Author: Hayley Stoneman
Date: 2026-04-28

# CCAFE Vignette

Hayley Stoneman

#### 2026-04-28

# Contents

# 1 Introduction

Motivation

Growth in the field of genetic and genomic research has vastly increased the amount of available data which is often made publicly available through summary statistics. While there are many opportunities and new methods to use summary-level genetic data, it is often limited by the statistics that are made available. Many post-hoc analyses of disease data require case and control allele frequencies (AFs), which are not always published. We present a framework to derive the case and control AFs from Genome Wide Association Study (GWAS) summary statistics using the whole sample (case and control aggregated) AF, odds ratio, and case and control sample sizes, and compare it to a previously published method that uses the standard error (SE), odds ratio, and case and control sample sizes to recapitulate case and control AFs.

Results

In simulations and real data, we find estimating case and control AFs using the whole sample AF is highly accurate across all settings. When applying this method to the Pan-UK Biobank we find high concordance with the known AFs. Conversely, we find that in Pan-UK Biobank and simulations including covariates, deriving case and control AFs from the SE underestimates the minor AF (MAF) for higher MAFs. To enable estimation of case and control AFs using SE, we use gnomAD v3.1.2 AFs as a proxy for true AFs to estimate and correct for bias fit. While estimating the case control AF using the whole sample AF is preferred due to its high accuracy, estimating from the SE can be used more broadly since the SE can be calculated from the p-value and beta estimate, which are more commonly provided. Both methods expand the utility of publicly available genetic summary statistics, can be used when different statistics are reported, and promote the reusability of genomic data.

This document introduces you to the functions in CCAFE and gives small examples of how they can be used with summary statistics.

Find more details and documentation on GitHub: (https://wolffha.github.io/CCAFE_documentation/)

To jump forward to individual function specifics:

CaseControl_AF – fast forward to example

CaseControl_SE – fast forward to example

To view details on using GWAS VCF formatted data and Bioconductor packages to prepare your data for use in CCAFE:

Fast forward to demo

# 2 Installation

To install this package from BioConductor:

    if (!require("BiocManager", quietly = TRUE))
        install.packages("BiocManager")

    BiocManager::install("CCAFE")

To install this package from GitHub:

    if(!require(devtools, quietly = TRUE)) {
      install.packages("devtools")
    }

    devtools::install_github("https://github.com/wolffha/CCAFE")

## 2.1 Overview of CCAFE Functions

CCAFE contains two functions:

- CaseControl_AF()
- CaseControl_SE()

The two functions are both used to reconstruct case and control allele frequencies (AFs) from genetic summary statistics.

The user will select which function to use based on the available summary statistics.

CaseControl_AF() has the following required parameters:

- Number of cases
- Number of controls
- Odds Ratio (OR) or beta coefficient
- AF for the whole sample (cases and controls combined)

CaseControl_SE() has the follwoing required parameters:

- Number of cases
- Number of controls
- Odds Ratio (OR) or beta coefficient
- SE of the log(OR) for each variant

Code adapted from ReACt GroupFreq function available here: (https://github.com/Paschou-Lab/ReAct/blob/main/GrpPRS_src/CountConstruct.c)

# 3 CaseControl_AF

## 3.1 CaseControl_AF() input

- data: a dataframe with each row being a variant and columns for total AF and OR
- N_case: an integer for the number of case samples
- N_control: an integer for the number of control samples
- OR_colname: a string containing the exact column name in data with the odds ratios
- AF_total_colname: a string containing the exact column name in data with the total AFs

## 3.2 CaseControl_AF() output

Returns the original data dataframe with two additional columns:

- AF_case
- AF_control

The number of rows is equal to the number of variants.

# 4 CaseControl_SE

## 4.1 CaseControl_SE() input

CaseControl_SE has the following required inputs:

- data: a dataframe where each row is a variant and columns for the OR, SE, chromosome, and position
- N_case: an integer for the number of case samples
- N_control: an integer for the number of control samples
- OR_colname: a string containing the exact column name in data with the odds ratios
- SE_colname: a string containing the exact column name in data with the standard errors
- position_colname: a string containing the exact column name in data with the positions of the variants
- chromosome_colname: a string containing the exact column name in data with the chromosome of the variants. Note, sex chromosomes can be either characters (‘X’, ‘x’, ‘Y’, ‘y’) or numeric where X=23 and Y=24
- sex_chromosomes: boolean, TRUE if variants from sex chromosome(s) are included in the dataset
- do_correction: boolean, TRUE if data is provided to correct the estimates using proxy MAFs
- remove_sex_chromosomes: boolean, TRUE if variants on sex chromosomes should be removed. This is only necessary if sex_chromosomes == TRUE and the number of XX/XY individuals per case and control sample is NOT known

CaseControl_SE has the following optional inputs:

If sex_chromosomes == TRUE and remove_sex_chromosomes == FALSE, the following inputs are required:

- N_XX_case: the number of XX chromosome case individuals
- N_XX_control: the number of XX chromosome control individuals
- N_XY_case: the number of XY chromosome case individuals
- N_XY_control: the number of XY chromosome control individuals

If do_correction == TRUE, then data must be provided that includes harmonized data with proxy MAFs

- correction_data: a dataframe with the following EXACT column names: CHR, POS, proxy_MAF, containing data for variants harmonized between the observed and proxy datasets

## 4.2 CaseControl_SE() output

Returns the data dataframe with three additional columns:

- MAF_case
- MAF_control
- MAF_total

Each columns contains the estimated MAF in the cases, controls, and whole sample, respectively. The number of rows is equal to the number of variants

If do_correction == TRUE, three additional columns are included in the dataframe:

- MAF_case_adj
- MAF_control_adj
- MAF_total_adj

Containing MAFs adjusted using the the proxy MAFs to model the expected bias.

NOTE: This method assumes we are estimating the minor allele frequency (MAF). The minor allele may or may not be the effect variant used to calculate AF for the GWAS summary statistics. For additional details see vignette titled “CCAFE Extra Details”

# 5 Examples

These examples use the provided sample data which is a subset of 500 variants from chromosome 1 of the Pan-UKBB diabetes GWAS in non-Finnish European individuals.

    library(CCAFE)
    library(tidyverse)

## 5.1 A quick demo of CaseControl_AF()

Here is a quick demonstration of using the CaseControl_AF function in the CCAFE package using the sample data. This code block loads the library. It then loads the sample data and ensures that it is in the required format (dataframe). It then runs the methods using the required parameters (data, N_case, N_control, OR_colname, AF_total_colname) and prints out the first few lines of the resulting returned dataframe.

    # load the data
    data("sampleDat")
    sampleDat <- as.data.frame(sampleDat)

    results_af <- CaseControl_AF(data = sampleDat,
                                 N_case = 16550,
                                 N_control = 403923,
                                 OR_colname = "OR",
                                 AF_total_colname = "true_maf_pop")

    head(results_af)
    #>    CHR       POS REF ALT true_maf_case true_maf_control       beta      SE
    #> 1 chr1 226824710   C   T     2.048e-01        2.041e-01  0.0003441 0.01471
    #> 2 chr1 117812346   G   A     4.480e-01        4.471e-01 -0.0013920 0.01196
    #> 3 chr1 230838863   C   T     1.647e-01        1.642e-01  0.0042250 0.01620
    #> 4 chr1  93121792 ATT   A     0.000e+00        0.000e+00 -0.1058000 3.53000
    #> 5 chr1 240236388   G   A     1.114e-05        3.775e-05 -1.2540000 1.25300
    #> 6 chr1  12385196   G   A     1.390e-02        1.402e-02 -0.0152700 0.05046
    #>    gnomad_maf        OR true_maf_pop      AF_case   AF_control
    #> 1 2.04050e-01 1.0003442 2.041276e-01 2.041813e-01 2.041254e-01
    #> 2 4.45508e-01 0.9986090 4.471354e-01 4.468049e-01 4.471490e-01
    #> 3 1.82612e-01 1.0042339 1.642197e-01 1.647775e-01 1.641968e-01
    #> 4 1.07925e-04 0.8996046 0.000000e+00 0.000000e+00 0.000000e+00
    #> 5 8.82145e-05 0.2853611 3.670262e-05 1.077692e-05 3.776488e-05
    #> 6 1.15995e-02 0.9848460 1.401528e-02 1.381395e-02 1.402353e-02

We can then plot the estimates from this example using the provided known AFs.

    # plot the results
    # first need minor allele frequency to compare to true_maf_case
    results_af$MAF_case <- sapply(results_af$AF_case, function(x)
      ifelse(x > 0.5, 1-x, x))
    results_af <- as.data.frame(results_af)

    ggplot(results_af, aes(x = true_maf_case, y = MAF_case)) +
      geom_point() +
      geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "red") +
      theme_bw() +
      ggtitle("Example CaseControl_AF()")

## 5.2 A quick demo of CaseControl_SE()

Here is a quick demonstration of using the CaseControl_SE function in the CCAFE package using the sample data. This code block loads the library. It then loads the sample data and ensures that it is in the required format (dataframe). It then runs the methods using the required parameters (data, N_case, N_control, OR_colname, SE_colname, chromosome_colname, position_colname, do_correction, sex_chromosomes) and prints out the first few lines of the resulting returned dataframe.

    # load the data
    data("sampleDat")

    # always ensure data is a dataframe before inputting to CCAFE methods
    sampleDat <- as.data.frame(sampleDat)

    # First run without correction
    results_se_noCorr <- CaseControl_SE(data = sampleDat,
                                        N_case = 16550,
                                        N_control = 403923,
                                        OR_colname = "OR",
                                        SE_colname = "SE",
                                        chromosome_colname = "CHR",
                                        position_colname = "POS",
                                        do_correction = FALSE,
                                        sex_chromosomes = FALSE)
    head(results_se_noCorr)
    #>    CHR       POS REF ALT true_maf_case true_maf_control       beta      SE
    #> 1 chr1 226824710   C   T     2.048e-01        2.041e-01  0.0003441 0.01471
    #> 2 chr1 117812346   G   A     4.480e-01        4.471e-01 -0.0013920 0.01196
    #> 3 chr1 230838863   C   T     1.647e-01        1.642e-01  0.0042250 0.01620
    #> 4 chr1  93121792 ATT   A     0.000e+00        0.000e+00 -0.1058000 3.53000
    #> 5 chr1 240236388   G   A     1.114e-05        3.775e-05 -1.2540000 1.25300
    #> 6 chr1  12385196   G   A     1.390e-02        1.402e-02 -0.0152700 0.05046
    #>    gnomad_maf        OR true_maf_pop     MAF_case  MAF_control    MAF_total
    #> 1 2.04050e-01 1.0003442 2.041276e-01 1.764905e-01 1.764405e-01 1.764425e-01
    #> 2 4.45508e-01 0.9986090 4.471354e-01 3.263835e-01 3.266896e-01 3.266776e-01
    #> 3 1.82612e-01 1.0042339 1.642197e-01 1.392353e-01 1.387297e-01 1.387496e-01
    #> 4 1.07925e-04 0.8996046 0.000000e+00 2.513875e-06 2.794421e-06 2.783379e-06
    #> 5 8.82145e-05 0.2853611 3.670262e-05 1.946826e-05 6.821993e-05 6.630105e-05
    #> 6 1.15995e-02 0.9848460 1.401528e-02 1.250050e-02 1.269041e-02 1.268294e-02

We can then plot the estimates from this example using the provided known AFs.

    # plot the results
    ggplot(results_se_noCorr, aes(x = true_maf_case, y = MAF_case)) +
      geom_point() +
      geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "red") +
      theme_bw() +
      coord_cartesian(ylim = c(0,.5)) +
      ggtitle("Example CaseControl_SE() No Correction")

Here is a quick demonstration of using the CaseControl_SE function in the CCAFE package using the sample data, but also performing the provided bias correction. This code block loads the library. It then loads the sample data and ensures that it is in the required format (dataframe). It then runs the methods using the required parameters (data, N_case, N_control, OR_colname, SE_colname, chromosome_colname, position_colname, do_correction, sex_chromosomes) and prints out the first few lines of the resulting returned dataframe.

    # load the data
    data("sampleDat")
    # always ensure data is a dataframe before inputting to CCAFE methods
    sampleDat <- as.data.frame(sampleDat)

    corr_data <- data.frame(CHR = sampleDat$CHR,
                            POS = sampleDat$POS,
                            proxy_MAF = sampleDat$gnomad_maf)

    # now run with correction
    results_se_corr <- CaseControl_SE(data = sampleDat,
                                      N_case = 16550,
                                      N_control = 403923,
                                      OR_colname = "OR",
                                      SE_colname = "SE",
                                      chromosome_colname = "CHR",
                                      position_colname = "POS",
                                      do_correction = TRUE,
                                      correction_data = corr_data,
                                      sex_chromosomes = FALSE)

    head(results_se_corr)
    #>    CHR       POS REF ALT true_maf_case true_maf_control       beta      SE
    #> 1 chr1 226824710   C   T     2.048e-01        2.041e-01  0.0003441 0.01471
    #> 2 chr1 117812346   G   A     4.480e-01        4.471e-01 -0.0013920 0.01196
    #> 3 chr1 230838863   C   T     1.647e-01        1.642e-01  0.0042250 0.01620
    #> 4 chr1  93121792 ATT   A     0.000e+00        0.000e+00 -0.1058000 3.53000
    #> 5 chr1 240236388   G   A     1.114e-05        3.775e-05 -1.2540000 1.25300
    #> 6 chr1  12385196   G   A     1.390e-02        1.402e-02 -0.0152700 0.05046
    #>    gnomad_maf        OR true_maf_pop     MAF_case  MAF_control    MAF_total
    #> 1 2.04050e-01 1.0003442 2.041276e-01 1.764905e-01 1.764405e-01 1.764425e-01
    #> 2 4.45508e-01 0.9986090 4.471354e-01 3.263835e-01 3.266896e-01 3.266776e-01
    #> 3 1.82612e-01 1.0042339 1.642197e-01 1.392353e-01 1.387297e-01 1.387496e-01
    #> 4 1.07925e-04 0.8996046 0.000000e+00 2.513875e-06 2.794421e-06 2.783379e-06
    #> 5 8.82145e-05 0.2853611 3.670262e-05 1.946826e-05 6.821993e-05 6.630105e-05
    #> 6 1.15995e-02 0.9848460 1.401528e-02 1.250050e-02 1.269041e-02 1.268294e-02
    #>   MAF_total_adj MAF_control_adj MAF_case_adj
    #> 1  2.100692e-01    2.100673e-01 2.101173e-01
    #> 2  4.834218e-01    4.834338e-01 4.831277e-01
    #> 3  1.594029e-01    1.593830e-01 1.598885e-01
    #> 4  2.783379e-06    2.794421e-06 2.513875e-06
    #> 5  6.630105e-05    6.821993e-05 1.946826e-05
    #> 6  9.160387e-03    9.167862e-03 8.977955e-03

Again, we can plot the estimates (corrected) and compare them to the provided known AFs and see reduced bias.

    # plot the results
    ggplot(results_se_corr, aes(x = true_maf_case, y = MAF_case_adj)) +
      geom_point() +
      geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "red") +
      theme_bw() +
      ggtitle("Example CaseControl_SE() With Correction")

# 6 Integration with Bioconductor

## 6.1 Tabular data

This software package aims to increase the utility of existing, publicly available genomic summary data from genome-wide association studies (GWAS). Summary statistics are still widely underutilized, despite their wide availability. As such, Bioconductor packages with methods specifically designed to analyze these statistics are still being developed.

The most commonly used repository of these summary statistics is the NHGRI GWAS Catalog (https://www.ebi.ac.uk/gwas/). From the GWAS catalog, a user can download full summary statistics through the FTP sites. The majority of these summary statistics are zipped plain text (usually space or tab delimited) files. The GWAS Catalog itself has recommended a tabular, plain-text format as its recommended GWAS summary statistic format. As such, this tabular data is most simply read into R as a dataframe structure.

## 6.2 VCF data

While the many users will download the GWAS files and load them into R, there are other ways to obtain GWAS summary statistics, including through the IEU Open GWAS Project (https://gwas.mrcieu.ac.uk/). The IEU Open GWAS Project instead uses the GWAS VCF format (https://doi.org/10.1101/2020.05.29.115824) to store GWAS summary statistics and ensure alignment to the hg19 reference genome. We can use an assortment of Bioconductor packages and data structures to work with GWAS VCF data.

First we will make use of the Bioconductor package VariantAnnotation to handle VCF data

    BiocManager::install("VariantAnnotation")

    suppressWarnings(suppressPackageStartupMessages({
      library(VariantAnnotation)
      library(tidyverse)
    }))

We will use the VCF from this GWAS of Type 2 Diabetes https://doi.org/10.1038/s41588-018-0084-1. A subset of 10,000 variants has been stored within the package and can be loaded.

    data("vcf_sample")

We can see the name of the GWAS dataset (refers to the IEU GWAS database ID name)

    samples(header(vcf_sample))
    #> [1] "ebi-a-GCST007515"

To understand what the data format, we can look view the VCF object

    vcf_sample
    #> class: CollapsedVCF
    #> dim: 10000 1
    #> rowRanges(vcf):
    #>   GRanges with 5 metadata columns: paramRangeID, REF, ALT, QUAL, FILTER
    #> info(vcf):
    #>   DataFrame with 1 column: AF
    #> info(header(vcf)):
    #>       Number Type  Description
    #>    AF A      Float Allele Frequency
    #> geno(vcf):
    #>   List of length 9: ES, SE, LP, AF, SS, EZ, SI, NC, ID
    #> geno(header(vcf)):
    #>       Number Type    Description
    #>    ES A      Float   Effect size estimate relative to the alternative allele
    #>    SE A      Float   Standard error of effect size estimate
    #>    LP A      Float   -log10 p-value for effect estimate
    #>    AF A      Float   Alternate allele frequency in the association study
    #>    SS A      Integer Sample size used to estimate genetic effect
    #>    EZ A      Float   Z-score provided if it was used to derive the EFFECT an...
    #>    SI A      Float   Accuracy score of summary data imputation
    #>    NC A      Integer Number of cases used to estimate genetic effect
    #>    ID 1      String  Study variant identifier

The columns that we want for use in CCAFE are:

From the rowRanges object:

- seqnames (chromosome)
- ranges (position)
- REF
- ALT
- RSID

From the geno object:

- ES (effect size of ALT)
- SE
- AF (allele frequency of ALT)

We then want to convert this VCF formatted dataset into a simple dataframe to use with CCAFE.

    # first we will get the info from GRanges object (position, RSID)
    meta <- as.data.frame(ranges(vcf_sample))
    meta <- meta[,c(1, 4)]
    colnames(meta) <- c("Position", "RSID")
    # get the chromosome (as a)
    meta$Chromosome <- as.vector(seqnames(rowRanges(vcf_sample)))

    # now we can also get the meta data (REF, ALT) from the GRanges object
    meta <- cbind(meta, mcols(vcf_sample)[,c(2,3)])
    rownames(meta) <- seq(1, nrow(meta))

    # now we will get the info from the geno object
    geno_dat <- data.frame(
      beta = unlist(geno(vcf_sample)$ES),
      SE = unlist(geno(vcf_sample)$SE),
      AF = unlist(geno(vcf_sample)$AF)
    )

    df_data <- cbind(meta, geno_dat)
    head(df_data)
    #>   Position        RSID Chromosome REF ALT    beta     SE     AF
    #> 1   762320  rs75333668          1   C   T -0.0352 0.0574 0.0078
    #> 2   861349 rs200686669          1   C   T -0.0299 0.1821 0.0034
    #> 3   865545 rs201186828          1   G   A  0.1188 0.1535 0.0041
    #> 4   865584 rs148711625          1   G   A -0.2004 0.0840 0.0219
    #> 5   865625 rs146327803          1   G   A -0.0778 0.2306 0.0046
    #> 6   865628  rs41285790          1   G   A -0.0037 0.0510 0.0049

For CCAFE we will use OR, so let’s create this column by exponentiating the effect estimate

    df_data$OR <- exp(df_data$beta)

Let’s now apply the CCAFE methods to get case and control specific AFs

First, since we have total AF, we’ll use CaseControl_AF

    df_data <- CaseControl_AF(data = df_data,
                                  N_case = 48286,
                                  N_control = 250671,
                                  OR_colname = "OR",
                                  AF_total_colname = "AF")
    head(df_data)
    #>   Position        RSID Chromosome REF ALT    beta     SE     AF        OR
    #> 1   762320  rs75333668          1   C   T -0.0352 0.0574 0.0078 0.9654123
    #> 2   861349 rs200686669          1   C   T -0.0299 0.1821 0.0034 0.9705426
    #> 3   865545 rs201186828          1   G   A  0.1188 0.1535 0.0041 1.1261447
    #> 4   865584 rs148711625          1   G   A -0.2004 0.0840 0.0219 0.8184033
    #> 5   865625 rs146327803          1   G   A -0.0778 0.2306 0.0046 0.9251494
    #> 6   865628  rs41285790          1   G   A -0.0037 0.0510 0.0049 0.9963068
    #>       AF_case  AF_control
    #> 1 0.007574252 0.007843485
    #> 2 0.003315901 0.003416200
    #> 3 0.004523116 0.004018497
    #> 4 0.018530155 0.022549123
    #> 5 0.004309041 0.004656047
    #> 6 0.004884892 0.004902910

We can look at a comparison of the AFs

Create a dataframe to plot

    plotdata_AF <- df_data %>% dplyr::select(AF, AF_case, AF_control)
    plotdata_AF$AF_total <- (plotdata_AF$AF_case*48286 + plotdata_AF$AF_control*250671)/(298857)
    colnames(plotdata_AF)[1] <- "AF(known)"
    plotdata_AF_long <- pivot_longer(plotdata_AF, cols = colnames(plotdata_AF))

Plot the results

    ggplot(plotdata_AF_long, aes(x = value, fill = name)) +
      geom_boxplot(alpha = 0.5, outliers = FALSE) +
      facet_wrap(~name, nrow = 4) +
      theme_bw()

These case and control AFs can now be used in further downstream analyses. For example, if the data was harmonized with a reference panel, then Summix2 (https://www.bioconductor.org/packages/release/bioc/html/Summix.html) could be used to estimate substructure in the GWAS sample (total sample, cases, or controls). The AFs could also be used in case-case GWAS (see https://wolffha.github.io/CCAFE_documentation/articles/CCAFE_CCGWAS.html) or certain meta-analysis software (e.g. https://doi.org/10.1038/s41598-022-12185-6).

### 6.2.1 VCF Conversion Function

For ease of use of CCAFE with VCF files, we have included a function ‘CCAFE_convertVCF()’.

It performs the steps outlined above, assuming the user has loaded their VCF into R.

    df_data_2 <- CCAFE_convertVCF(vcf_sample)
    #> Valid VCF object, converting to dataframe for CCAFE...

    df_data_2 <- CaseControl_AF(data = df_data_2,
                                  N_case = 48286,
                                  N_control = 250671,
                                  OR_colname = "OR",
                                  AF_total_colname = "AF")
    head(df_data_2)
    #>   Position        RSID Chromosome REF ALT    beta     SE     AF        OR
    #> 1   762320  rs75333668          1   C   T -0.0352 0.0574 0.0078 0.9654123
    #> 2   861349 rs200686669          1   C   T -0.0299 0.1821 0.0034 0.9705426
    #> 3   865545 rs201186828          1   G   A  0.1188 0.1535 0.0041 1.1261447
    #> 4   865584 rs148711625          1   G   A -0.2004 0.0840 0.0219 0.8184033
    #> 5   865625 rs146327803          1   G   A -0.0778 0.2306 0.0046 0.9251494
    #> 6   865628  rs41285790          1   G   A -0.0037 0.0510 0.0049 0.9963068
    #>       AF_case  AF_control
    #> 1 0.007574252 0.007843485
    #> 2 0.003315901 0.003416200
    #> 3 0.004523116 0.004018497
    #> 4 0.018530155 0.022549123
    #> 5 0.004309041 0.004656047
    #> 6 0.004884892 0.004902910

# 7 Session Information

    sessionInfo()
    #> R version 4.6.0 Patched (2026-04-24 r89963)
    #> Platform: aarch64-apple-darwin23
    #> Running under: macOS Tahoe 26.3.1
    #>
    #> Matrix products: default
    #> BLAS:   /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
    #> LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
    #>
    #> locale:
    #> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    #>
    #> time zone: America/New_York
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #>  [1] VariantAnnotation_1.58.0    Rsamtools_2.28.0
    #>  [3] Biostrings_2.80.0           XVector_0.52.0
    #>  [5] SummarizedExperiment_1.42.0 Biobase_2.72.0
    #>  [7] GenomicRanges_1.64.0        IRanges_2.46.0
    #>  [9] S4Vectors_0.50.0            Seqinfo_1.2.0
    #> [11] MatrixGenerics_1.24.0       matrixStats_1.5.0
    #> [13] BiocGenerics_0.58.0         generics_0.1.4
    #> [15] lubridate_1.9.5             forcats_1.0.1
    #> [17] stringr_1.6.0               dplyr_1.2.1
    #> [19] purrr_1.2.2                 readr_2.2.0
    #> [21] tidyr_1.3.2                 tibble_3.3.1
    #> [23] ggplot2_4.0.3               tidyverse_2.0.0
    #> [25] CCAFE_1.4.0                 BiocStyle_2.40.0
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] tidyselect_1.2.1         blob_1.3.0               farver_2.1.2
    #>  [4] S7_0.2.2                 bitops_1.0-9             RCurl_1.98-1.18
    #>  [7] fastmap_1.2.0            GenomicAlignments_1.48.0 XML_3.99-0.23
    #> [10] digest_0.6.39            timechange_0.4.0         lifecycle_1.0.5
    #> [13] KEGGREST_1.52.0          RSQLite_2.4.6            magrittr_2.0.5
    #> [16] compiler_4.6.0           rlang_1.2.0              sass_0.4.10
    #> [19] tools_4.6.0              yaml_2.3.12              rtracklayer_1.72.0
    #> [22] knitr_1.51               S4Arrays_1.12.0          labeling_0.4.3
    #> [25] curl_7.1.0               bit_4.6.0                DelayedArray_0.38.0
    #> [28] RColorBrewer_1.1-3       abind_1.4-8              BiocParallel_1.46.0
    #> [31] withr_3.0.2              grid_4.6.0               scales_1.4.0
    #> [34] dichromat_2.0-0.1        tinytex_0.59             cli_3.6.6
    #> [37] rmarkdown_2.31           crayon_1.5.3             otel_0.2.0
    #> [40] rjson_0.2.23             httr_1.4.8               tzdb_0.5.0
    #> [43] DBI_1.3.0                cachem_1.1.0             parallel_4.6.0
    #> [46] AnnotationDbi_1.74.0     restfulr_0.0.16          BiocManager_1.30.27
    #> [49] vctrs_0.7.3              Matrix_1.7-5             jsonlite_2.0.0
    #> [52] bookdown_0.46            hms_1.1.4                bit64_4.8.0
    #> [55] GenomicFeatures_1.64.0   magick_2.9.1             jquerylib_0.1.4
    #> [58] glue_1.8.1               codetools_0.2-20         stringi_1.8.7
    #> [61] gtable_0.3.6             BiocIO_1.22.0            pillar_1.11.1
    #> [64] htmltools_0.5.9          BSgenome_1.80.0          R6_2.6.1
    #> [67] evaluate_1.0.5           lattice_0.22-9           cigarillo_1.2.0
    #> [70] png_0.1-9                memoise_2.0.1            bslib_0.10.0
    #> [73] Rcpp_1.1.1-1.1           SparseArray_1.12.0       xfun_0.57
    #> [76] pkgconfig_2.0.3