Transcriptome sequencing data pre-processing

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Last updated: 2025-06-05

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Knit directory: locust-comparative-genomics/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/3_seq-data-qc.Rmd) and HTML (docs/3_seq-data-qc.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	b982319	Maeva TECHER	2025-03-03	update font
html	b982319	Maeva TECHER	2025-03-03	update font
html	474315f	Maeva TECHER	2025-02-27	Build site.
Rmd	faf2db3	Maeva TECHER	2025-01-13	update markdown
html	faf2db3	Maeva TECHER	2025-01-13	update markdown
html	3fa8e62	Maeva TECHER	2024-11-09	updated analysis
Rmd	edb70fe	Maeva TECHER	2024-11-08	overlap and deg results created
html	edb70fe	Maeva TECHER	2024-11-08	overlap and deg results created
html	7f1d1fe	Maeva TECHER	2024-11-01	Build site.
Rmd	f01f1cf	Maeva TECHER	2024-11-01	Adding new files and docs
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html	b9f148d	Maeva A. TECHER	2024-05-15	Build site.
Rmd	bba0fce	Maeva A. TECHER	2024-05-15	wflow_publish("analysis/3_seq-data-qc.Rmd")
html	178254e	Maeva A. TECHER	2024-05-14	Build site.
Rmd	be09a11	Maeva A. TECHER	2024-05-14	update markdown
html	be09a11	Maeva A. TECHER	2024-05-14	update markdown
html	d1cebea	Maeva A. TECHER	2024-02-20	Build site.
Rmd	55f9385	Maeva A. TECHER	2024-02-20	wflow_publish("analysis/3_seq-data-qc.Rmd")
html	df94db2	Maeva A. TECHER	2024-02-20	adding markdown qc
Rmd	f6b4961	Maeva A. TECHER	2024-02-20	wflow_publish("analysis/3_seq-data-qc.Rmd")
html	e39d280	Maeva A. TECHER	2024-01-30	Build site.
html	f701a01	Maeva A. TECHER	2024-01-30	reupdate
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html	1b09cbe	Maeva A. TECHER	2024-01-24	remove
html	141b63c	Maeva A. TECHER	2023-12-18	Build site.
Rmd	53877fa	Maeva A. TECHER	2023-12-18	add pages

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At this point, we will have organized both SRA and de novo paired-end sequencing data within our working directory (for us: located on Grace cluster at /scratch/group/songlab/maeva/headthor-locusts-rna/data) and will be ready to run the Snakemake pipeline on it. As a reminder, each library was build from total RNA extracted and ribodepleted (mRNA, lncRNA, circRNA) from bulk tissues (either head or thorax) from a single specimen reared under isolated or crowded conditions.

Load R libraries (install first from CRAN or Bioconductor)

library("knitr")
library("rmdformats")
library("tidyverse")
library("DT")  # for making interactive search table
library("plotly") # for interactive plots
library("ggthemes") # for theme_calc
library("reshape2")
library("readr")
library("ggplot2")

## Global options
options(max.print="10000")
knitr::opts_chunk$set(
    echo = TRUE,
    message = FALSE,
    warning = FALSE,
    cache = FALSE,
    comment = FALSE,
    prompt = FALSE,
    tidy = TRUE
)
opts_knit$set(width=75)

Control the quality of the .fastq files

The first step is to ensure that we have enough reads per library and remove any potential outlier resulting from library preparation or sequencing failure. For that, we will assess each .fastq file with FASTQC. However, considering we are working with a significant sample size, we will compile the results using MULTIQC.

The aggregated results can be conveniently viewed by opening the HTML report in a web browser.

# On Grace cluster at Texas A&M University

module load GCC/12.2.0  OpenMPI/4.1.4 MultiQC/1.14
multiqc --title 'TYPE THE TITLE YOU WANT' -v /PATHTODIRECTORY

montana <- read_table("/Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/metadata/Stats_RNAseq_QC_19Feb2024.txt", col_names = TRUE, guess_max = 1000)
head(montana)

FALSE # A tibble: 6 × 7
FALSE   Sample_Name      Species Perc_Dups Perc_GC M_Seqs Unique_Reads Duplicate_Reads
FALSE   <chr>            <chr>       <dbl>   <dbl>  <dbl>        <dbl>           <dbl>
FALSE 1 SAMER_G_Crd_SRR… americ…      0.71    0.46   23.7      6958831        16765516
FALSE 2 SAMER_G_Crd_SRR… americ…      0.67    0.47   23.7      7784916        15939431
FALSE 3 SAMER_G_Crd_SRR… americ…      0.71    0.48   15.7      4570667        11085054
FALSE 4 SAMER_G_Crd_SRR… americ…      0.67    0.48   15.7      5145265        10510456
FALSE 5 SAMER_G_Crd_SRR… americ…      0.84    0.49   37.2      6049864        31125514
FALSE 6 SAMER_G_Crd_SRR… americ…      0.8     0.49   37.2      7301268        29874110

# Convert values to millions
montana <- montana %>%
  mutate_at(vars(contains("Reads")), list(~ ./1000000))

# Pivot the data
montana_long <- montana %>%
  pivot_longer(cols = contains("Reads"), names_to = "Variable", values_to = "Value")

# Define colors
colors.reads <- c("Duplicate_Reads" = "black", "Unique_Reads" = "deepskyblue")

# Plot the stacked bar plot with values in millions and custom colors
ggplot(montana_long, aes(x = Sample_Name, y = Value, fill = Variable)) +
  geom_bar(stat = "identity", position = "stack") +
  facet_wrap(~Species, scales = "free") +
  labs(title = "Stacked Bar Plot of Unique Reads and Duplicate Reads by Sample",
       x = NULL,  # Remove x-axis label
       y = "Millions of Reads") +
  theme_minimal() +
  theme(axis.text.x = element_blank()) +  # Remove x-axis labels
  scale_fill_manual(values = colors.reads) +  # Set custom colors
  geom_hline(yintercept = 30, linetype = "dashed", color = "red") +
  geom_hline(yintercept = 50, linetype = "dashed", color = "gray70") +
  ylim(0, 62)  # Set y-axis limits

Past versions of "plot of reads duplicates-1.png"

Version	Author	Date
3fa8e62	Maeva TECHER	2024-11-09
edb70fe	Maeva TECHER	2024-11-08
7f1d1fe	Maeva TECHER	2024-11-01
1aaa476	Maeva TECHER	2024-11-01
d1cebea	Maeva A. TECHER	2024-02-20

These plots show that most of our samples have over 30 million reads per sample and that most of these reads are considered duplicates. However, it is possible that the “duplicate” status come from the over expression of certain genes in Schistocerca.

library(ggConvexHull)
# Define custom colors for each species
species_colors <- c("americana" = "forestgreen", "cubense" = "yellow3", "gregaria" = "orange", "nitens" = "blue", "piceifrons" = "red2", cancellata = "deeppink")  

p <- ggplot(montana, aes(x = Perc_Dups, y = M_Seqs, color = Species)) +
  geom_point(size =2) +
  scale_color_manual(values = species_colors) +  # Apply custom colors
  labs(title = "M_Seqs vs % Dups by Species",
       x = "Percentage of Duplicates",
       y = "Millions of Reads") +
  theme_minimal()

p + geom_convexhull(aes(fill = Species, color = Species), alpha = 0.2) +
    scale_fill_manual(values = species_colors)

Past versions of "plot of reads duplicates2-1.png"

Version	Author	Date
3fa8e62	Maeva TECHER	2024-11-09
edb70fe	Maeva TECHER	2024-11-08
7f1d1fe	Maeva TECHER	2024-11-01
1aaa476	Maeva TECHER	2024-11-01
f01f1cf	Maeva TECHER	2024-11-01

Trim and adapter removal

After checking the initial sequence quality, we can determine whether any parameters adjustments are needed. There are several tools for trimming and removing adapters but we used fastp and trim_galore to remove our contaminants. Below are the two Snakemake rules we are using. We decided to go ahead with trim_galore as it was able to remove adapter-dimers the best in our dataset.

########################################
# Snakefile rule
########################################

rule trimming_fastp:
        input:
                read1 = WORKDir + "/00-{species}-reads/{locust}_1.fastq.gz",
                read2 = WORKDir + "/00-{species}-reads/{locust}_2.fastq.gz"
        output: 
                reportjson = WORKDir + "/01-{species}-trimmed-fastp/TrimQC/{locust}_fastp.json", 
                reporthtml = WORKDir + "/01-{species}-trimmed-fastp/TrimQC/{locust}_fastp.html",
                tread1 = WORKDir + "/01-{species}-trimmed-fastp/{locust}_1.trimmed.fastq.gz",
                tread2 = WORKDir + "/01-{species}-trimmed-fastp/{locust}_2.trimmed.fastq.gz"
        shell:
                """
                module purge
                ml GCC/11.2.0 fastp/0.23.2

                fastp --thread 16 \\
                --in1 {input.read1} \\
                --in2 {input.read2} \\
                --out1 {output.tread1} \\
                --out2 {output.tread2} \\
                --trim_front1 2 \\
                --trim_front2 2 \\
                --detect_adapter_for_pe \\
                -l 50 \\
                --json {output.reportjson} \\
                --html {output.reporthtml}
                """    


rule trimming_tgalore:
        input:
                read1 = WORKDir + "/00-{species}-reads/{locust}_1.fastq.gz",
                read2 = WORKDir + "/00-{species}-reads/{locust}_2.fastq.gz"
        output: 
                tread1 = WORKDir + "/01-{species}-trimmed-tgalore/{locust}_1_val_1.fq.gz",
                tread2 = WORKDir + "/01-{species}-trimmed-tgalore/{locust}_2_val_2.fq.gz"
        shell:
                """
                module purge
                ml GCCcore/11.2.0 Trim_Galore/0.6.7

                trim_galore --trim-n \\
                --cores 16 \\
                --quality 20 \\
                --clip_R1 2 \\
                --clip_R2 2 \\
                --nextera \\
                --output_dir {WORKDir}/01-{wildcards.species}-trimmed-tgalore/ \\
                --fastqc \\
                --paired {input.read1} {input.read2}
                """    
        
########################################
# Parameters in the cluster.json file
########################################

    "trimming_fastp": {
        "cpus-per-task" : 16,
        "partition" : "medium",
        "ntasks": 1,
        "mem" : "40G",
        "time": "0-12:00:00"
    },
    "trimming_tgalore": {
        "cpus-per-task" : 16,
        "partition" : "medium",
        "ntasks": 1,
        "mem" : "40G",
        "time": "0-12:00:00"
    },

For trimming with fastp, we use the following parameters:

• --in1 {input.read1} and --in2 {input.read2}: Specifies the input files in FASTQ format.

• --out1 {output.tread1} and --out2 {output.tread2}: Defines the output files for the trimmed reads.

• --trim_front1 2 and --trim_front2 2: Trims the first two nucleotides from the start (5’ end) of each read in both files.

• --detect_adapter_for_pe: Enables automatic adapter detection for paired-end sequencing reads. This setting ensures that adapter sequences, if present, are detected and trimmed from the reads.

• -l 50: Sets the minimum length for reads after trimming. Any read shorter than 50 nucleotides after trimming is discarded.

• --json {output.reportjson} and --html {output.reporthtml}: Specifies a JSON/HTML format report file to summarize trimming statistics and quality control metrics.

For trimming with trim_galore, we use the following parameters:

• --paired {input.read1} {input.read2}: Specifies that the input is paired-end data.
• --output_dir {WORKDir}/01-{wildcards.species}-trimmed-tgalore/: Specifies the output directory where trimmed files will be saved. {wildcards.species} is a placeholder for the species name, allowing for organized output by species.
• --clip_R1 2 and --clip_R2 2: Clips the first two bases from the 5’ end of both reads (R1 and R2) in paired-end sequencing.
• --nextera: Specifies the use of Nextera adapter sequences for trimming, which are specific to Nextera library preparations. We use this option because with FastQC we saw contaminants of these adapters in some libraries.
• --quality 20: Trims low-quality bases from the end of each read, keeping bases with a Phred score of 20 or higher.
• --fastqc: Runs FastQC after trimming to generate quality reports, allowing you to assess the quality of the trimmed reads.

Trim quality control

If you run the two rules above, you do not need to run the following Snakemake portion, but I put it in case we need to perform extra and customized quality control checks after trimming to ensure that the clipping and filtering of sequences were not overly aggressive. Given the number of sequences we work with, manually inspecting each file immediately would be time-consuming. Instead, we adopt a random sampling approach across different species, rearing conditions, and tissues to see that the trimming process worked well.

########################################
# Snakefile rule
########################################

# Quality control step after trimming: checked for adapter content in particular and quality scores
rule trim_fastqc:
    input:
        read1 = OUTdir + "/trimming/{locust}_trim1P_1.fastq.gz",
        read2 = OUTdir + "/trimming/{locust}_trim2P_2.fastq.gz",
    output:
        htmlqc1 = OUTdir + "/trimming/{locust}_trim1P_1_fastqc.html",
        htmlqc2 = OUTdir + "/trimming/{locust}_trim2P_2_fastqc.html",
    shell:
        """
        module load FastQC/0.11.9-Java-11
        fastqc {input.read1}
        fastqc {input.read2}
        """
        
########################################
# Parameters in the cluster.json file
########################################

    "trim_fastqc":
    {
        "cpus-per-task" : 2,
        "partition" : "medium",
        "ntasks": 1,
        "mem" : "500M",
        "time": "0-03:00:00"
    },

EXAMPLE OF READS QUALITY BEFORE TRIMMING

EXAMPLE OF READS QUALITY AFTER TRIMMING
We can see that the sequence length has changed and that the 5’ and 3’ end positions with lower quality have been removed.

EXAMPLE OF READS QUALITY BEFORE TRIMMING

EXAMPLE OF READS QUALITY AFTER TRIMMING
We can see that most detected adapter sequences have been adequately removed after trimming.

Non-target sequencing data

Contamination is likely to occur throughout various stages of experiments, including tissue acquisition, RNA extraction and library preparation. One can hope to reduces as much as possible its impact on the final sequencing data which can affect the success rate of reads mapping.

Screen for microbial contamination with Kaiju

We decided to screen for microbes sequences present in the trimmed paired-end reads FASTQ.gz files using the tool Kaiju. Kaiju translates metatranscriptomics sequencing reads into six possible reading frames and searches for maximum exact matches of amino acid sequences in a given annotation protein database.

We used the most extensive microbial database nr_euk which encompass the subset of NCBI BLAST nr database containing all proteins belonging to Archaea, Bacteria, Viruses, Fungi and microbial Eukaryotes.

# enable proxy to allow compute node connection to internet
module load WebProxy

## Downloading the 2023-05-10 database from Kaiju webserver
wget --no-check-certificate https://kaiju-idx.s3.eu-central-1.amazonaws.com/2023/kaiju_db_nr_euk_2023-05-10.tgz

To run Kaiju we used the following Snakemake rule:

########################################
# Snakefile rule
########################################

rule kaiju:
    input:
        database = KAIJUdir + "/kaiju_db_nr_euk.fmi",
        taxonid = KAIJUdir + "/nodes.dmp",
        taxonnames = KAIJUdir + "/names.dmp",
        trimmed_read1 = WORKDir + "/01-{species}-trimmed-tgalore/{locust}_1_val_1.fq.gz",
        trimmed_read2 = WORKDir + "/01-{species}-trimmed-tgalore/{locust}_2_val_2.fq.gz"
    output:
        kaijuout = WORKDir + "/KAIJU/{species}-kaiju/{locust}_kaiju.out",
        classification = WORKDir + "/KAIJU/{species}-kaiju/{locust}_kaiju.tsv"
    shell:
        """
        module purge
        ml GCC/8.3.0 OpenMPI/3.1.4 Kaiju/1.7.3-Python-3.7.4

        kaiju -z 12 -v \\
        -a greedy \\
        -f {input.database} \\
        -t {input.taxonid} \\
        -i {input.trimmed_read1} \\
        -j {input.trimmed_read2} \\
        -o {output.kaijuout}

        kaiju2table -v \\
        -t {input.taxonid} \\
        -n {input.taxonnames} \\
        -r phylum \\
        -o {output.classification} {output.kaijuout}
        """
           
        
########################################
# Parameters in the cluster.json file
########################################

    "kaiju":
    {
        "cpus-per-task" : 12,
        "partition" : "medium",
        "ntasks": 2,
        "mem" : "200G",
        "time": "0-12:00:00"
   },

• -z 12: Sets the number of 12 CPU threads to use for parallel processing.

• -v: Enables verbose mode, which provides detailed output information as the program runs.

• -a greedy: Chooses the greedy algorithm for sequence alignment in Kaiju. This algorithm searches for the longest exact match in the database and allows for more sensitive, although potentially slower, matching compared to other options.

• -f {input.database}: Specifies the database file to use for classification, typically a precompiled .fmi file containing microbial or taxonomic protein sequences.

• -t {input.taxonid}: Points to the taxonomy node file (nodes.dmp), which Kaiju uses to assign taxonomic IDs based on the sequence matches. This file is part of the NCBI taxonomy data.

• -i {input.trimmed_read1} and -j {input.trimmed_read2}: Specifies the input FASTQ files for paired-end reads. -i is for the forward read file, and -j is for the reverse read file.

• -o {output.kaijuout}: Defines the output file for Kaiju results. This file contains the classifications and matches for each read.

Parameters for kaiju2table:

• -n {input.taxonnames}: Points to the taxonomy names file (names.dmp), which Kaiju uses to convert taxonomic IDs into scientific names in the output table.

• -r phylum: Specifies the taxonomic rank at which results are summarized. Setting it to phylum means that results will be grouped at the phylum level in the output table.

• -o {output.classification}: Specifies the output file for the generated classification table, where each row corresponds to a read’s classification and its taxonomic path to the specified rank (phylum).

The output produced here allows us to see the percentage of reads that map to unclassified (likely our locust host here) and the percentage of microbial contamination ranked in a phylum level.

Visualize the metatranscriptomics result

Kaiju output can be exported to be view in a interactive and hierarchical multi-layered pie-charts using Krona. The results are generated by a .html page. We followed the Kaiju tutorial on the Github page:

########################################
# Snakefile rule
########################################

rule krona:
    input:
        kaijuout = WORKDir + "/KAIJU/{species}-kaiju/{locust}_kaiju.out",
        taxonid = KAIJUdir + "/nodes.dmp",
        taxonnames = KAIJUdir + "/names.dmp",
    output:
        conversion = WORKDir + "/KAIJU/{species}-kaiju/{locust}_krona.out",
        webreport = WORKDir + "/KAIJU/{species}-kaiju/{locust}_krona.html",
    shell:
        """
        module purge
        ml GCCcore/8.2.0 KronaTools/2.7.1

        kaiju2krona \\
        -t {input.taxonid} \\
        -n {input.taxonnames} \\
        -i {input.kaijuout} \\
        -o {output.conversion}

        ktImportText -o {output.webreport} {output.conversion} 
        """
        
########################################
# Parameters in the cluster.json file
########################################

    "krona":
    {
        "cpus-per-task" : 2,
        "partition" : "short",
        "ntasks": 1,
        "mem" : "500M",
        "time": "0-0:10:00"
   },

• -t {input.taxonid}: Specifies the taxonomy node file (nodes.dmp), which contains the hierarchical structure of taxonomic IDs. This file is used by Kaiju to organize and interpret taxonomic relationships for each classified read.

• -n {input.taxonnames}: Specifies the taxonomy names file (names.dmp), which maps taxonomic IDs to scientific names. This file allows Kaiju to output the actual names of taxa instead of numerical IDs.

• -i {input.kaijuout}: Indicates the input file with classification results generated by the kaiju command. This file contains the taxonomic assignments for each read.

• -o {output.conversion}: Specifies the output file to be used as input for Krona. This file will be formatted to include taxonomic information that Krona can interpret, allowing for a hierarchical visualization of microbial content.

• -o {output.webreport}: Specifies the output HTML file that Krona will generate. This file will contain an interactive, multi-layered pie chart for visualizing the microbial classification data at different taxonomic levels.

Screen for microbial contamination with Kraken2

The combination of Kraken2 and Bracken is another option to classify the reads compared to a database. Before launching the snakemake, you will need to create a database:

# enable proxy to allow compute node connection to internet
module load WebProxy

## Downloading the Kraken database
wget --no-check-certificate https://genome-idx.s3.amazonaws.com/kraken/k2_core_nt_20240904.tar.gz

Then you can launch this Snakemake by referencing to KRAKENDir.

########################################
# Snakefile rule
########################################

rule kraken2:
        input:
            trimmed_read1 = WORKDir + "/01-{species}-trimmed-tgalore/{locust}_1_val_1.fq.gz",
            trimmed_read2 = WORKDir + "/01-{species}-trimmed-tgalore/{locust}_2_val_2.fq.gz",
        params:
            report = WORKDir + "/KRAKEN2/{species}-kraken2/{locust}_report",
        output:
            taxoreport = WORKDir + "/KRAKEN2/{species}-kraken2/{locust}.kraken"),

        shell:
        """
        module purge
        ml GCC/11.2.0  OpenMPI/4.1.1 Kraken2/2.1.3

        kraken2 \\
        --use-names \\
        --threads 16 \\
        --db {KRAKENDir} \\
        --report {params.report} \\
        --gzip-compressed \\
        --paired {input.read1} {input.read2} > {output.taxoreport}
        """

rule braken:
        input:
            report = directory(WORKDir + "/KRAKEN2/{species}-kraken2/{locust}_report")
        output:
            taxonomy = WORKDir + "/KRAKEN2/{species}-kraken2/{locust}_GENUS.bracken")
        shell:
        """
        module purge
        ml GCCcore/10.3.0 Bracken/2.9

        bracken -v \\
        -d {KRAKENDir} \\
        -i {input.report} \\
        -l G \\
        -o {output.taxonomy}
        """
        
########################################
# Parameters in the cluster.json file
########################################

• --paired {input.read1} {input.read2}: Specifies that the input data is paired-end sequencing reads, with {input.read1} as the forward read file and {input.read2} as the reverse read file.

• --gzip-compressed: Indicates that the input files are gzip-compressed, so Kraken2 will decompress them on the fly.

• --use-names: This option tells Kraken2 to display the taxonomic names in the output rather than just taxonomic IDs, making it easier to interpret the results directly.

• --db {KRAKENDir}: Specifies the path to the Kraken2 database, where {KRAKENDir} is a placeholder for the directory containing the database files. This database contains the reference sequences and taxonomy information Kraken2 uses to classify the reads.

• --report {params.report}: Defines the output file for the Kraken2 report, which summarizes the classification results, typically showing the percentage of reads classified at each taxonomic level.

• > {output.taxoreport}: Redirects the standard output of Kraken2 to a file, {output.taxoreport}, which will contain the detailed classification information for each read. This includes the assigned taxonomic classification and confidence scores.

Session information

sessionInfo()

FALSE R version 4.4.2 (2024-10-31)
FALSE Platform: aarch64-apple-darwin20
FALSE Running under: macOS Sequoia 15.5
FALSE 
FALSE Matrix products: default
FALSE BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
FALSE LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
FALSE 
FALSE locale:
FALSE [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
FALSE 
FALSE time zone: Asia/Tokyo
FALSE tzcode source: internal
FALSE 
FALSE attached base packages:
FALSE [1] stats     graphics  grDevices utils     datasets  methods   base     
FALSE 
FALSE other attached packages:
FALSE  [1] ggConvexHull_0.1.0 reshape2_1.4.4     ggthemes_5.1.0     plotly_4.10.4     
FALSE  [5] DT_0.33            lubridate_1.9.4    forcats_1.0.0      stringr_1.5.1     
FALSE  [9] dplyr_1.1.4        purrr_1.0.4        readr_2.1.5        tidyr_1.3.1       
FALSE [13] tibble_3.2.1       ggplot2_3.5.2      tidyverse_2.0.0    rmdformats_1.0.4  
FALSE [17] knitr_1.49         workflowr_1.7.1   
FALSE 
FALSE loaded via a namespace (and not attached):
FALSE  [1] gtable_0.3.6       xfun_0.51          bslib_0.9.0        htmlwidgets_1.6.4 
FALSE  [5] processx_3.8.6     callr_3.7.6        tzdb_0.4.0         vctrs_0.6.5       
FALSE  [9] tools_4.4.2        ps_1.9.0           generics_0.1.3     pkgconfig_2.0.3   
FALSE [13] data.table_1.17.0  RColorBrewer_1.1-3 lifecycle_1.0.4    compiler_4.4.2    
FALSE [17] farver_2.1.2       git2r_0.35.0       getPass_0.2-4      httpuv_1.6.15     
FALSE [21] htmltools_0.5.8.1  sass_0.4.9         yaml_2.3.10        lazyeval_0.2.2    
FALSE [25] later_1.4.1        pillar_1.10.2      crayon_1.5.3       jquerylib_0.1.4   
FALSE [29] whisker_0.4.1      cachem_1.1.0       tidyselect_1.2.1   digest_0.6.37     
FALSE [33] stringi_1.8.4      bookdown_0.42      labeling_0.4.3     rprojroot_2.0.4   
FALSE [37] fastmap_1.2.0      grid_4.4.2         cli_3.6.5          magrittr_2.0.3    
FALSE [41] utf8_1.2.4         dichromat_2.0-0.1  withr_3.0.2        scales_1.4.0      
FALSE [45] promises_1.3.2     timechange_0.3.0   rmarkdown_2.29     httr_1.4.7        
FALSE [49] hms_1.1.3          evaluate_1.0.3     viridisLite_0.4.2  rlang_1.1.6       
FALSE [53] Rcpp_1.0.14        glue_1.8.0         formatR_1.14       rstudioapi_0.17.1 
FALSE [57] jsonlite_1.9.1     R6_2.6.1           plyr_1.8.9         fs_1.6.5