2024)
- sessioninfo (Wickham, Chang, Flight et al., 2025)
- testthat (Wickham, 2011)
This package was developed using biocthis.
R session information.
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
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#> quarto 1.7.33 @ /usr/local/bin/quarto
#>
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#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────Bibliography
This vignette was generated using BiocStyle (Oleś, 2025) with knitr (Xie, 2025) and rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind the scenes.
Citations made with RefManageR (McLean, 2017).
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