M.tuberculosis 20180913: Preprocessing DIA data with two libraries.

1 OpenSwathWorkflow against the Tuberculist transitions

The following invocations of OpenMS/pyprophet/tric are included in:

dia_invocation_20180913.sh

This script includes the process for generating a local, comet-based transition library from DDA samples generated locally as well as the steps used when processing the downloaded transition libraries.

2 Gathering parameters

In this first block, I will set a couple of variables and source a file containing the parameters for the rest of the script.

export VERSION="20180913"
source parameters/20180913_settings.sh

3 Plotting metrics of the raw data

The raw data files provide opportunities to make sure that the later invocations of openMS/etc will actually work; for example, if there are too few transitions observed here, one should not be surprised if pyprophet and tric fail later.

sample_sheet <- glue::glue("sample_sheets/Mtb_dia_samples_{ver}.xlsx")
savefile <- "mzxml_dia_data_20180913.rda"

metadata <- openxlsx::read.xlsx(sample_sheet)
keeper_idx <- !grepl(pattern="^#", x=metadata[["sampleid"]])
metadata <- metadata[keeper_idx, ]
knitr::kable(metadata)

sampleid	TubeID	tubelabel	FigureReplicate	Figure.Name	Sample.Description	Bio.Replicate	LC.Run	MS.Run	Technical.Replicate	Replicate.State	rep	run	expt_id	Genotype	Collection.Type	Condition	batch	windowsize	enzyme	harvestdate	prepdate	rundate	runinfo	rawfile	Filename	mzmlfile	dia_scored	tuberculist_scored	include_exclude	Run_note
2019_0709Briken01	6.24.19–4	WT H37Rv Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken01	NA	NA	NA	wt_cf	early	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken01.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken01.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken01.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken01_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken02	6.24.19–5	H37Rv ΔEsx-5A; Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken02	NA	NA	NA	dt_cf	early	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken02.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken02.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken02.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken02_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken03	6.24.19–6	H37Rv ΔEsx-5A Complement; Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken03	NA	NA	NA	cp_cf	early	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken03.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken03.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken03.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken03_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken04	6.24.19–10	WT H37Rv Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken04	NA	NA	NA	wt_cf	early	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken04.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken04.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken04.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken04_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken05	6.24.19–11	H37Rv ΔEsx-5A; Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken05	NA	NA	NA	dt_cf	early	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken05.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken05.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken05.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken05_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken06	6.24.19–12	H37Rv ΔEsx-5A Complement; Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken06	NA	NA	NA	cp_cf	early	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken06.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken06.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken06.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken06_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken07	6.24.19–16	WT H37Rv Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken07	NA	NA	NA	wt_cf	early	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken07.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken07.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken07.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken07_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken08	6.24.19–17	H37Rv ΔEsx-5A; Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken08	NA	NA	NA	dt_cf	early	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken08.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken08.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken08.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken08_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken09	6.24.19–18	H37Rv ΔEsx-5A Complement; Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken09	NA	NA	NA	cp_cf	early	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken09.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken09.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken09.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken09_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken10	6.24.19–22	WT H37Rv Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken10	NA	NA	NA	wt_cf	late	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken10.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken10.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken10.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken10_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken11	6.24.19–23	H37Rv ΔEsx-5A; Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken11	NA	NA	NA	dt_cf	late	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken11.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken11.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken11.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken11_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken12	6.24.19–24	H37Rv ΔEsx-5A Complement; Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken12	NA	NA	NA	cp_cf	late	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken12.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken12.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken12.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken12_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken13	6.24.19–28	WT H37Rv Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken13	NA	NA	NA	wt_cf	late	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken13.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken13.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken13.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken13_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken14	6.24.19–29	H37Rv ΔEsx-5A; Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken14	NA	NA	NA	dt_cf	late	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken14.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken14.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken14.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken14_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken15	6.24.19–30	H37Rv ΔEsx-5A Complement; Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken15	NA	NA	NA	cp_cf	late	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken15.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken15.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken15.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken15_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken16	6.24.19–34	WT H37Rv Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken16	NA	NA	NA	wt_cf	late	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken16.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken16.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken16.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken16_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken17	6.24.19–35	H37Rv ΔEsx-5A; Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken17	NA	NA	NA	dt_cf	late	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken17.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken17.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken17.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken17_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA
2019_0709Briken18	6.24.19–36	H37Rv ΔEsx-5A Complement; Culture Filtrate	NA	NA	NA	b20190610	l20190709	m20190709	t20190709	NA	a	2019_0709Briken18	NA	NA	NA	cp_cf	late	8mz	NA	NA	NA	NA	NA	preprocessing/00_raw/2019_0709Briken18.raw	preprocessing/01mzXML/dia/20190718/2019_0709Briken18.mzXML	preprocessing/01mzML/dia/20190718/2019_0709Briken18.mzML	preprocessing/08pyprophet/20190718/whole_8mz_tuberculist/2019_0709Briken18_vs_20190718_whole_HCD_dia_scored.tsv	NA	NA	NA

if (file.exists(savefile)) {
  load(savefile)
} else {
  mzxml_data <- extract_msraw_data(metadata, parallel=FALSE,
                                   allow_window_overlap=FALSE,
                                   file_column="Filename",
                                   savefile=savefile)
  mzml_data <- extract_msraw_data(metadata, parallel=FALSE,
                                  format="mzML",
                                  allow_window_overlap=FALSE,
                                  file_column="mzmlfile",
                                  savefile="testing.rda")
}

intensity_boxplot <- plot_mzxml_boxplot(mzxml_data)

## Adding 2019_0709Briken01

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## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, adding 1 to the data.

## Changed 86349 zero count features.

pp(file=paste0("images/20180913_dia_mzxml_intensities-v", ver, ".png"), image=intensity_boxplot)

## Writing the image to: images/20180913_dia_mzxml_intensities-v20190718.png and calling dev.off().

retention_boxplot <- plot_mzxml_boxplot(mzxml_data, table="scans", column="peakscount")

## Adding 2019_0709Briken01

## Adding 2019_0709Briken02

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pp(file=paste0("images/20180913_dia_mzxml_retention-v", ver, ".png"), image=retention_boxplot)

## Writing the image to: images/20180913_dia_mzxml_retention-v20190718.png and calling dev.off().

mz_boxplot <- plot_mzxml_boxplot(mzxml_data, table="scans", column="basepeakmz")

## Adding 2019_0709Briken01

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## Adding 2019_0709Briken18

pp(file=paste0("images/20180913_dia_mzxml_mzbase-v", ver, ".png"), image=mz_boxplot)

## Writing the image to: images/20180913_dia_mzxml_mzbase-v20190718.png and calling dev.off().

scanintensity_boxplot <- plot_mzxml_boxplot(mzxml_data, table="scans", column="basepeakintensity")

## Adding 2019_0709Briken01

## Adding 2019_0709Briken02

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## Adding 2019_0709Briken14

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## Adding 2019_0709Briken18

pp(file=paste0("images/20180913_dia_mzxml_scanintensity-v", ver, ".png"), image=scanintensity_boxplot)

## Writing the image to: images/20180913_dia_mzxml_scanintensity-v20190718.png and calling dev.off().

##intensity_wrt_mz <- plot_intensity_mz(mzxml_data, x_scale="log", y_scale="log")
##pp(file=paste0("images/201805_dia_intensity_wrt_mz_dia-v", ver, ".png"), image=intensity_wrt_mz)

4 Converting downloaded transitions

In block 15 of dia_invocation_20180913.sh the command used is prested and copied here for interactive running.

echo "Converting the Tuberculist libraries to pqp."
TUBERCULIST_TRAML="preprocessing/05spectral_libraries/Mtb_TubercuList-R27_iRT_UPS_noMox_noMC_sall_osw_decoy.TraML"
echo "The input is: ${TUBERCULIST_TRAML}"
echo "The output is: ${TUBERCULIST_PQP}"
mkdir -p $(dirname ${TUBERCULIST_PQP})
TargetedFileConverter \
    -in "${TUBERCULIST_TRAML}" \
    -in_type TraML \
    -out "${TUBERCULIST_PQP}" \
    -out_type pqp \
    2>"${TUBERCULIST_PQP}_convert.log" 1>&2

5 OpenSwathWorkflow invocation

block 16 contains the commands used to run openswathworkflow and pyprophet. Those are repeated here in order to test them interactively when needed.

echo "Invoking the OpenSwathWorkflow using the tuberculist transitions."
base_mzxmldir="preprocessing/01mzXML/dia/${VERSION}"
swath_inputs=$(/bin/ls "${base_mzxmldir}")
echo "Checking in, the inputs are: ${swath_inputs}"
mkdir -p "${TUBERCULIST_OUTDIR}"
pypdir="${PYPROPHET_OUTDIR}_tuberculist"
echo "Creating pyprophet output directory: ${pypdir}."
mkdir -p "${pypdir}"
for input in ${swath_inputs}
do
    in_mzxml="${base_mzxmldir}/${input}"
    name=$(basename "${input}" .mzXML)
    echo "Starting openswath run of ${name} using ${MZ_WINDOWS} windows at $(date)."
    tb_output_prefix="${TUBERCULIST_OUTDIR}/${name}_vs_${VERSION}_${TYPE}_${DDA_METHOD}_dia"
    pyprophet_output_prefix="${pypdir}/${name}_vs_${VERSION}_${TYPE}_${DDA_METHOD}_dia"
    echo "Deleting previous swath output file: ${tb_output_prefix}.osw"
    rm -f "${tb_output_prefix}.osw"
    OpenSwathWorkflow \
        -force \
        -ini "parameters/openms_${VERSION}.ini" \
        -in "${in_mzxml}" \
        -swath_windows_file "windows/openswath_${name}.txt" \
        -tr "${TUBERCULIST_PQP}" \
        -out_osw "${tb_output_prefix}.osw" \
        2>"${tb_output_prefix}_osw.log" 1>&2
    if [[ "$?" -ne "0" ]]; then
        echo "OpenSwathWorkflow for ${name} failed."
    fi

    rm -f "${tb_output_prefix}_scored.osw"
    echo "Scoring individual swath run: ${tb_output_prefix}"
    pyprophet \
        score \
        --in "${tb_output_prefix}.osw" \
        --out "${pyprophet_output_prefix}_scored.osw" \
    2>>"${pyprophet_output_prefix}_pyprophet_all.log" 1>&2
    rm -f "${pyprophet_output_prefix}_scored.tsv"
    echo "Exporting individual swath run: to ${pyprophet_output_prefix}_scored.tsv"
    pyprophet \
        export \
        --in "${pyprophet_output_prefix}_scored.osw" \
        --out "${pyprophet_output_prefix}_scored.tsv" \
        2>>"${pyprophet_output_prefix}_pyprophet_export.log" 1>&2
    ## ok something is fubar, the stupid tsv files are being written in the cwd as run_filename.tsv
    ## No matter what I do!
    mv "${input}.tsv" "${pyprophet_output_prefix}_scored.tsv"
    if [[ "$?" -ne "0" ]]; then
        echo "Exporting ${pyprophet_output_prefix}_scored.tsv failed."
    fi
done

6 Merging the Tuberculist-derived data with TRIC

Finally, block 17 of the invocation script provides the command used to make the final, feature-aligned data which is used by SWATH2stats and friends. In addition, it generates a matrix of intensities by sample and some metadata. Once again, it is copy/pasted here to allow interactive testing.

tric_tb="${TRIC_OUTDIR}_tuberculist"
mkdir -p "${tric_tb}"
feature_alignment.py \
  --force \
  --in "./${pypdir}/"*.tsv \
  --out "${tric_tb}/${SEARCH_METHOD}_${DDA_METHOD}.tsv" \
  --out_matrix "${tric_tb}/${DDA_METHOD}_outmatrix.tsv" \
  --out_meta "${tric_tb}/${DDA_METHOD}_meta.tsv" \
  2>"${tric_tb}/feature_alignment.err" \
  1>"${tric_tb}/feature_alignment.out"
echo "Wrote final output to ${tric_tb}/${SEARCH_METHOD}_${DDA_METHOD}.tsv"

7 Index version: 20190718

8 TODO

if (!isTRUE(get0("skip_load"))) {
  message(paste0("This is hpgltools commit: ", get_git_commit()))
  this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
  message(paste0("Saving to ", this_save))
  tmp <- sm(saveme(filename=this_save))
  pander::pander(sessionInfo())
}

## If you wish to reproduce this exact build of hpgltools, invoke the following:

## > git clone http://github.com/abelew/hpgltools.git

## > git reset 083922869a37724ece10beed7b0bb758a179fdfb

## This is hpgltools commit: Thu Oct 17 11:43:00 2019 -0400: 083922869a37724ece10beed7b0bb758a179fdfb

## Saving to 02_preprocessing-v20190718-v20190718.rda.xz

R version 3.6.1 (2019-07-05)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C

attached base packages: parallel, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: hpgltools(v.1.0), Biobase(v.2.45.1) and BiocGenerics(v.0.31.6)

loaded via a namespace (and not attached): backports(v.1.1.5), fastmatch(v.1.1-0), BiocFileCache(v.1.9.1), plyr(v.1.8.4), igraph(v.1.2.4.1), lazyeval(v.0.2.2), splines(v.3.6.1), BiocParallel(v.1.19.3), usethis(v.1.5.1), GenomeInfoDb(v.1.21.2), ggplot2(v.3.2.1), urltools(v.1.7.3), sva(v.3.33.1), digest(v.0.6.21), foreach(v.1.4.7), htmltools(v.0.4.0), GOSemSim(v.2.11.0), viridis(v.0.5.1), GO.db(v.3.8.2), gdata(v.2.18.0), magrittr(v.1.5), memoise(v.1.1.0), doParallel(v.1.0.15), openxlsx(v.4.1.0.1), limma(v.3.41.18), remotes(v.2.1.0), graphlayouts(v.0.5.0), Biostrings(v.2.53.2), annotate(v.1.63.0), matrixStats(v.0.55.0), askpass(v.1.1), enrichplot(v.1.5.2), prettyunits(v.1.0.2), colorspace(v.1.4-1), blob(v.1.2.0), rappdirs(v.0.3.1), ggrepel(v.0.8.1), xfun(v.0.10), dplyr(v.0.8.3), jsonlite(v.1.6), callr(v.3.3.2), crayon(v.1.3.4), RCurl(v.1.95-4.12), genefilter(v.1.67.1), lme4(v.1.1-21), zeallot(v.0.1.0), survival(v.2.44-1.1), iterators(v.1.0.12), glue(v.1.3.1), polyclip(v.1.10-0), gtable(v.0.3.0), zlibbioc(v.1.31.0), XVector(v.0.25.0), DelayedArray(v.0.11.8), pkgbuild(v.1.0.6), scales(v.1.0.0), DOSE(v.3.11.2), DBI(v.1.0.0), Rcpp(v.1.0.2), viridisLite(v.0.3.0), xtable(v.1.8-4), progress(v.1.2.2), gridGraphics(v.0.4-1), europepmc(v.0.3), bit(v.1.1-14), stats4(v.3.6.1), httr(v.1.4.1), fgsea(v.1.11.1), RColorBrewer(v.1.1-2), gplots(v.3.0.1.1), ellipsis(v.0.3.0), pkgconfig(v.2.0.3), XML(v.3.98-1.20), farver(v.1.1.0), dbplyr(v.1.4.2), ggplotify(v.0.0.4), tidyselect(v.0.2.5), rlang(v.0.4.0), reshape2(v.1.4.3), AnnotationDbi(v.1.47.1), munsell(v.0.5.0), tools(v.3.6.1), cli(v.1.1.0), RSQLite(v.2.1.2), ggridges(v.0.5.1), devtools(v.2.2.1), evaluate(v.0.14), stringr(v.1.4.0), yaml(v.2.2.0), processx(v.3.4.1), knitr(v.1.25), bit64(v.0.9-7), fs(v.1.3.1), tidygraph(v.1.1.2), pander(v.0.6.3), zip(v.2.0.4), caTools(v.1.17.1.2), purrr(v.0.3.2), ggraph(v.2.0.0), nlme(v.3.1-141), xml2(v.1.2.2), DO.db(v.2.9), biomaRt(v.2.41.9), compiler(v.3.6.1), pbkrtest(v.0.4-7), rstudioapi(v.0.10), curl(v.4.2), variancePartition(v.1.15.8), testthat(v.2.2.1), tibble(v.2.1.3), tweenr(v.1.0.1), stringi(v.1.4.3), highr(v.0.8), ps(v.1.3.0), GenomicFeatures(v.1.37.4), desc(v.1.2.0), lattice(v.0.20-38), Matrix(v.1.2-17), nloptr(v.1.2.1), vctrs(v.0.2.0), pillar(v.1.4.2), lifecycle(v.0.1.0), BiocManager(v.1.30.8), triebeard(v.0.3.0), cowplot(v.1.0.0), data.table(v.1.12.4), bitops(v.1.0-6), rtracklayer(v.1.45.6), GenomicRanges(v.1.37.17), qvalue(v.2.17.0), colorRamps(v.2.3), R6(v.2.4.0), KernSmooth(v.2.23-16), gridExtra(v.2.3), IRanges(v.2.19.17), sessioninfo(v.1.1.1), codetools(v.0.2-16), boot(v.1.3-23), MASS(v.7.3-51.4), gtools(v.3.8.1), assertthat(v.0.2.1), pkgload(v.1.0.2), SummarizedExperiment(v.1.15.9), openssl(v.1.4.1), rprojroot(v.1.3-2), withr(v.2.1.2), GenomicAlignments(v.1.21.7), Rsamtools(v.2.1.7), S4Vectors(v.0.23.25), GenomeInfoDbData(v.1.2.1), mgcv(v.1.8-29), hms(v.0.5.1), clusterProfiler(v.3.13.0), grid(v.3.6.1), tidyr(v.1.0.0), minqa(v.1.2.4), rvcheck(v.0.1.5), rmarkdown(v.1.16), ggforce(v.0.3.1) and base64enc(v.0.1-3)