In the following blocks I want to use DIA Umpire to create transition libraries for openswath, then I want to run openswath and score the runs.
cd ~/scratch/proteomics/mycobacterium_tuberculosis_2018
module add openms
type="mzXML"
export VERSION="20190228"
basedir="${HOME}/scratch/proteomics/mycobacterium_tuberculosis_2018"
base_input="${basedir}/results/01${type}/dia/${VERSION}/"
umpire_inputs=$(/usr/bin/find "${base_input}" -name "*.${type}" | sort)
echo "Checking in: ${umpire_inputs}"
for input in ${umpire_inputs};
do
in_name=$(basename $input ".${type}")
out_name="${in_name}_Q1.mgf"
if [[ ! -f "${base_input}/${out_name}" ]]; then
echo "The output file: ${out_name} already exists."
else
java -jar DIA_Umpire_SE.jar ${input} diaumpire_se.params
fi
done
ProteinProphet \
results/04_dia_umpire_xinteract/interact.comet.pep.xml \
results/05_dia_umpire_prophet/combined.prot.xml
InterProphetParser \
DECOY=DECOY \
results/04_dia_umpire_xinteract/interact.comet.pep.xml \
results/05_dia_umpire_prophet/iProphet.pep.xml
Mayu.pl \
-A results/05_dia_umpire_prophet/iProphet.pep.xml \
-C reference/mtb_irt.fasta \
-E DECOY
mayu_output <- "../2019-05-12_12.37.03_main_1.07.csv"
number <- hpgltools::extract_mayu_pps_fdr(mayu_output)
message("The number is: ", number)
## 0.43291
## Rerunning because writing the file failed.
spectrast \
-cNSpecLib -cICID-QTOF \
-cf "Protein! ~ DECOY_" \
-cP0.4237 \
-c_IRTreference/irt.txt \
-c_IRR results/05_dia_umpire_prophet/iProphet.pep.xml
spectrast \
-cNSpecLib_cons \
-cICID-QTOF \
-cAC SpecLib.splib
spectrast2tsv.py \
-l 350,2000 \
-s b,y \
-x 1,2 \
-o 6 \
-n 6 \
-p 0.05 \
-d -e \
-k openswath \
-w windows/2018_0817BrikenTrypsinDIA19.txt \
-a SpecLib_cons_openswath.tsv \
SpecLib_cons.sptxt
TargetedFileConverter \
-in SpecLib_cons_openswath.tsv \
-in_type tsv \
-out SpecLib_cons_openswath.TraML \
-out_type TraML
OpenSwathDecoyGenerator \
-in SpecLib_cons_openswath.TraML \
-out SpecLib_cons_openswath_decoy.TraML \
-method shuffle
## -exclude_similar \
## -similarity_threshold 0.05 \
## -identity_threshold 0.7
TargetedFileConverter \
-in SpecLib_cons_openswath_decoy.TraML \
-in_type TraML \
-out SpecLib_cons_openswath_decoy.tsv \
-out_type tsv
TargetedFileConverter \
-in SpecLib_cons_openswath_decoy.TraML \
-in_type TraML \
-out SpecLib_cons_openswath_decoy.pqp \
-out_type pqp
export VERSION=${VERSION:-20190327}
echo "Loading environment modules and parameters for version: ${VERSION}."
source "parameters/${VERSION}_settings.sh"
echo "Invoking the OpenSwathWorkflow using local comet-derived transitions."
type="diaumpire"
input_type="mzXML"
export TRANSITION_PREFIX="SpecLib_cons_openswath_decoy"
echo "Checking in, the transition library is: ${TRANSITION_PREFIX}.pqp"
base_mzxmldir="results/01${input_type}/dia/${VERSION}"
swath_inputs=$(/usr/bin/find "${base_mzxmldir}" -name *.${input_type} -print | sort)
echo "Checking in, the inputs are: ${swath_inputs}"
mkdir -p "${SWATH_OUTDIR}_${type}"
pypdir="${PYPROPHET_OUTDIR}_${type}"
mkdir -p "${pypdir}"
for input in ${swath_inputs}
do
name=$(basename "${input}" ".${input_type}")
echo "Starting openswath run, library type ${type} for ${name} using ${MZ_WINDOWS} windows at $(date)."
swath_output_prefix="${SWATH_OUTDIR}_${type}/${name}_${DDA_METHOD}"
pyprophet_output_prefix="${PYPROPHET_OUTDIR}_${type}/${name}_${DDA_METHOD}"
echo "Deleting previous swath output file: ${swath_output_prefix}.osw"
rm -f "${swath_output_prefix}.osw"
rm -f "${swath_output_prefix}.tsv"
OpenSwathWorkflow \
-in "${input}" \
-force \
-sort_swath_maps \
-min_upper_edge_dist 1 \
-mz_correction_function "quadratic_regression_delta_ppm" \
-Scoring:TransitionGroupPicker:background_subtraction "original" \
-Scoring:stop_report_after_feature "5" \
-swath_windows_file "windows/openswath_${name}.txt" \
-tr "${TRANSITION_PREFIX}.pqp" \
-out_tsv "${swath_output_prefix}.tsv"
OpenSwathWorkflow \
-in "${input}" \
-force \
-sort_swath_maps \
-min_upper_edge_dist 1 \
-mz_correction_function "quadratic_regression_delta_ppm" \
-Scoring:TransitionGroupPicker:background_subtraction "original" \
-Scoring:stop_report_after_feature "5" \
-swath_windows_file "windows/openswath_${name}.txt" \
-tr "${TRANSITION_PREFIX}.pqp" \
-out_osw "${swath_output_prefix}.osw"
##2>"${swath_output_prefix}_osw.log" 1>&2
done
swath_out=$(dirname ${swath_output_prefix})
pyprophet_out="$(dirname "${pyprophet_output_prefix}")/openswath_merged.osw"
echo "Merging osw files to ${pyprophet_out}"
pyprophet merge \
--template "${TRANSITION_PREFIX}.pqp" \
--out="${pyprophet_out}" \
${swath_out}/*.osw
pyprophet score --in="${pyprophet_out}"
pyprophet export --in="${pyprophet_out}" --out "test.tsv"
## pyprophet always exports to the current working directory.
final_name="$(dirname ${pyprophet_out})/$(basename ${pyprophet_out} ".osw").tsv"
echo $final_name
mv "test.tsv"
ls -ld "${pyprophet_out}"
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}_${DDA_METHOD}.tsv"
Thanks to Vivek, I now am aware of DEP, which does everything I wish MSstats did. The matrix given to me by tric’s feature_alignment.py I think gives me what DEP requires, along with my annotations and sample sheet.
Let us see if this is true.
mtb_gff <- "reference/mycobacterium_tuberculosis_h37rv_2.gff.gz"
mtb_genome <- "reference/mtuberculosis_h37rv_genbank.fasta"
mtb_cds <- "reference/mtb_cds.fasta"
mtb_annotations <- sm(load_gff_annotations(mtb_gff, type="gene"))
colnames(mtb_annotations) <- gsub(pattern="\\.", replacement="", x=colnames(mtb_annotations))
mtb_annotations[["description"]] <- gsub(pattern="\\+", replacement=" ",
x=mtb_annotations[["description"]])
mtb_annotations[["function"]] <- gsub(pattern="\\+", replacement=" ",
x=mtb_annotations[["function"]])
rownames(mtb_annotations) <- mtb_annotations[["ID"]]
mtb_microbes <- load_microbesonline_annotations(id=83332)
## The species being downloaded is: Mycobacterium tuberculosis H37Rv
tric_data <- read.csv(
paste0("results/tric/", ver, "/whole_8mz_dia_umpire/comet_HCD.tsv"), sep="\t")
tric_data[["ProteinName"]] <- gsub(pattern="^(.*)_.*$", replacement="\\1",
x=tric_data[["ProteinName"]])
sample_annot <- extract_metadata(paste0("sample_sheets/Mtb_dia_samples_20190521.xlsx"))
rownames(sample_annot)
## [1] "s2018_0315Briken01" "s2018_0315Briken02"
## [3] "s2018_0315Briken03" "s2018_0315Briken04"
## [5] "s2018_0315Briken05" "s2018_0315Briken06"
## [7] "s2018_0315Briken21" "s2018_0315Briken22"
## [9] "s2018_0315Briken23" "s2018_0315Briken24"
## [11] "s2018_0315Briken25" "s2018_0315Briken26"
## [13] "s2018_0502BrikenDIA01" "s2018_0502BrikenDIA02"
## [15] "s2018_0502BrikenDIA03" "s2018_0502BrikenDIA04"
## [17] "s2018_0502BrikenDIA05" "s2018_0502BrikenDIA06"
## [19] "s2018_0502BrikenDIA07" "s2018_0502BrikenDIA08"
## [21] "s2018_0502BrikenDIA09" "s2018_0502BrikenDIA10"
## [23] "s2018_0502BrikenDIA11" "s2018_0502BrikenDIA12"
## [25] "s2018_0726Briken01" "s2018_0726Briken02"
## [27] "s2018_0726Briken03" "s2018_0726Briken04"
## [29] "s2018_0726Briken05" "s2018_0726Briken06"
## [31] "s2018_0726Briken07" "s2018_0726Briken08"
## [33] "s2018_0726Briken09" "s2018_0726Briken11"
## [35] "s2018_0726Briken12" "s2018_0726Briken13"
## [37] "s2018_0726Briken14" "s2018_0726Briken15"
## [39] "s2018_0726Briken16" "s2018_0726Briken17"
## [41] "s2018_0726Briken18" "s2018_0726Briken19"
## [43] "s2018_0817BrikenTrypsinDIA01" "s2018_0817BrikenTrypsinDIA02"
## [45] "s2018_0817BrikenTrypsinDIA03" "s2018_0817BrikenTrypsinDIA04"
## [47] "s2018_0817BrikenTrypsinDIA05" "s2018_0817BrikenTrypsinDIA06"
## [49] "s2018_0817BrikenTrypsinDIA07" "s2018_0817BrikenTrypsinDIA08"
## [51] "s2018_0817BrikenTrypsinDIA09" "s2018_0817BrikenTrypsinDIA11"
## [53] "s2018_0817BrikenTrypsinDIA12" "s2018_0817BrikenTrypsinDIA13"
## [55] "s2018_0817BrikenTrypsinDIA14" "s2018_0817BrikenTrypsinDIA15"
## [57] "s2018_0817BrikenTrypsinDIA16" "s2018_0817BrikenTrypsinDIA17"
## [59] "s2018_0817BrikenTrypsinDIA18" "s2018_0817BrikenTrypsinDIA19"
## Loading SWATH2stats
s2s_exp <- SWATH2stats::sample_annotation(data=tric_data,
sample_annotation=sample_annot, verbose=TRUE,
fullpeptidename_column="fullpeptidename")
## Found the same mzXML files in the annotations and data.
## results/01mzXML/dia/20190327/2018_0315Briken01.mzXML
## results/01mzXML/dia/20190327/2018_0315Briken02.mzXML
## results/01mzXML/dia/20190327/2018_0315Briken03.mzXML
## results/01mzXML/dia/20190327/2018_0315Briken04.mzXML
## results/01mzXML/dia/20190327/2018_0315Briken05.mzXML
## results/01mzXML/dia/20190327/2018_0315Briken06.mzXML
## results/01mzXML/dia/20190327/2018_0315Briken21.mzXML
## results/01mzXML/dia/20190327/2018_0315Briken22.mzXML
## results/01mzXML/dia/20190327/2018_0315Briken23.mzXML
## results/01mzXML/dia/20190327/2018_0315Briken24.mzXML
## results/01mzXML/dia/20190327/2018_0315Briken25.mzXML
## results/01mzXML/dia/20190327/2018_0315Briken26.mzXML
## results/01mzXML/dia/20190327/2018_0502BrikenDIA01.mzXML
## results/01mzXML/dia/20190327/2018_0502BrikenDIA02.mzXML
## results/01mzXML/dia/20190327/2018_0502BrikenDIA03.mzXML
## results/01mzXML/dia/20190327/2018_0502BrikenDIA04.mzXML
## results/01mzXML/dia/20190327/2018_0502BrikenDIA05.mzXML
## results/01mzXML/dia/20190327/2018_0502BrikenDIA06.mzXML
## results/01mzXML/dia/20190327/2018_0502BrikenDIA07.mzXML
## results/01mzXML/dia/20190327/2018_0502BrikenDIA08.mzXML
## results/01mzXML/dia/20190327/2018_0502BrikenDIA09.mzXML
## results/01mzXML/dia/20190327/2018_0502BrikenDIA10.mzXML
## results/01mzXML/dia/20190327/2018_0502BrikenDIA11.mzXML
## results/01mzXML/dia/20190327/2018_0502BrikenDIA12.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken01.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken02.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken03.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken04.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken05.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken06.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken07.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken08.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken09.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken11.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken12.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken13.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken14.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken15.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken16.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken17.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken18.mzXML
## results/01mzXML/dia/20190327/2018_0726Briken19.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA01.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA02.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA03.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA04.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA05.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA06.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA07.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA08.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA09.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA11.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA12.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA13.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA14.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA15.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA16.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA17.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA18.mzXML
## results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA19.mzXML
## 60 samples were read from the annotations.
## 224796 transitions were read from the data and merged with the annotations.
## Number of non-decoy peptides: 17081
## Number of decoy peptides: 1801
## Decoy rate: 0.1054
## This seems a bit high to me, yesno?
fdr_overall <- assess_fdr_overall(s2s_exp, output="Rconsole", plot=TRUE)
## The average FDR by run on assay level is 0.015
## The average FDR by run on peptide level is 0.016
## The average FDR by run on protein level is 0.001
## Target assay FDR: 0.02
## Required overall m-score cutoff: 0.0031623
## achieving assay FDR: 0.0194
## Target protein FDR: 0.02
## Required overall m-score cutoff: 0.01
## achieving protein FDR: 0.00115
## Original dimension: 221952, new dimension: 211415, difference: 10537.
## Peptides need to have been quantified in more conditions than: 48 in order to pass this percentage-based threshold.
## Fraction of peptides selected: 0.00058
## Original dimension: 224796, new dimension: 680, difference: 224116.
filtered_ms_fdr <- filter_mscore_fdr(filtered_ms, FFT=0.7,
overall_protein_fdr_target=prot_score,
upper_overall_peptide_fdr_limit=0.05)
## Target protein FDR: 0.01
## Required overall m-score cutoff: 0.01
## achieving protein FDR: 0
## filter_mscore_fdr is filtering the data...
## finding m-score cutoff to achieve desired protein FDR in protein master list..
## finding m-score cutoff to achieve desired global peptide FDR..
## Target peptide FDR: 0.05
## Required overall m-score cutoff: 0.01
## Achieving peptide FDR: 0
## Proteins selected:
## Total proteins selected: 2412
## Final target proteins: 2412
## Final decoy proteins: 0
## Peptides mapping to these protein entries selected:
## Total mapping peptides: 16868
## Final target peptides: 16868
## Final decoy peptides: 0
## Total peptides selected from:
## Total peptides: 16868
## Final target peptides: 16868
## Final decoy peptides: 0
## Individual run FDR quality of the peptides was not calculated
## as not every run contains a decoy.
## The decoys have been removed from the returned data.
## Number of proteins detected: 2363
## Protein identifiers: Rv0577, Rv0242c, Rv3012c, Rv2467, Rv3715c, Rv2220
## Number of proteins detected that are supported by a proteotypic peptide: 2337
## Number of proteotypic peptides detected: 16728
## Number of proteins detected: 2337
## First 6 protein identifiers: Rv0577, Rv0242c, Rv3012c, Rv2467, Rv3715c, Rv2220
## Before filtering:
## Number of proteins: 2337
## Number of peptides: 16728
##
## Percentage of peptides removed: 25.94%
##
## After filtering:
## Number of proteins: 2331
## Number of peptides: 12388
## Before filtering:
## Number of proteins: 2331
## Number of peptides: 12388
##
## Percentage of peptides removed: 0%
##
## After filtering:
## Number of proteins: 2284
## Number of peptides: 12388
matrix_prefix <- file.path("results", "swath2stats", ver)
if (!file.exists(matrix_prefix)) {
dir.create(matrix_prefix)
}
protein_matrix_all <- write_matrix_proteins(
s2s_exp, write.csv=TRUE,
filename=file.path(matrix_prefix, "protein_all.csv"))
## Protein overview matrix results/swath2stats/20190327/protein_all.csv written to working folder.
## [1] 2434 61
protein_matrix_mscore <- write_matrix_proteins(
filtered_ms, write.csv=TRUE,
filename=file.path(matrix_prefix, "protein_matrix_mscore.csv"))
## Protein overview matrix results/swath2stats/20190327/protein_matrix_mscore.csv written to working folder.
## [1] 2412 61
peptide_matrix_mscore <- write_matrix_peptides(
filtered_ms, write.csv=TRUE,
filename=file.path(matrix_prefix, "peptide_matrix_mscore.csv"))
## Peptide overview matrix results/swath2stats/20190327/peptide_matrix_mscore.csv written to working folder.
## [1] 16868 61
protein_matrix_filtered <- write_matrix_proteins(
filtered_all_filters, write.csv=TRUE,
filename=file.path(matrix_prefix, "protein_matrix_filtered.csv"))
## Protein overview matrix results/swath2stats/20190327/protein_matrix_filtered.csv written to working folder.
## [1] 2284 61
peptide_matrix_filtered <- write_matrix_peptides(
filtered_all_filters, write.csv=TRUE,
filename=file.path(matrix_prefix, "peptide_matrix_filtered.csv"))
## Peptide overview matrix results/swath2stats/20190327/peptide_matrix_filtered.csv written to working folder.
## [1] 144819 61
intensities <- protein_matrix_filtered
cols <- gsub(x=colnames(intensities), pattern="^.*(2018.*$)", replacement="s\\1")
cols[[1]] <- "Protein"
colnames(intensities) <- cols
rownames(intensities) <- intensities[["Protein"]]
intensities[["Protein"]] <- NULL
## Check the sample names of the intensity matrix and sample annotations.
colnames(intensities) %in% rownames(sample_annot)
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [57] TRUE TRUE TRUE TRUE
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [57] TRUE TRUE TRUE TRUE
##rownames(sample_annot)
##colnames(intensities)
reordered <- colnames(intensities)
metadata <- sample_annot[reordered, ]
protein_expt <- create_expt(sample_annot,
count_dataframe=intensities,
gene_info=mtb_annotations)
## Reading the sample metadata.
## Warning in `[<-.factor`(`*tmp*`, iseq, value = c("undefined",
## "undefined", : invalid factor level, NA generated
## Warning in `[<-.factor`(`*tmp*`, iseq, value = c("undefined",
## "undefined", : invalid factor level, NA generated
## The sample definitions comprises: 60 rows(samples) and 28 columns(metadata fields).
## Matched 2268 annotations and counts.
## Bringing together the count matrix and gene information.
## Some annotations were lost in merging, setting them to 'undefined'.
## The final expressionset has 2284 rows and 60 columns.
protein_norm <- normalize_expt(protein_expt, transform="log2", convert="cpm",
filter=TRUE, norm="quant")
## This function will replace the expt$expressionset slot with:
## log2(cpm(quant(cbcb(data))))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.
## Step 1: performing count filter with option: cbcb
## Removing 8 low-count genes (2276 remaining).
## Step 2: normalizing the data with quant.
## Using normalize.quantiles.robust due to a thread error in preprocessCore.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## transform_counts: Found 11255 values equal to 0, adding 1 to the matrix.
## Step 5: not doing batch correction.
## There were 60, now there are 48 samples.
## There were 48, now there are 36 samples.
protein_norm <- normalize_expt(protein_sub, transform="log2", convert="cpm",
filter=TRUE, norm="quant", batch="svaseq")
## This function will replace the expt$expressionset slot with:
## log2(svaseq(cpm(quant(cbcb(data)))))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Warning in normalize_expt(protein_sub, transform = "log2", convert =
## "cpm", : Quantile normalization and sva do not always play well together.
## Step 1: performing count filter with option: cbcb
## Removing 0 low-count genes (2276 remaining).
## Step 2: normalizing the data with quant.
## Using normalize.quantiles.robust due to a thread error in preprocessCore.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## Step 5: doing batch correction with svaseq.
## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.
## Passing off to all_adjusters.
## batch_counts: Before batch/surrogate estimation, 81936 entries are x>1: 100%.
## batch_counts: Before batch/surrogate estimation, 0 entries are x==0: 0%.
## The be method chose 4 surrogate variable(s).
## Attempting svaseq estimation with 4 surrogates.
## Loading DEP
wtf <- function (proteins_unique, columns, expdesign) {
assertthat::assert_that(is.data.frame(proteins_unique), is.integer(columns),
is.data.frame(expdesign))
if (any(!c("name", "ID") %in% colnames(proteins_unique))) {
stop("'name' and/or 'ID' columns are not present in '",
deparse(substitute(proteins_unique)), "'.\nRun make_unique() to obtain the required columns",
call. = FALSE)
}
if (any(!c("label", "condition", "replicate") %in% colnames(expdesign))) {
stop("'label', 'condition' and/or 'replicate' columns",
"are not present in the experimental design", call. = FALSE)
}
if (any(!apply(proteins_unique[, columns], 2, is.numeric))) {
stop("specified 'columns' should be numeric", "\nRun make_se_parse() with the appropriate columns as argument",
call. = FALSE)
}
if (tibble::is.tibble(proteins_unique))
proteins_unique <- as.data.frame(proteins_unique)
if (tibble::is.tibble(expdesign))
expdesign <- as.data.frame(expdesign)
rownames(proteins_unique) <- proteins_unique$name
raw <- proteins_unique[, columns]
raw[raw == 0] <- NA
raw <- log2(raw)
expdesign <- mutate(expdesign, condition = make.names(condition))
## I changed the following because it didn't make sense to me.
if (is.null(expdesign[["ID"]])) {
expdesign <- expdesign %>%
tidyr::unite(condition, replicate, remove=FALSE)
}
rownames(expdesign) <- expdesign$ID
matched <- match(make.names(delete_prefix(expdesign$label)),
make.names(delete_prefix(colnames(raw))))
if (any(is.na(matched))) {
stop("None of the labels in the experimental design match ",
"with column names in 'proteins_unique'", "\nRun make_se() with the correct labels in the experimental design",
"and/or correct columns specification")
}
colnames(raw)[matched] <- expdesign$ID
raw <- raw[, !is.na(colnames(raw))][rownames(expdesign)]
row_data <- proteins_unique[, -columns]
rownames(row_data) <- row_data$name
se <- SummarizedExperiment(assays = as.matrix(raw), colData = expdesign,
rowData = row_data)
return(se)
}
## Loading required package: GenomicRanges
## Loading required package: stats4
## Loading required package: S4Vectors
##
## Attaching package: 'S4Vectors'
## The following objects are masked from 'package:DEP':
##
## first, rename
## The following object is masked from 'package:base':
##
## expand.grid
## Loading required package: IRanges
##
## Attaching package: 'IRanges'
## The following objects are masked from 'package:DEP':
##
## collapse, desc, slice
## Loading required package: GenomeInfoDb
## Loading required package: DelayedArray
## Loading required package: matrixStats
##
## Attaching package: 'matrixStats'
## The following objects are masked from 'package:DEP':
##
## count, rowRanges
## The following objects are masked from 'package:hpgltools':
##
## anyMissing, rowMedians
## The following objects are masked from 'package:Biobase':
##
## anyMissing, rowMedians
## Loading required package: BiocParallel
##
## Attaching package: 'DelayedArray'
## The following objects are masked from 'package:matrixStats':
##
## colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
## The following objects are masked from 'package:base':
##
## aperm, apply
design <- pData(protein_expt)
design[["label"]] <- design[["sampleid"]]
design[["replicate"]] <- design[["sampleid"]]
my_se <- SummarizedExperiment(
assays=exprs(protein_expt),
colData=design,
rowData=fData(protein_expt))
mtb_unique <- as.data.frame(exprs(protein_expt))
mtb_unique[["name"]] <- rownames(mtb_unique)
mtb_unique[["ID"]] <- rownames(mtb_unique)
intensity_columns <- 1:60
## mtb_se <- wtf(mtb_unique, intensity_columns, design)
mtb_se <- make_se(mtb_unique, intensity_columns, design)
DEP::plot_frequency(mtb_se)
## [1] 2284 60
## [1] 848 60
## vsn2: 2284 x 60 matrix (1 stratum).
## Please use 'meanSdPlot' to verify the fit.
## Loading required package: imputeLCMD
## Loading required package: tmvtnorm
## Loading required package: mvtnorm
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following object is masked from 'package:S4Vectors':
##
## expand
## Loading required package: gmm
## Loading required package: sandwich
## Loading required package: norm
## Loading required package: pcaMethods
##
## Attaching package: 'pcaMethods'
## The following object is masked from 'package:stats':
##
## loadings
## Loading required package: impute
## [1] 1.708
mtb_imp_man <- DEP::impute(mtb_norm, fun="man", shift=1.8, scale=0.3)
DEP::plot_imputation(mtb_norm, mtb_imp)
## Tested contrasts: wt_filtrate_vs_wt_whole, wt_filtrate_vs_delta_filtrate, wt_filtrate_vs_comp_filtrate, wt_filtrate_vs_delta_whole, wt_filtrate_vs_comp_whole, wt_whole_vs_delta_filtrate, wt_whole_vs_comp_filtrate, wt_whole_vs_delta_whole, wt_whole_vs_comp_whole, delta_filtrate_vs_comp_filtrate, delta_filtrate_vs_delta_whole, delta_filtrate_vs_comp_whole, comp_filtrate_vs_delta_whole, comp_filtrate_vs_comp_whole, delta_whole_vs_comp_whole
mtb_diff <- DEP::test_diff(mtb_imp, type="manual",
test=c("wt_filtrate_vs_wt_whole",
"delta_filtrate_vs_wt_filtrate",
"comp_filtrate_vs_wt_filtrate",
"wt_filtrate_vs_delta_filtrate",
"wt_filtrate_vs_comp_filtrate",
"wt_whole_vs_delta_whole",
"wt_whole_vs_comp_whole"))
## Tested contrasts: wt_filtrate_vs_wt_whole, delta_filtrate_vs_wt_filtrate, comp_filtrate_vs_wt_filtrate, wt_filtrate_vs_delta_filtrate, wt_filtrate_vs_comp_filtrate, wt_whole_vs_delta_whole, wt_whole_vs_comp_whole
mtb_dep <- DEP::add_rejections(mtb_diff, alpha=0.05, lfc=0.6)
## mtb_pca <- DEP::plot_pca(mtb_dep)
## The PCA plotter provided by DEP has some problems.
DEP::plot_cor(mtb_dep)
## Saving to: excel/dep_result.xlsx
## Note: zip::zip() is deprecated, please use zip::zipr() instead
imputed_mtrx <- assay(mtb_imp)
colnames(imputed_mtrx) <- gsub(x=colnames(imputed_mtrx),
pattern="^.*(2018.*$)",
replacement="s\\1")
imputed_mtrx <- 2 ^ imputed_mtrx
dim(imputed_mtrx)
## [1] 2284 60
exprset <- protein_expt[["expressionset"]]
exprset_rows <- rownames(exprs(exprset))
imputed_mtrx <- imputed_mtrx[exprset_rows, ]
exprs(exprset) <- imputed_mtrx
protein_expt[["expressionset"]] <- exprset
my_norm <- normalize_expt(protein_expt, filter=TRUE, convert="cpm", norm="quant", transform="log2")
## This function will replace the expt$expressionset slot with:
## log2(cpm(quant(cbcb(data))))
## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize
## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.
## Step 1: performing count filter with option: cbcb
## Removing 0 low-count genes (2284 remaining).
## Step 2: normalizing the data with quant.
## Using normalize.quantiles.robust due to a thread error in preprocessCore.
## Step 3: converting the data with cpm.
## Step 4: transforming the data with log2.
## Step 5: not doing batch correction.
DEP has a neat function to plot missing values. Sadly, it does not return the actual matrix, only the plot. This is nice and all, but I need the matrix, ergo this minor change.
count_defined <- function(se) {
df <- as.data.frame(assay(se))
na_idx <- is.na(df)
defined_mtrx <- ifelse(is.na(df), 0, 1)
return(defined_mtrx)
}
def_mtrx <- count_defined(mtb_se)
summary(def_mtrx)
## wt_filtrate_2018_0315Briken01 wt_filtrate_2018_0315Briken02
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.355 Mean :0.374
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## wt_filtrate_2018_0315Briken03 wt_whole_2018_0315Briken04
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :1.000
## Mean :0.403 Mean :0.731
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## wt_whole_2018_0315Briken05 wt_whole_2018_0315Briken06
## Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000
## Mean :0.783 Mean :0.757
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## wt_whole_2018_0315Briken21 wt_whole_2018_0315Briken22
## Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000
## Mean :0.795 Mean :0.817
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## wt_filtrate_2018_0315Briken23 wt_filtrate_2018_0315Briken24
## Min. :0.00 Min. :0.000
## 1st Qu.:0.00 1st Qu.:0.000
## Median :0.00 Median :0.000
## Mean :0.39 Mean :0.412
## 3rd Qu.:1.00 3rd Qu.:1.000
## Max. :1.00 Max. :1.000
## wt_filtrate_2018_0315Briken25 wt_whole_2018_0315Briken26
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:1.000
## Median :0.000 Median :1.000
## Mean :0.362 Mean :0.799
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## delta_filtrate_2018_0502BrikenDIA01 delta_filtrate_2018_0502BrikenDIA02
## Min. :0.000 Min. :0.00
## 1st Qu.:0.000 1st Qu.:0.00
## Median :0.000 Median :0.00
## Mean :0.305 Mean :0.35
## 3rd Qu.:1.000 3rd Qu.:1.00
## Max. :1.000 Max. :1.00
## delta_filtrate_2018_0502BrikenDIA03 comp_filtrate_2018_0502BrikenDIA04
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.296 Mean :0.366
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## comp_filtrate_2018_0502BrikenDIA05 comp_filtrate_2018_0502BrikenDIA06
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.401 Mean :0.359
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## delta_whole_2018_0502BrikenDIA07 delta_whole_2018_0502BrikenDIA08
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :1.000 Median :1.000
## Mean :0.747 Mean :0.738
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## delta_whole_2018_0502BrikenDIA09 comp_whole_2018_0502BrikenDIA10
## Min. :0.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000
## Median :1.000 Median :0.0000
## Mean :0.535 Mean :0.0814
## 3rd Qu.:1.000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.0000
## comp_whole_2018_0502BrikenDIA11 comp_whole_2018_0502BrikenDIA12
## Min. :0.00 Min. :0.000
## 1st Qu.:1.00 1st Qu.:0.000
## Median :1.00 Median :1.000
## Mean :0.75 Mean :0.679
## 3rd Qu.:1.00 3rd Qu.:1.000
## Max. :1.00 Max. :1.000
## delta_filtrate_2018_0726Briken01 delta_filtrate_2018_0726Briken02
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.063 Mean :0.102
## 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000
## delta_filtrate_2018_0726Briken03 comp_filtrate_2018_0726Briken04
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000
## Mean :0.0517 Mean :0.0517
## 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000
## comp_filtrate_2018_0726Briken05 comp_filtrate_2018_0726Briken06
## Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.000
## Mean :0.0823 Mean :0.113
## 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :1.0000 Max. :1.000
## wt_filtrate_2018_0726Briken07 wt_filtrate_2018_0726Briken08
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.115 Mean :0.109
## 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000
## wt_filtrate_2018_0726Briken09 delta_whole_2018_0726Briken11
## Min. :0.00 Min. :0.000
## 1st Qu.:0.00 1st Qu.:0.000
## Median :0.00 Median :0.000
## Mean :0.14 Mean :0.255
## 3rd Qu.:0.00 3rd Qu.:1.000
## Max. :1.00 Max. :1.000
## delta_whole_2018_0726Briken12 delta_whole_2018_0726Briken13
## Min. :0.000 Min. :0.00
## 1st Qu.:0.000 1st Qu.:0.00
## Median :0.000 Median :0.00
## Mean :0.304 Mean :0.23
## 3rd Qu.:1.000 3rd Qu.:0.00
## Max. :1.000 Max. :1.00
## comp_whole_2018_0726Briken14 comp_whole_2018_0726Briken15
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.226 Mean :0.255
## 3rd Qu.:0.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## comp_whole_2018_0726Briken16 wt_whole_2018_0726Briken17
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.288 Mean :0.262
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## wt_whole_2018_0726Briken18 wt_whole_2018_0726Briken19
## Min. :0.00 Min. :0.000
## 1st Qu.:0.00 1st Qu.:0.000
## Median :0.00 Median :0.000
## Mean :0.35 Mean :0.186
## 3rd Qu.:1.00 3rd Qu.:0.000
## Max. :1.00 Max. :1.000
## delta_filtrate_2018_0817BrikenTrypsinDIA01
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.246
## 3rd Qu.:0.000
## Max. :1.000
## delta_filtrate_2018_0817BrikenTrypsinDIA02
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.254
## 3rd Qu.:1.000
## Max. :1.000
## delta_filtrate_2018_0817BrikenTrypsinDIA03
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.251
## 3rd Qu.:1.000
## Max. :1.000
## comp_filtrate_2018_0817BrikenTrypsinDIA04
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.311
## 3rd Qu.:1.000
## Max. :1.000
## comp_filtrate_2018_0817BrikenTrypsinDIA05
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.283
## 3rd Qu.:1.000
## Max. :1.000
## comp_filtrate_2018_0817BrikenTrypsinDIA06
## Min. :0.00
## 1st Qu.:0.00
## Median :0.00
## Mean :0.29
## 3rd Qu.:1.00
## Max. :1.00
## wt_filtrate_2018_0817BrikenTrypsinDIA07
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.201
## 3rd Qu.:0.000
## Max. :1.000
## wt_filtrate_2018_0817BrikenTrypsinDIA08
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.222
## 3rd Qu.:0.000
## Max. :1.000
## wt_filtrate_2018_0817BrikenTrypsinDIA09
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.236
## 3rd Qu.:0.000
## Max. :1.000
## delta_whole_2018_0817BrikenTrypsinDIA11
## Min. :0.000
## 1st Qu.:0.000
## Median :1.000
## Mean :0.656
## 3rd Qu.:1.000
## Max. :1.000
## delta_whole_2018_0817BrikenTrypsinDIA12
## Min. :0.000
## 1st Qu.:0.000
## Median :1.000
## Mean :0.655
## 3rd Qu.:1.000
## Max. :1.000
## delta_whole_2018_0817BrikenTrypsinDIA13
## Min. :0.000
## 1st Qu.:0.000
## Median :1.000
## Mean :0.697
## 3rd Qu.:1.000
## Max. :1.000
## comp_whole_2018_0817BrikenTrypsinDIA14
## Min. :0.000
## 1st Qu.:0.000
## Median :1.000
## Mean :0.671
## 3rd Qu.:1.000
## Max. :1.000
## comp_whole_2018_0817BrikenTrypsinDIA15
## Min. :0.000
## 1st Qu.:0.000
## Median :1.000
## Mean :0.723
## 3rd Qu.:1.000
## Max. :1.000
## comp_whole_2018_0817BrikenTrypsinDIA16
## Min. :0.000
## 1st Qu.:0.000
## Median :1.000
## Mean :0.719
## 3rd Qu.:1.000
## Max. :1.000
## wt_filtrate_2018_0817BrikenTrypsinDIA17
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.499
## 3rd Qu.:1.000
## Max. :1.000
## wt_whole_2018_0817BrikenTrypsinDIA18 wt_whole_2018_0817BrikenTrypsinDIA19
## Min. :0.00 Min. :0.000
## 1st Qu.:0.00 1st Qu.:0.000
## Median :1.00 Median :1.000
## Mean :0.65 Mean :0.654
## 3rd Qu.:1.00 3rd Qu.:1.000
## Max. :1.00 Max. :1.000
## iRT_Kit Rv0001 Rv0002 Rv0003 Rv0005 Rv0006
## 35 22 57 16 50 57
## wt_filtrate_2018_0315Briken01
## 810
## wt_filtrate_2018_0315Briken02
## 855
## wt_filtrate_2018_0315Briken03
## 920
## wt_whole_2018_0315Briken04
## 1669
## wt_whole_2018_0315Briken05
## 1789
## wt_whole_2018_0315Briken06
## 1728
## wt_whole_2018_0315Briken21
## 1817
## wt_whole_2018_0315Briken22
## 1867
## wt_filtrate_2018_0315Briken23
## 891
## wt_filtrate_2018_0315Briken24
## 941
## wt_filtrate_2018_0315Briken25
## 828
## wt_whole_2018_0315Briken26
## 1825
## delta_filtrate_2018_0502BrikenDIA01
## 697
## delta_filtrate_2018_0502BrikenDIA02
## 799
## delta_filtrate_2018_0502BrikenDIA03
## 677
## comp_filtrate_2018_0502BrikenDIA04
## 835
## comp_filtrate_2018_0502BrikenDIA05
## 915
## comp_filtrate_2018_0502BrikenDIA06
## 819
## delta_whole_2018_0502BrikenDIA07
## 1706
## delta_whole_2018_0502BrikenDIA08
## 1685
## delta_whole_2018_0502BrikenDIA09
## 1223
## comp_whole_2018_0502BrikenDIA10
## 186
## comp_whole_2018_0502BrikenDIA11
## 1714
## comp_whole_2018_0502BrikenDIA12
## 1552
## delta_filtrate_2018_0726Briken01
## 144
## delta_filtrate_2018_0726Briken02
## 233
## delta_filtrate_2018_0726Briken03
## 118
## comp_filtrate_2018_0726Briken04
## 118
## comp_filtrate_2018_0726Briken05
## 188
## comp_filtrate_2018_0726Briken06
## 259
## wt_filtrate_2018_0726Briken07
## 263
## wt_filtrate_2018_0726Briken08
## 248
## wt_filtrate_2018_0726Briken09
## 319
## delta_whole_2018_0726Briken11
## 583
## delta_whole_2018_0726Briken12
## 694
## delta_whole_2018_0726Briken13
## 526
## comp_whole_2018_0726Briken14
## 516
## comp_whole_2018_0726Briken15
## 582
## comp_whole_2018_0726Briken16
## 658
## wt_whole_2018_0726Briken17
## 598
## wt_whole_2018_0726Briken18
## 800
## wt_whole_2018_0726Briken19
## 426
## delta_filtrate_2018_0817BrikenTrypsinDIA01
## 561
## delta_filtrate_2018_0817BrikenTrypsinDIA02
## 580
## delta_filtrate_2018_0817BrikenTrypsinDIA03
## 574
## comp_filtrate_2018_0817BrikenTrypsinDIA04
## 711
## comp_filtrate_2018_0817BrikenTrypsinDIA05
## 646
## comp_filtrate_2018_0817BrikenTrypsinDIA06
## 662
## wt_filtrate_2018_0817BrikenTrypsinDIA07
## 459
## wt_filtrate_2018_0817BrikenTrypsinDIA08
## 508
## wt_filtrate_2018_0817BrikenTrypsinDIA09
## 538
## delta_whole_2018_0817BrikenTrypsinDIA11
## 1498
## delta_whole_2018_0817BrikenTrypsinDIA12
## 1495
## delta_whole_2018_0817BrikenTrypsinDIA13
## 1592
## comp_whole_2018_0817BrikenTrypsinDIA14
## 1532
## comp_whole_2018_0817BrikenTrypsinDIA15
## 1652
## comp_whole_2018_0817BrikenTrypsinDIA16
## 1642
## wt_filtrate_2018_0817BrikenTrypsinDIA17
## 1139
## wt_whole_2018_0817BrikenTrypsinDIA18
## 1485
## wt_whole_2018_0817BrikenTrypsinDIA19
## 1494
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 0abc58e173be7300595d30d407b7efd4e4a512d6
## This is hpgltools commit: Thu May 9 14:56:34 2019 -0400: 0abc58e173be7300595d30d407b7efd4e4a512d6
## Saving to dia_umpire_20190308-v20190327.rda.xz
R version 3.5.3 (2019-03-11)
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: stats4, parallel, stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: imputeLCMD(v.2.0), impute(v.1.56.0), pcaMethods(v.1.74.0), norm(v.1.0-9.5), tmvtnorm(v.1.4-10), gmm(v.1.6-2), sandwich(v.2.5-1), Matrix(v.1.2-17), mvtnorm(v.1.0-10), SummarizedExperiment(v.1.12.0), DelayedArray(v.0.8.0), BiocParallel(v.1.16.6), matrixStats(v.0.54.0), GenomicRanges(v.1.34.0), GenomeInfoDb(v.1.18.2), IRanges(v.2.16.0), S4Vectors(v.0.20.1), DEP(v.1.5.3), ruv(v.0.9.7), SWATH2stats(v.1.13.5), testthat(v.2.1.1), hpgltools(v.1.0), Biobase(v.2.42.0) and BiocGenerics(v.0.28.0)
loaded via a namespace (and not attached): shinydashboard(v.0.7.1), tidyselect(v.0.2.5), lme4(v.1.1-21), htmlwidgets(v.1.3), RSQLite(v.2.1.1), AnnotationDbi(v.1.44.0), grid(v.3.5.3), devtools(v.2.0.2), munsell(v.0.5.0), codetools(v.0.2-16), preprocessCore(v.1.44.0), DT(v.0.6), withr(v.2.1.2), colorspace(v.1.4-1), GOSemSim(v.2.8.0), knitr(v.1.23), rstudioapi(v.0.10), DOSE(v.3.8.2), mzID(v.1.20.1), labeling(v.0.3), urltools(v.1.7.3), GenomeInfoDbData(v.1.2.0), polyclip(v.1.10-0), bit64(v.0.9-7), farver(v.1.1.0), rprojroot(v.1.3-2), xfun(v.0.7), R6(v.2.4.0), doParallel(v.1.0.14), locfit(v.1.5-9.1), bitops(v.1.0-6), fgsea(v.1.8.0), gridGraphics(v.0.4-1), assertthat(v.0.2.1), promises(v.1.0.1), scales(v.1.0.0), ggraph(v.1.0.2), enrichplot(v.1.2.0), gtable(v.0.3.0), affy(v.1.60.0), sva(v.3.30.1), processx(v.3.3.1), rlang(v.0.3.4), genefilter(v.1.64.0), mzR(v.2.16.2), GlobalOptions(v.0.1.0), splines(v.3.5.3), rtracklayer(v.1.42.2), lazyeval(v.0.2.2), selectr(v.0.4-1), europepmc(v.0.3), BiocManager(v.1.30.4), yaml(v.2.2.0), reshape2(v.1.4.3), GenomicFeatures(v.1.34.8), backports(v.1.1.4), httpuv(v.1.5.1), qvalue(v.2.14.1), clusterProfiler(v.3.10.1), tools(v.3.5.3), usethis(v.1.5.0), ggplotify(v.0.0.3), ggplot2(v.3.1.1), affyio(v.1.52.0), gplots(v.3.0.1.1), RColorBrewer(v.1.1-2), sessioninfo(v.1.1.1), MSnbase(v.2.8.3), ggridges(v.0.5.1), Rcpp(v.1.0.1), plyr(v.1.8.4), base64enc(v.0.1-3), progress(v.1.2.2), zlibbioc(v.1.28.0), purrr(v.0.3.2), RCurl(v.1.95-4.12), ps(v.1.3.0), prettyunits(v.1.0.2), GetoptLong(v.0.1.7), viridis(v.0.5.1), cowplot(v.0.9.4), zoo(v.1.8-5), cluster(v.2.0.9), ggrepel(v.0.8.1), colorRamps(v.2.3), fs(v.1.3.1), variancePartition(v.1.12.3), magrittr(v.1.5), data.table(v.1.12.2), DO.db(v.2.9), openxlsx(v.4.1.0), circlize(v.0.4.6), triebeard(v.0.3.0), packrat(v.0.5.0), ProtGenerics(v.1.14.0), pkgload(v.1.0.2), mime(v.0.6), hms(v.0.4.2), evaluate(v.0.13), xtable(v.1.8-4), pbkrtest(v.0.4-7), XML(v.3.98-1.19), shape(v.1.4.4), gridExtra(v.2.3), compiler(v.3.5.3), biomaRt(v.2.38.0), tibble(v.2.1.1), KernSmooth(v.2.23-15), ncdf4(v.1.16.1), crayon(v.1.3.4), minqa(v.1.2.4), htmltools(v.0.3.6), later(v.0.8.0), mgcv(v.1.8-28), corpcor(v.1.6.9), tidyr(v.0.8.3), DBI(v.1.0.0), tweenr(v.1.0.1), ComplexHeatmap(v.1.20.0), MASS(v.7.3-51.4), boot(v.1.3-22), readr(v.1.3.1), cli(v.1.1.0), vsn(v.3.50.0), gdata(v.2.18.0), igraph(v.1.2.4.1), pkgconfig(v.2.0.2), rvcheck(v.0.1.3), GenomicAlignments(v.1.18.1), MALDIquant(v.1.19.3), xml2(v.1.2.0), foreach(v.1.4.4), annotate(v.1.60.1), XVector(v.0.22.0), rvest(v.0.3.4), stringr(v.1.4.0), callr(v.3.2.0), digest(v.0.6.19), Biostrings(v.2.50.2), rmarkdown(v.1.12), fastmatch(v.1.1-0), edgeR(v.3.24.3), curl(v.3.3), shiny(v.1.3.2), Rsamtools(v.1.34.1), gtools(v.3.8.1), rjson(v.0.2.20), nloptr(v.1.2.1), nlme(v.3.1-140), jsonlite(v.1.6), desc(v.1.2.0), viridisLite(v.0.3.0), limma(v.3.38.3), pillar(v.1.4.0), lattice(v.0.20-38), httr(v.1.4.0), pkgbuild(v.1.0.3), survival(v.2.44-1.1), GO.db(v.3.7.0), glue(v.1.3.1), remotes(v.2.0.4), fdrtool(v.1.2.15), zip(v.2.0.2), UpSetR(v.1.3.3), iterators(v.1.0.10), pander(v.0.6.3), bit(v.1.1-14), ggforce(v.0.2.2), stringi(v.1.4.3), blob(v.1.1.1), caTools(v.1.17.1.2), memoise(v.1.1.0) and dplyr(v.0.8.1)