1 Rework DIA-Umpire to feed OpenSWATH.
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.
1.1 Invoke DIA Umpire
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 ${input} diaumpire_se.params
fi
done1.2 Convert DIA Umpire results
1.3 Search the Umpire results
1.4 Merge them
1.5 Combine the statistics
ProteinProphet \
results/04_dia_umpire_xinteract/interact.comet.pep.xml \
results/05_dia_umpire_prophet/combined.prot.xml
InterProphetParser \
DECOY=DECOY \
results/04_dia_umpire_xinteract/interact.comet.pep.xml \
results/05_dia_umpire_prophet/iProphet.pep.xml
Mayu.pl \
-A results/05_dia_umpire_prophet/iProphet.pep.xml \
-C reference/mtb_irt.fasta \
-E DECOYmayu_output <- "../2019-05-12_12.37.03_main_1.07.csv"
number <- hpgltools::extract_mayu_pps_fdr(mayu_output)
message("The number is: ", number)
## 0.43291## Rerunning because writing the file failed.
spectrast \
-cNSpecLib -cICID-QTOF \
-cf "Protein! ~ DECOY_" \
-cP0.4237 \
-c_IRTreference/irt.txt \
-c_IRR results/05_dia_umpire_prophet/iProphet.pep.xml
spectrast \
-cNSpecLib_cons \
-cICID-QTOF \
-cAC SpecLib.splib
spectrast2tsv.py \
-l 350,2000 \
-s b,y \
-x 1,2 \
-o 6 \
-n 6 \
-p 0.05 \
-d -e \
-k openswath \
-w windows/2018_0817BrikenTrypsinDIA19.txt \
-a SpecLib_cons_openswath.tsv \
SpecLib_cons.sptxt
TargetedFileConverter \
-in SpecLib_cons_openswath.tsv \
-in_type tsv \
-out SpecLib_cons_openswath.TraML \
-out_type TraML
OpenSwathDecoyGenerator \
-in SpecLib_cons_openswath.TraML \
-out SpecLib_cons_openswath_decoy.TraML \
-method shuffle
## -exclude_similar \
## -similarity_threshold 0.05 \
## -identity_threshold 0.7
TargetedFileConverter \
-in SpecLib_cons_openswath_decoy.TraML \
-in_type TraML \
-out SpecLib_cons_openswath_decoy.tsv \
-out_type tsv
TargetedFileConverter \
-in SpecLib_cons_openswath_decoy.TraML \
-in_type TraML \
-out SpecLib_cons_openswath_decoy.pqp \
-out_type pqp
export VERSION=${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}_${DDA_METHOD}.tsv"2 DEP usage
Thanks to Vivek, I now am aware of DEP, which does everything I wish MSstats did. The matrix given to me by tric’s feature_alignment.py I think gives me what DEP requires, along with my annotations and sample sheet.
Let us see if this is true.
2.1 Protein annotations
mtb_gff <- "reference/mycobacterium_tuberculosis_h37rv_2.gff.gz"
mtb_genome <- "reference/mtuberculosis_h37rv_genbank.fasta"
mtb_cds <- "reference/mtb_cds.fasta"
mtb_annotations <- sm(load_gff_annotations(mtb_gff, type="gene"))
colnames(mtb_annotations) <- gsub(pattern="\\.", replacement="", x=colnames(mtb_annotations))
mtb_annotations[["description"]] <- gsub(pattern="\\+", replacement=" ",
x=mtb_annotations[["description"]])
mtb_annotations[["function"]] <- gsub(pattern="\\+", replacement=" ",
x=mtb_annotations[["function"]])
rownames(mtb_annotations) <- mtb_annotations[["ID"]]
mtb_microbes <- load_microbesonline_annotations(id=83332)## The species being downloaded is: Mycobacterium tuberculosis H37Rv2.2 Preprocess intensities in preparation for DEP
ver <- "20190327"
ump_data <- read.csv(
paste0("results/tric/", ver, "/whole_8mz_dia_umpire/comet_HCD.tsv"), sep="\t")
ump_data[["ProteinName"]] <- gsub(pattern="^(.*)_.*$", replacement="\\1",
x=ump_data[["ProteinName"]])
sample_annot <- extract_metadata(paste0("sample_sheets/Mtb_dia_samples_umpire_20190522.xlsx"))
colnames(sample_annot)## [1] "sampleid" "tubeid" "tubelabel"
## [4] "figurereplicate" "figurename" "sampledescription"
## [7] "bioreplicate" "lcrun" "msrun"
## [10] "technicalreplicate" "replicatestate" "rep"
## [13] "run" "exptid" "genotype"
## [16] "collectiontype" "condition" "batch"
## [19] "windowsize" "enzyme" "harvestdate"
## [22] "prepdate" "rundate" "runinfo"
## [25] "rawfile" "filename" "mzmlfile"
## [28] "diascored" "includeexclude"## [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"## [1] "results/01mzXML/dia/20190327/2018_0315Briken01.mzXML"
## [2] "results/01mzXML/dia/20190327/2018_0315Briken02.mzXML"
## [3] "results/01mzXML/dia/20190327/2018_0315Briken03.mzXML"
## [4] "results/01mzXML/dia/20190327/2018_0315Briken04.mzXML"
## [5] "results/01mzXML/dia/20190327/2018_0315Briken05.mzXML"
## [6] "results/01mzXML/dia/20190327/2018_0315Briken06.mzXML"
## [7] "results/01mzXML/dia/20190327/2018_0315Briken21.mzXML"
## [8] "results/01mzXML/dia/20190327/2018_0315Briken22.mzXML"
## [9] "results/01mzXML/dia/20190327/2018_0315Briken23.mzXML"
## [10] "results/01mzXML/dia/20190327/2018_0315Briken24.mzXML"
## [11] "results/01mzXML/dia/20190327/2018_0315Briken25.mzXML"
## [12] "results/01mzXML/dia/20190327/2018_0315Briken26.mzXML"
## [13] "results/01mzXML/dia/20190327/2018_0502BrikenDIA01.mzXML"
## [14] "results/01mzXML/dia/20190327/2018_0502BrikenDIA02.mzXML"
## [15] "results/01mzXML/dia/20190327/2018_0502BrikenDIA03.mzXML"
## [16] "results/01mzXML/dia/20190327/2018_0502BrikenDIA04.mzXML"
## [17] "results/01mzXML/dia/20190327/2018_0502BrikenDIA05.mzXML"
## [18] "results/01mzXML/dia/20190327/2018_0502BrikenDIA06.mzXML"
## [19] "results/01mzXML/dia/20190327/2018_0502BrikenDIA07.mzXML"
## [20] "results/01mzXML/dia/20190327/2018_0502BrikenDIA08.mzXML"
## [21] "results/01mzXML/dia/20190327/2018_0502BrikenDIA09.mzXML"
## [22] "results/01mzXML/dia/20190327/2018_0502BrikenDIA10.mzXML"
## [23] "results/01mzXML/dia/20190327/2018_0502BrikenDIA11.mzXML"
## [24] "results/01mzXML/dia/20190327/2018_0502BrikenDIA12.mzXML"
## [25] "results/01mzXML/dia/20190327/2018_0726Briken01.mzXML"
## [26] "results/01mzXML/dia/20190327/2018_0726Briken02.mzXML"
## [27] "results/01mzXML/dia/20190327/2018_0726Briken03.mzXML"
## [28] "results/01mzXML/dia/20190327/2018_0726Briken04.mzXML"
## [29] "results/01mzXML/dia/20190327/2018_0726Briken05.mzXML"
## [30] "results/01mzXML/dia/20190327/2018_0726Briken06.mzXML"
## [31] "results/01mzXML/dia/20190327/2018_0726Briken07.mzXML"
## [32] "results/01mzXML/dia/20190327/2018_0726Briken08.mzXML"
## [33] "results/01mzXML/dia/20190327/2018_0726Briken09.mzXML"
## [34] "results/01mzXML/dia/20190327/2018_0726Briken11.mzXML"
## [35] "results/01mzXML/dia/20190327/2018_0726Briken12.mzXML"
## [36] "results/01mzXML/dia/20190327/2018_0726Briken13.mzXML"
## [37] "results/01mzXML/dia/20190327/2018_0726Briken14.mzXML"
## [38] "results/01mzXML/dia/20190327/2018_0726Briken15.mzXML"
## [39] "results/01mzXML/dia/20190327/2018_0726Briken16.mzXML"
## [40] "results/01mzXML/dia/20190327/2018_0726Briken17.mzXML"
## [41] "results/01mzXML/dia/20190327/2018_0726Briken18.mzXML"
## [42] "results/01mzXML/dia/20190327/2018_0726Briken19.mzXML"
## [43] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA01.mzXML"
## [44] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA02.mzXML"
## [45] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA03.mzXML"
## [46] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA04.mzXML"
## [47] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA05.mzXML"
## [48] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA06.mzXML"
## [49] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA07.mzXML"
## [50] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA08.mzXML"
## [51] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA09.mzXML"
## [52] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA11.mzXML"
## [53] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA12.mzXML"
## [54] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA13.mzXML"
## [55] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA14.mzXML"
## [56] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA15.mzXML"
## [57] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA16.mzXML"
## [58] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA17.mzXML"
## [59] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA18.mzXML"
## [60] "results/01mzXML/dia/20190327/2018_0817BrikenTrypsinDIA19.mzXML"## Loading SWATH2statsump_s2s <- SWATH2stats::sample_annotation(data=ump_data,
sample_annotation=sample_annot,
fullpeptidename_column="fullpeptidename")## Found the same mzXML files in the annotations and data.## Number of non-decoy peptides: 17081
## Number of decoy peptides: 1801
## Decoy rate: 0.1054## This seems a bit high to me, yesno?
ump_fdr <- assess_fdr_overall(ump_s2s, 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.ump_filtered_ms_fdr <- filter_mscore_fdr(ump_filtered_ms, FFT=0.7,
overall_protein_fdr_target=ump_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: 12388ump_matrix_prefix <- file.path("results", "swath2stats", ver)
if (!file.exists(ump_matrix_prefix)) {
dir.create(ump_matrix_prefix)
}
protein_matrix_all <- write_matrix_proteins(
ump_s2s, write.csv=TRUE,
filename=file.path(ump_matrix_prefix, "ump_protein_all.csv"))## Protein overview matrix results/swath2stats/20190327/ump_protein_all.csv written to working folder.## [1] 2434 61protein_matrix_mscore <- write_matrix_proteins(
ump_filtered_ms, write.csv=TRUE,
filename=file.path(ump_matrix_prefix, "ump_protein_matrix_mscore.csv"))## Protein overview matrix results/swath2stats/20190327/ump_protein_matrix_mscore.csv written to working folder.## [1] 2412 61peptide_matrix_mscore <- write_matrix_peptides(
ump_filtered_ms, write.csv=TRUE,
filename=file.path(ump_matrix_prefix, "ump_peptide_matrix_mscore.csv"))## Peptide overview matrix results/swath2stats/20190327/ump_peptide_matrix_mscore.csv written to working folder.## [1] 16868 61protein_matrix_filtered <- write_matrix_proteins(
ump_filtered_all_filters, write.csv=TRUE,
filename=file.path(ump_matrix_prefix, "ump_protein_matrix_filtered.csv"))## Protein overview matrix results/swath2stats/20190327/ump_protein_matrix_filtered.csv written to working folder.## [1] 2284 61peptide_matrix_filtered <- write_matrix_peptides(
ump_filtered_all_filters, write.csv=TRUE,
filename=file.path(ump_matrix_prefix, "ump_peptide_matrix_filtered.csv"))## Peptide overview matrix results/swath2stats/20190327/ump_peptide_matrix_filtered.csv written to working folder.## [1] 144819 613 Look at raw data
mzml_data <- extract_msraw_data(sample_annot, parallel=FALSE,
format="mzML",
allow_window_overlap=FALSE,
file_column="mzmlfile",
savefile="testing.rda")## Reading results/01mzML/dia/20190327/2018_0315Briken01.mzML## Warning in extract_msraw_data(sample_annot, parallel = FALSE,
## format = "mzML", : There was an error reading results/01mzML/dia/
## 20190327/2018_0315Briken01.mzML.## Reading results/01mzML/dia/20190327/2018_0315Briken02.mzML## Warning in extract_msraw_data(sample_annot, parallel = FALSE,
## format = "mzML", : There was an error reading results/01mzML/dia/
## 20190327/2018_0315Briken02.mzML.## Reading results/01mzML/dia/20190327/2018_0315Briken03.mzML## Warning in extract_msraw_data(sample_annot, parallel = FALSE,
## format = "mzML", : There was an error reading results/01mzML/dia/
## 20190327/2018_0315Briken03.mzML.## Reading results/01mzML/dia/20190327/2018_0315Briken04.mzML## Warning in extract_msraw_data(sample_annot, parallel = FALSE,
## format = "mzML", : There was an error reading results/01mzML/dia/
## 20190327/2018_0315Briken04.mzML.## Reading results/01mzML/dia/20190327/2018_0315Briken05.mzML## Warning in extract_msraw_data(sample_annot, parallel = FALSE,
## format = "mzML", : There was an error reading results/01mzML/dia/
## 20190327/2018_0315Briken05.mzML.## Reading results/01mzML/dia/20190327/2018_0315Briken06.mzML## Warning in extract_msraw_data(sample_annot, parallel = FALSE,
## format = "mzML", : There was an error reading results/01mzML/dia/
## 20190327/2018_0315Briken06.mzML.## Reading results/01mzML/dia/20190327/2018_0315Briken21.mzML## Warning in extract_msraw_data(sample_annot, parallel = FALSE,
## format = "mzML", : There was an error reading results/01mzML/dia/
## 20190327/2018_0315Briken21.mzML.## Reading results/01mzML/dia/20190327/2018_0315Briken22.mzML## Warning in extract_msraw_data(sample_annot, parallel = FALSE,
## format = "mzML", : There was an error reading results/01mzML/dia/
## 20190327/2018_0315Briken22.mzML.## Reading results/01mzML/dia/20190327/2018_0315Briken23.mzML## Warning in extract_msraw_data(sample_annot, parallel = FALSE,
## format = "mzML", : There was an error reading results/01mzML/dia/
## 20190327/2018_0315Briken23.mzML.## Reading results/01mzML/dia/20190327/2018_0315Briken24.mzML## Warning in extract_msraw_data(sample_annot, parallel = FALSE,
## format = "mzML", : There was an error reading results/01mzML/dia/
## 20190327/2018_0315Briken24.mzML.## Reading results/01mzML/dia/20190327/2018_0315Briken25.mzML## Warning in extract_msraw_data(sample_annot, parallel = FALSE,
## format = "mzML", : There was an error reading results/01mzML/dia/
## 20190327/2018_0315Briken25.mzML.## Reading results/01mzML/dia/20190327/2018_0315Briken26.mzML## Warning in extract_msraw_data(sample_annot, parallel = FALSE,
## format = "mzML", : There was an error reading results/01mzML/dia/
## 20190327/2018_0315Briken26.mzML.## Reading results/01mzML/dia/20190327/2018_0502BrikenDIA01.mzML## Reading results/01mzML/dia/20190327/2018_0502BrikenDIA02.mzML## Reading results/01mzML/dia/20190327/2018_0502BrikenDIA03.mzML## Reading results/01mzML/dia/20190327/2018_0502BrikenDIA04.mzML## Reading results/01mzML/dia/20190327/2018_0502BrikenDIA05.mzML## Reading results/01mzML/dia/20190327/2018_0502BrikenDIA06.mzML## Reading results/01mzML/dia/20190327/2018_0502BrikenDIA07.mzML## Reading results/01mzML/dia/20190327/2018_0502BrikenDIA08.mzML## Reading results/01mzML/dia/20190327/2018_0502BrikenDIA09.mzML## Reading results/01mzML/dia/20190327/2018_0502BrikenDIA10.mzML## Reading results/01mzML/dia/20190327/2018_0502BrikenDIA11.mzML## Reading results/01mzML/dia/20190327/2018_0502BrikenDIA12.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken01.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken02.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken03.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken04.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken05.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken06.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken07.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken08.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken09.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken11.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken12.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken13.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken14.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken15.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken16.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken17.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken18.mzML## Reading results/01mzML/dia/20190327/2018_0726Briken19.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA01.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA02.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA03.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA04.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA05.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA06.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA07.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA08.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA09.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA11.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA12.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA13.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA14.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA15.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA16.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA17.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA18.mzML## Reading results/01mzML/dia/20190327/2018_0817BrikenTrypsinDIA19.mzML4 Get pyprophet data
in_ver <- "20190522"
pyp_metadata <- glue::glue("sample_sheets/Mtb_dia_samples_umpire_{in_ver}.xlsx")
pyprophet_fun <- extract_pyprophet_data(metadata=pyp_metadata,
pyprophet_column="diascored")## Attempting to read the tsv file for: 2018_0315Briken01: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0315Briken01.mzXML.tsv.## Attempting to read the tsv file for: 2018_0315Briken02: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0315Briken02.mzXML.tsv.## Attempting to read the tsv file for: 2018_0315Briken03: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0315Briken03.mzXML.tsv.## Attempting to read the tsv file for: 2018_0315Briken04: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0315Briken04.mzXML.tsv.## Attempting to read the tsv file for: 2018_0315Briken05: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0315Briken05.mzXML.tsv.## Attempting to read the tsv file for: 2018_0315Briken06: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0315Briken06.mzXML.tsv.## Attempting to read the tsv file for: 2018_0315Briken21: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0315Briken21.mzXML.tsv.## Attempting to read the tsv file for: 2018_0315Briken22: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0315Briken22.mzXML.tsv.## Attempting to read the tsv file for: 2018_0315Briken23: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0315Briken23.mzXML.tsv.## Attempting to read the tsv file for: 2018_0315Briken24: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0315Briken24.mzXML.tsv.## Attempting to read the tsv file for: 2018_0315Briken25: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0315Briken25.mzXML.tsv.## Attempting to read the tsv file for: 2018_0315Briken26: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0315Briken26.mzXML.tsv.## Attempting to read the tsv file for: 2018_0502BrikenDIA01: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0502BrikenDIA01.mzXML.tsv.## Attempting to read the tsv file for: 2018_0502BrikenDIA02: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0502BrikenDIA02.mzXML.tsv.## Attempting to read the tsv file for: 2018_0502BrikenDIA03: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0502BrikenDIA03.mzXML.tsv.## Attempting to read the tsv file for: 2018_0502BrikenDIA04: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0502BrikenDIA04.mzXML.tsv.## Attempting to read the tsv file for: 2018_0502BrikenDIA05: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0502BrikenDIA05.mzXML.tsv.## Attempting to read the tsv file for: 2018_0502BrikenDIA06: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0502BrikenDIA06.mzXML.tsv.## Attempting to read the tsv file for: 2018_0502BrikenDIA07: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0502BrikenDIA07.mzXML.tsv.## Attempting to read the tsv file for: 2018_0502BrikenDIA08: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0502BrikenDIA08.mzXML.tsv.## Attempting to read the tsv file for: 2018_0502BrikenDIA09: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0502BrikenDIA09.mzXML.tsv.## Attempting to read the tsv file for: 2018_0502BrikenDIA10: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0502BrikenDIA10.mzXML.tsv.## Attempting to read the tsv file for: 2018_0502BrikenDIA11: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0502BrikenDIA11.mzXML.tsv.## Attempting to read the tsv file for: 2018_0502BrikenDIA12: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0502BrikenDIA12.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken01: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken01.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken02: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken02.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken03: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken03.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken04: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken04.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken05: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken05.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken06: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken06.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken07: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken07.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken08: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken08.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken09: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken09.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken11: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken11.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken12: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken12.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken13: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken13.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken14: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken14.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken15: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken15.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken16: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken16.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken17: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken17.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken18: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken18.mzXML.tsv.## Attempting to read the tsv file for: 2018_0726Briken19: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0726Briken19.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA01: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA01.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA02: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA02.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA03: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA03.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA04: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA04.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA05: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA05.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA06: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA06.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA07: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA07.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA08: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA08.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA09: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA09.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA11: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA11.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA12: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA12.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA13: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA13.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA14: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA14.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA15: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA15.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA16: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA16.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA17: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA17.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA18: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA18.mzXML.tsv.## Attempting to read the tsv file for: 2018_0817BrikenTrypsinDIA19: results/08pyprophet/20190327/whole_8mz_diaumpire/2018_0817BrikenTrypsinDIA19.mzXML.tsv.intensities_esxG <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename", scale="log",
title="esxG Intensities", column="intensity", protein="Rv0287")## Adding filtwt0301## Adding filtwt0302## Adding filtwt0303## Adding whlwt0304## Adding whlwt0305## Adding whlwt0306## Adding whlwt0321## Adding whlwt0322## Adding filtwt0323## Adding filtwt0324## Adding filtwt0325## Adding whlwt0326## Adding filtdt0501## Adding filtdt0502## Adding filtdt0503## Adding filtcm0504## Adding filtcm0505## Adding filtcm0506## Adding whldt0507## Adding whldt0508## Adding whldt0509## Adding whlcm0510## Adding whlcm0511## Adding whlcm0512## Adding filtdt0701## Adding filtdt0702## Adding filtdt0703## Adding filtcm0704## Adding filtcm0705## Adding filtcm0706## Adding filtwt0707## Adding filtwt0708## Adding filt0709## Adding whldt0711## Adding whldt0712## Adding whldt0713## Adding wholcm0714## Adding whlcm0715## Adding whlcm0716## Adding whlwt0717## Adding whlwt0718## Adding whlwt0719## Adding filtdt0801## Adding filtdt0802## Adding filtdt0803## Adding filtcm0804## Adding filtcm0805## Adding filtcm0806## Adding filtwt0807## Adding filtwt0808## Adding filtwt0809## Adding whldt0811## Adding whldt0812## Adding whldt0813## Adding whlcm0814## Adding whlcm0815## Adding whlcm0816## Adding whlwt0817## Adding whlwt0818## Adding whlwt0819## Writing the image to: images/ump_esxG_intensities.png and calling dev.off().
intensities_esxH <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename",
title="esxH Intensities",
scale="log", column="intensity", protein="Rv0288")## Adding filtwt0301## Adding filtwt0302## Adding filtwt0303## Adding whlwt0304## Adding whlwt0305## Adding whlwt0306## Adding whlwt0321## Adding whlwt0322## Adding filtwt0323## Adding filtwt0324## Adding filtwt0325## Adding whlwt0326## Adding filtdt0501## Adding filtdt0502## Adding filtdt0503## Adding filtcm0504## Adding filtcm0505## Adding filtcm0506## Adding whldt0507## Adding whldt0508## Adding whldt0509## Adding whlcm0510## Adding whlcm0511## Adding whlcm0512## Adding filtdt0701## Adding filtdt0702## Adding filtdt0703## Adding filtcm0704## Adding filtcm0705## Adding filtcm0706## Adding filtwt0707## Adding filtwt0708## Adding filt0709## Adding whldt0711## Adding whldt0712## Adding whldt0713## Adding wholcm0714## Adding whlcm0715## Adding whlcm0716## Adding whlwt0717## Adding whlwt0718## Adding whlwt0719## Adding filtdt0801## Adding filtdt0802## Adding filtdt0803## Adding filtcm0804## Adding filtcm0805## Adding filtcm0806## Adding filtwt0807## Adding filtwt0808## Adding filtwt0809## Adding whldt0811## Adding whldt0812## Adding whldt0813## Adding whlcm0814## Adding whlcm0815## Adding whlcm0816## Adding whlwt0817## Adding whlwt0818## Adding whlwt0819## Writing the image to: images/ump_esxH_intensities.png and calling dev.off().
intensities_lpqH <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename", scale="log",
title="lpqH_intensities", column="intensity", protein="Rv3763")## Adding filtwt0301## Adding filtwt0302## Adding filtwt0303## Adding whlwt0304## Adding whlwt0305## Adding whlwt0306## Adding whlwt0321## Adding whlwt0322## Adding filtwt0323## Adding filtwt0324## Adding filtwt0325## Adding whlwt0326## Adding filtdt0501## Adding filtdt0502## Adding filtdt0503## Adding filtcm0504## Adding filtcm0505## Adding filtcm0506## Adding whldt0507## Adding whldt0508## Adding whldt0509## Adding whlcm0510## Adding whlcm0511## Adding whlcm0512## Adding filtdt0701## Adding filtdt0702## Adding filtdt0703## Adding filtcm0704## Adding filtcm0705## Adding filtcm0706## Adding filtwt0707## Adding filtwt0708## Adding filt0709## Adding whldt0711## Adding whldt0712## Adding whldt0713## Adding wholcm0714## Adding whlcm0715## Adding whlcm0716## Adding whlwt0717## Adding whlwt0718## Adding whlwt0719## Adding filtdt0801## Adding filtdt0802## Adding filtdt0803## Adding filtcm0804## Adding filtcm0805## Adding filtcm0806## Adding filtwt0807## Adding filtwt0808## Adding filtwt0809## Adding whldt0811## Adding whldt0812## Adding whldt0813## Adding whlcm0814## Adding whlcm0815## Adding whlcm0816## Adding whlwt0817## Adding whlwt0818## Adding whlwt0819## Writing the image to: images/ump_lpqh_intensities.png and calling dev.off().
intensities_groel1 <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename",
title="groEL1 intensities", scale="log",
column="intensity", protein="Rv3417")## Adding filtwt0301## Adding filtwt0302## Adding filtwt0303## Adding whlwt0304## Adding whlwt0305## Adding whlwt0306## Adding whlwt0321## Adding whlwt0322## Adding filtwt0323## Adding filtwt0324## Adding filtwt0325## Adding whlwt0326## Adding filtdt0501## Adding filtdt0502## Adding filtdt0503## Adding filtcm0504## Adding filtcm0505## Adding filtcm0506## Adding whldt0507## Adding whldt0508## Adding whldt0509## Adding whlcm0510## Adding whlcm0511## Adding whlcm0512## Adding filtdt0701## Adding filtdt0702## Adding filtdt0703## Adding filtcm0704## Adding filtcm0705## Adding filtcm0706## Adding filtwt0707## Adding filtwt0708## Adding filt0709## Adding whldt0711## Adding whldt0712## Adding whldt0713## Adding wholcm0714## Adding whlcm0715## Adding whlcm0716## Adding whlwt0717## Adding whlwt0718## Adding whlwt0719## Adding filtdt0801## Adding filtdt0802## Adding filtdt0803## Adding filtcm0804## Adding filtcm0805## Adding filtcm0806## Adding filtwt0807## Adding filtwt0808## Adding filtwt0809## Adding whldt0811## Adding whldt0812## Adding whldt0813## Adding whlcm0814## Adding whlcm0815## Adding whlcm0816## Adding whlwt0817## Adding whlwt0818## Adding whlwt0819## Writing the image to: images/ump_groel1_intensities.png and calling dev.off().
intensities_groel2 <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename",
title="groEL2 intensities", scale="log",
column="intensity", protein="Rv0440")## Adding filtwt0301## Adding filtwt0302## Adding filtwt0303## Adding whlwt0304## Adding whlwt0305## Adding whlwt0306## Adding whlwt0321## Adding whlwt0322## Adding filtwt0323## Adding filtwt0324## Adding filtwt0325## Adding whlwt0326## Adding filtdt0501## Adding filtdt0502## Adding filtdt0503## Adding filtcm0504## Adding filtcm0505## Adding filtcm0506## Adding whldt0507## Adding whldt0508## Adding whldt0509## Adding whlcm0510## Adding whlcm0511## Adding whlcm0512## Adding filtdt0701## Adding filtdt0702## Adding filtdt0703## Adding filtcm0704## Adding filtcm0705## Adding filtcm0706## Adding filtwt0707## Adding filtwt0708## Adding filt0709## Adding whldt0711## Adding whldt0712## Adding whldt0713## Adding wholcm0714## Adding whlcm0715## Adding whlcm0716## Adding whlwt0717## Adding whlwt0718## Adding whlwt0719## Adding filtdt0801## Adding filtdt0802## Adding filtdt0803## Adding filtcm0804## Adding filtcm0805## Adding filtcm0806## Adding filtwt0807## Adding filtwt0808## Adding filtwt0809## Adding whldt0811## Adding whldt0812## Adding whldt0813## Adding whlcm0814## Adding whlcm0815## Adding whlcm0816## Adding whlwt0817## Adding whlwt0818## Adding whlwt0819## Writing the image to: images/ump_groel2_intensities.png and calling dev.off().
intensities_fap <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename",
title="fap intensities", scale="log",
column="intensity", protein="Rv1860")## Adding filtwt0301## Adding filtwt0302## Adding filtwt0303## Adding whlwt0304## Adding whlwt0305## Adding whlwt0306## Adding whlwt0321## Adding whlwt0322## Adding filtwt0323## Adding filtwt0324## Adding filtwt0325## Adding whlwt0326## Adding filtdt0501## Adding filtdt0502## Adding filtdt0503## Adding filtcm0504## Adding filtcm0505## Adding filtcm0506## Adding whldt0507## Adding whldt0508## Adding whldt0509## Adding whlcm0510## Adding whlcm0511## Adding whlcm0512## Adding filtdt0701## Adding filtdt0702## Adding filtdt0703## Adding filtcm0704## Adding filtcm0705## Adding filtcm0706## Adding filtwt0707## Adding filtwt0708## Adding filt0709## Adding whldt0711## Adding whldt0712## Adding whldt0713## Adding wholcm0714## Adding whlcm0715## Adding whlcm0716## Adding whlwt0717## Adding whlwt0718## Adding whlwt0719## Adding filtdt0801## Adding filtdt0802## Adding filtdt0803## Adding filtcm0804## Adding filtcm0805## Adding filtcm0806## Adding filtwt0807## Adding filtwt0808## Adding filtwt0809## Adding whldt0811## Adding whldt0812## Adding whldt0813## Adding whlcm0814## Adding whlcm0815## Adding whlcm0816## Adding whlwt0817## Adding whlwt0818## Adding whlwt0819## Writing the image to: images/ump_fap_intensities.png and calling dev.off().
intensities_katg <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename",
title="katG intensities", scale="log",
column="intensity", protein="Rv1908")## Adding filtwt0301## Adding filtwt0302## Adding filtwt0303## Adding whlwt0304## Adding whlwt0305## Adding whlwt0306## Adding whlwt0321## Adding whlwt0322## Adding filtwt0323## Adding filtwt0324## Adding filtwt0325## Adding whlwt0326## Adding filtdt0501## Adding filtdt0502## Adding filtdt0503## Adding filtcm0504## Adding filtcm0505## Adding filtcm0506## Adding whldt0507## Adding whldt0508## Adding whldt0509## Adding whlcm0510## Adding whlcm0511## Adding whlcm0512## Adding filtdt0701## Adding filtdt0702## Adding filtdt0703## Adding filtcm0704## Adding filtcm0705## Adding filtcm0706## Adding filtwt0707## Adding filtwt0708## Adding filt0709## Adding whldt0711## Adding whldt0712## Adding whldt0713## Adding wholcm0714## Adding whlcm0715## Adding whlcm0716## Adding whlwt0717## Adding whlwt0718## Adding whlwt0719## Adding filtdt0801## Adding filtdt0802## Adding filtdt0803## Adding filtcm0804## Adding filtcm0805## Adding filtcm0806## Adding filtwt0807## Adding filtwt0808## Adding filtwt0809## Adding whldt0811## Adding whldt0812## Adding whldt0813## Adding whlcm0814## Adding whlcm0815## Adding whlcm0816## Adding whlwt0817## Adding whlwt0818## Adding whlwt0819## Writing the image to: images/ump_katg_intensities.png and calling dev.off().
5 Get intensities
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, ]
ump_expt <- create_expt(sample_annot,
count_dataframe=intensities,
gene_info=mtb_annotations)## Reading the sample metadata.## The sample definitions comprises: 60 rows(samples) and 29 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.6 Pass the data to DEP and see what happens.
## Loading DEP##
## Attaching package: 'dplyr'## The following object is masked from 'package:testthat':
##
## matches## The following object is masked from 'package:hpgltools':
##
## combine## The following object is masked from 'package:Biobase':
##
## combine## The following objects are masked from 'package:BiocGenerics':
##
## combine, intersect, setdiff, union## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionmy_make_se <- function (proteins_unique, columns, expdesign,
id_column="ID", name_column="name") {
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_column]]
raw <- proteins_unique[, columns]
raw[raw == 0] <- NA
raw <- log2(raw)
## Perhaps consider the following?
## raw <- log1p(raw) / log(2)
expdesign <- dplyr::mutate(expdesign, condition=make.names(condition))
## I changed the following because it didn't make sense to me.
if (is.null(expdesign[[id_column]])) {
expdesign <- expdesign %>%
tidyr::unite(condition, replicate, remove=FALSE)
}
rownames(expdesign) <- expdesign[[id_column]]
matched <- match(make.names(DEP:::delete_prefix(expdesign[["label"]])),
make.names(DEP:::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_column]]
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 DEP## 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 objects are masked from 'package:dplyr':
##
## 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## The following objects are masked from 'package:dplyr':
##
## 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 object is masked from 'package:dplyr':
##
## count## 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, rowsumdesign <- pData(ump_expt)
design[["label"]] <- design[["sampleid"]]
design[["replicate"]] <- design[["sampleid"]]
my_se <- SummarizedExperiment(
assays=exprs(ump_expt),
colData=design,
rowData=fData(ump_expt))
mtb_unique <- as.data.frame(exprs(ump_expt))
mtb_unique[["name"]] <- rownames(mtb_unique)
mtb_unique[["ID"]] <- rownames(mtb_unique)
design[["ID"]] <- rownames(design)
intensity_columns <- 1:60
## mtb_se <- wtf(mtb_unique, intensity_columns, design)
mtb_se <- my_make_se(mtb_unique, intensity_columns, design)## Warning: `is.tibble()` is deprecated, use `is_tibble()`.
## This warning is displayed once per session.
## [1] 2284 60## [1] 817 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] 1.708mtb_imp_man <- DEP::impute(mtb_norm, fun="man", shift=1.8, scale=0.3)
DEP::plot_imputation(mtb_norm, mtb_imp)
norm_exprs <- assay(mtb_norm)
imp_exprs <- assay(mtb_imp)
man_exprs <- assay(mtb_imp_man)
summary(norm_exprs)## s2018_0315Briken01 s2018_0315Briken02 s2018_0315Briken03
## Min. :15.4 Min. :14.5 Min. :16.1
## 1st Qu.:21.0 1st Qu.:20.9 1st Qu.:20.8
## Median :22.9 Median :22.6 Median :22.9
## Mean :23.2 Mean :23.1 Mean :23.2
## 3rd Qu.:25.1 3rd Qu.:25.0 3rd Qu.:25.2
## Max. :36.2 Max. :35.8 Max. :36.0
## NA's :1474 NA's :1429 NA's :1364
## s2018_0315Briken04 s2018_0315Briken05 s2018_0315Briken06
## Min. :14.8 Min. :14.2 Min. :13.4
## 1st Qu.:20.6 1st Qu.:20.6 1st Qu.:20.5
## Median :22.4 Median :22.4 Median :22.3
## Mean :22.4 Mean :22.4 Mean :22.3
## 3rd Qu.:24.2 3rd Qu.:24.2 3rd Qu.:24.1
## Max. :32.3 Max. :31.4 Max. :31.6
## NA's :615 NA's :495 NA's :556
## s2018_0315Briken21 s2018_0315Briken22 s2018_0315Briken23
## Min. :14.3 Min. :13.0 Min. :15.3
## 1st Qu.:20.5 1st Qu.:20.5 1st Qu.:20.7
## Median :22.4 Median :22.3 Median :22.6
## Mean :22.4 Mean :22.3 Mean :22.8
## 3rd Qu.:24.3 3rd Qu.:24.2 3rd Qu.:24.8
## Max. :32.2 Max. :31.8 Max. :35.6
## NA's :467 NA's :417 NA's :1393
## s2018_0315Briken24 s2018_0315Briken25 s2018_0315Briken26
## Min. :17.0 Min. :14.7 Min. :13.8
## 1st Qu.:20.9 1st Qu.:20.9 1st Qu.:20.5
## Median :22.6 Median :22.7 Median :22.3
## Mean :23.0 Mean :23.0 Mean :22.3
## 3rd Qu.:25.0 3rd Qu.:24.9 3rd Qu.:24.1
## Max. :35.2 Max. :35.4 Max. :31.7
## NA's :1343 NA's :1456 NA's :459
## s2018_0502BrikenDIA01 s2018_0502BrikenDIA02 s2018_0502BrikenDIA03
## Min. :16.9 Min. :15.9 Min. :17.2
## 1st Qu.:21.0 1st Qu.:20.5 1st Qu.:20.8
## Median :22.6 Median :22.4 Median :22.5
## Mean :23.0 Mean :22.8 Mean :22.8
## 3rd Qu.:24.9 3rd Qu.:24.7 3rd Qu.:24.7
## Max. :36.1 Max. :35.7 Max. :35.9
## NA's :1587 NA's :1485 NA's :1607
## s2018_0502BrikenDIA04 s2018_0502BrikenDIA05 s2018_0502BrikenDIA06
## Min. :16.7 Min. :16.4 Min. :16.5
## 1st Qu.:20.6 1st Qu.:20.4 1st Qu.:20.5
## Median :22.4 Median :22.3 Median :22.3
## Mean :22.8 Mean :22.6 Mean :22.6
## 3rd Qu.:24.6 3rd Qu.:24.5 3rd Qu.:24.4
## Max. :36.2 Max. :35.0 Max. :35.9
## NA's :1449 NA's :1369 NA's :1465
## s2018_0502BrikenDIA07 s2018_0502BrikenDIA08 s2018_0502BrikenDIA09
## Min. :14.0 Min. :15.1 Min. :17.7
## 1st Qu.:20.9 1st Qu.:20.5 1st Qu.:20.8
## Median :22.7 Median :22.3 Median :22.4
## Mean :22.7 Mean :22.4 Mean :22.5
## 3rd Qu.:24.6 3rd Qu.:24.1 3rd Qu.:24.0
## Max. :31.8 Max. :32.0 Max. :32.1
## NA's :578 NA's :599 NA's :1061
## s2018_0502BrikenDIA10 s2018_0502BrikenDIA11 s2018_0502BrikenDIA12
## Min. :21.7 Min. :13.5 Min. :13.9
## 1st Qu.:24.0 1st Qu.:20.5 1st Qu.:20.6
## Median :25.1 Median :22.3 Median :22.4
## Mean :25.4 Mean :22.3 Mean :22.4
## 3rd Qu.:26.6 3rd Qu.:24.1 3rd Qu.:24.2
## Max. :32.4 Max. :31.8 Max. :31.3
## NA's :2098 NA's :570 NA's :732
## s2018_0726Briken01 s2018_0726Briken02 s2018_0726Briken03
## Min. :21.0 Min. :16.7 Min. :21.1
## 1st Qu.:24.2 1st Qu.:22.2 1st Qu.:23.5
## Median :26.2 Median :24.5 Median :25.3
## Mean :26.2 Mean :24.4 Mean :25.7
## 3rd Qu.:27.7 3rd Qu.:26.2 3rd Qu.:27.5
## Max. :35.1 Max. :35.5 Max. :34.4
## NA's :2140 NA's :2051 NA's :2166
## s2018_0726Briken04 s2018_0726Briken05 s2018_0726Briken06
## Min. :21.2 Min. :19.4 Min. :18.5
## 1st Qu.:24.7 1st Qu.:22.6 1st Qu.:22.1
## Median :26.4 Median :24.3 Median :24.1
## Mean :26.7 Mean :24.5 Mean :24.1
## 3rd Qu.:28.2 3rd Qu.:26.1 3rd Qu.:26.1
## Max. :36.1 Max. :34.6 Max. :34.1
## NA's :2166 NA's :2096 NA's :2025
## s2018_0726Briken07 s2018_0726Briken08 s2018_0726Briken09
## Min. :17.5 Min. :20.0 Min. :17.6
## 1st Qu.:22.6 1st Qu.:23.1 1st Qu.:21.9
## Median :24.6 Median :25.0 Median :23.8
## Mean :24.7 Mean :25.3 Mean :24.0
## 3rd Qu.:26.6 3rd Qu.:27.0 3rd Qu.:25.6
## Max. :37.3 Max. :37.0 Max. :36.4
## NA's :2021 NA's :2036 NA's :1965
## s2018_0726Briken11 s2018_0726Briken12 s2018_0726Briken13
## Min. :16.3 Min. :15.7 Min. :17.7
## 1st Qu.:21.2 1st Qu.:21.0 1st Qu.:21.3
## Median :22.6 Median :22.8 Median :22.8
## Mean :22.7 Mean :22.8 Mean :22.9
## 3rd Qu.:24.4 3rd Qu.:24.5 3rd Qu.:24.3
## Max. :31.2 Max. :31.4 Max. :31.5
## NA's :1701 NA's :1590 NA's :1758
## s2018_0726Briken14 s2018_0726Briken15 s2018_0726Briken16
## Min. :16.8 Min. :16.9 Min. :14.8
## 1st Qu.:21.5 1st Qu.:21.2 1st Qu.:20.7
## Median :23.0 Median :22.9 Median :22.6
## Mean :23.1 Mean :22.9 Mean :22.5
## 3rd Qu.:24.8 3rd Qu.:24.4 3rd Qu.:24.4
## Max. :32.0 Max. :32.0 Max. :31.1
## NA's :1768 NA's :1702 NA's :1626
## s2018_0726Briken17 s2018_0726Briken18 s2018_0726Briken19
## Min. :13.6 Min. :14.6 Min. :15.6
## 1st Qu.:21.0 1st Qu.:20.5 1st Qu.:21.6
## Median :22.8 Median :22.1 Median :23.5
## Mean :22.7 Mean :22.2 Mean :23.4
## 3rd Qu.:24.6 3rd Qu.:23.9 3rd Qu.:25.2
## Max. :31.7 Max. :30.4 Max. :30.6
## NA's :1686 NA's :1484 NA's :1858
## s2018_0817BrikenTrypsinDIA01 s2018_0817BrikenTrypsinDIA02
## Min. :17.7 Min. :17.7
## 1st Qu.:20.8 1st Qu.:20.9
## Median :22.3 Median :22.4
## Mean :22.5 Mean :22.5
## 3rd Qu.:23.9 3rd Qu.:23.9
## Max. :33.8 Max. :33.5
## NA's :1723 NA's :1704
## s2018_0817BrikenTrypsinDIA03 s2018_0817BrikenTrypsinDIA04
## Min. :17.2 Min. :16.0
## 1st Qu.:21.0 1st Qu.:20.9
## Median :22.6 Median :22.7
## Mean :22.7 Mean :22.7
## 3rd Qu.:24.2 3rd Qu.:24.3
## Max. :33.7 Max. :33.3
## NA's :1710 NA's :1573
## s2018_0817BrikenTrypsinDIA05 s2018_0817BrikenTrypsinDIA06
## Min. :17.5 Min. :17.3
## 1st Qu.:20.4 1st Qu.:20.4
## Median :22.0 Median :22.1
## Mean :22.1 Mean :22.2
## 3rd Qu.:23.6 3rd Qu.:23.7
## Max. :32.7 Max. :32.5
## NA's :1638 NA's :1622
## s2018_0817BrikenTrypsinDIA07 s2018_0817BrikenTrypsinDIA08
## Min. :18.8 Min. :19.2
## 1st Qu.:22.0 1st Qu.:22.3
## Median :23.3 Median :23.7
## Mean :23.6 Mean :23.8
## 3rd Qu.:24.7 3rd Qu.:25.0
## Max. :34.1 Max. :34.6
## NA's :1825 NA's :1776
## s2018_0817BrikenTrypsinDIA09 s2018_0817BrikenTrypsinDIA11
## Min. :18.4 Min. :14.7
## 1st Qu.:21.2 1st Qu.:20.2
## Median :22.7 Median :21.7
## Mean :22.9 Mean :21.6
## 3rd Qu.:24.4 3rd Qu.:23.1
## Max. :33.8 Max. :30.1
## NA's :1746 NA's :786
## s2018_0817BrikenTrypsinDIA12 s2018_0817BrikenTrypsinDIA13
## Min. :14.9 Min. :14.3
## 1st Qu.:20.3 1st Qu.:20.1
## Median :21.7 Median :21.7
## Mean :21.7 Mean :21.6
## 3rd Qu.:23.0 3rd Qu.:23.1
## Max. :30.5 Max. :30.2
## NA's :789 NA's :692
## s2018_0817BrikenTrypsinDIA14 s2018_0817BrikenTrypsinDIA15
## Min. :14.7 Min. :15.0
## 1st Qu.:20.2 1st Qu.:20.2
## Median :21.8 Median :21.7
## Mean :21.7 Mean :21.6
## 3rd Qu.:23.2 3rd Qu.:23.0
## Max. :30.8 Max. :30.3
## NA's :752 NA's :632
## s2018_0817BrikenTrypsinDIA16 s2018_0817BrikenTrypsinDIA17
## Min. :14.7 Min. :13.4
## 1st Qu.:20.2 1st Qu.:20.0
## Median :21.6 Median :21.3
## Mean :21.5 Mean :21.3
## 3rd Qu.:23.0 3rd Qu.:22.7
## Max. :30.2 Max. :30.1
## NA's :642 NA's :1145
## s2018_0817BrikenTrypsinDIA18 s2018_0817BrikenTrypsinDIA19
## Min. :14.9 Min. :15.4
## 1st Qu.:20.1 1st Qu.:20.3
## Median :21.7 Median :21.8
## Mean :21.6 Mean :21.7
## 3rd Qu.:23.2 3rd Qu.:23.2
## Max. :30.6 Max. :30.2
## NA's :799 NA's :790## s2018_0315Briken01 s2018_0315Briken02 s2018_0315Briken03
## Min. :12.0 Min. :11.3 Min. :11.2
## 1st Qu.:16.7 1st Qu.:16.9 1st Qu.:16.8
## Median :18.3 Median :18.6 Median :18.5
## Mean :19.3 Mean :19.5 Mean :19.5
## 3rd Qu.:21.5 3rd Qu.:21.6 3rd Qu.:22.1
## Max. :36.2 Max. :35.8 Max. :36.0
## s2018_0315Briken04 s2018_0315Briken05 s2018_0315Briken06
## Min. :11.4 Min. :11.3 Min. :11.4
## 1st Qu.:18.0 1st Qu.:18.8 1st Qu.:18.2
## Median :21.1 Median :21.5 Median :21.2
## Mean :20.8 Mean :21.2 Mean :20.9
## 3rd Qu.:23.6 3rd Qu.:23.7 3rd Qu.:23.5
## Max. :32.4 Max. :31.4 Max. :31.6
## s2018_0315Briken21 s2018_0315Briken22 s2018_0315Briken23
## Min. :11.2 Min. :11.5 Min. :11.8
## 1st Qu.:18.6 1st Qu.:18.8 1st Qu.:16.5
## Median :21.5 Median :21.5 Median :18.2
## Mean :21.2 Mean :21.2 Mean :19.2
## 3rd Qu.:23.8 3rd Qu.:23.7 3rd Qu.:21.5
## Max. :32.2 Max. :31.8 Max. :35.6
## s2018_0315Briken24 s2018_0315Briken25 s2018_0315Briken26
## Min. :12.1 Min. :12.1 Min. :11.8
## 1st Qu.:16.9 1st Qu.:16.2 1st Qu.:18.7
## Median :18.7 Median :17.9 Median :21.4
## Mean :19.6 Mean :19.0 Mean :21.1
## 3rd Qu.:21.9 3rd Qu.:21.4 3rd Qu.:23.5
## Max. :35.2 Max. :35.4 Max. :31.7
## s2018_0502BrikenDIA01 s2018_0502BrikenDIA02 s2018_0502BrikenDIA03
## Min. :12.4 Min. :10.7 Min. :12.8
## 1st Qu.:17.4 1st Qu.:16.9 1st Qu.:17.1
## Median :18.9 Median :18.5 Median :18.6
## Mean :19.5 Mean :19.2 Mean :19.2
## 3rd Qu.:21.0 3rd Qu.:21.1 3rd Qu.:20.7
## Max. :36.1 Max. :35.7 Max. :35.9
## s2018_0502BrikenDIA04 s2018_0502BrikenDIA05 s2018_0502BrikenDIA06
## Min. :11.6 Min. :11.4 Min. :11.7
## 1st Qu.:17.3 1st Qu.:17.2 1st Qu.:17.0
## Median :18.8 Median :18.8 Median :18.7
## Mean :19.6 Mean :19.6 Mean :19.3
## 3rd Qu.:21.3 3rd Qu.:21.4 3rd Qu.:21.1
## Max. :36.2 Max. :35.0 Max. :35.9
## s2018_0502BrikenDIA07 s2018_0502BrikenDIA08 s2018_0502BrikenDIA09
## Min. :10.7 Min. :11.8 Min. :13.5
## 1st Qu.:18.3 1st Qu.:18.3 1st Qu.:18.5
## Median :21.6 Median :21.1 Median :20.4
## Mean :21.2 Mean :20.9 Mean :20.6
## 3rd Qu.:23.9 3rd Qu.:23.5 3rd Qu.:22.5
## Max. :31.8 Max. :32.0 Max. :32.1
## s2018_0502BrikenDIA10 s2018_0502BrikenDIA11 s2018_0502BrikenDIA12
## Min. :16.4 Min. : 9.62 Min. :10.9
## 1st Qu.:21.1 1st Qu.:18.33 1st Qu.:17.2
## Median :22.5 Median :21.11 Median :20.8
## Mean :22.5 Mean :20.93 Mean :20.4
## 3rd Qu.:23.7 3rd Qu.:23.47 3rd Qu.:23.3
## Max. :32.4 Max. :31.81 Max. :31.3
## s2018_0726Briken01 s2018_0726Briken02 s2018_0726Briken03
## Min. :15.8 Min. :12.3 Min. :15.8
## 1st Qu.:20.2 1st Qu.:17.3 1st Qu.:20.1
## Median :21.4 Median :18.6 Median :21.2
## Mean :21.5 Mean :18.9 Mean :21.4
## 3rd Qu.:22.7 3rd Qu.:19.9 3rd Qu.:22.5
## Max. :35.1 Max. :35.5 Max. :34.4
## s2018_0726Briken04 s2018_0726Briken05 s2018_0726Briken06
## Min. :15.2 Min. :13.6 Min. :12.8
## 1st Qu.:20.7 1st Qu.:18.5 1st Qu.:17.6
## Median :21.8 Median :19.8 Median :18.9
## Mean :22.0 Mean :20.0 Mean :19.3
## 3rd Qu.:23.1 3rd Qu.:21.1 3rd Qu.:20.3
## Max. :36.1 Max. :34.6 Max. :34.1
## s2018_0726Briken07 s2018_0726Briken08 s2018_0726Briken09
## Min. :11.9 Min. :13.8 Min. :12.2
## 1st Qu.:17.3 1st Qu.:19.3 1st Qu.:17.2
## Median :18.6 Median :20.5 Median :18.4
## Mean :19.0 Mean :20.8 Mean :18.9
## 3rd Qu.:19.9 3rd Qu.:21.9 3rd Qu.:19.9
## Max. :37.3 Max. :37.0 Max. :36.4
## s2018_0726Briken11 s2018_0726Briken12 s2018_0726Briken13
## Min. :11.2 Min. :12.0 Min. :11.9
## 1st Qu.:17.1 1st Qu.:16.4 1st Qu.:17.6
## Median :18.6 Median :18.0 Median :19.2
## Mean :19.1 Mean :18.8 Mean :19.5
## 3rd Qu.:20.5 3rd Qu.:20.8 3rd Qu.:20.9
## Max. :31.2 Max. :31.4 Max. :31.5
## s2018_0726Briken14 s2018_0726Briken15 s2018_0726Briken16
## Min. :12.7 Min. :12.9 Min. :10.7
## 1st Qu.:17.3 1st Qu.:17.7 1st Qu.:15.9
## Median :18.7 Median :19.1 Median :17.4
## Mean :19.2 Mean :19.6 Mean :18.3
## 3rd Qu.:20.5 3rd Qu.:21.0 3rd Qu.:20.1
## Max. :32.0 Max. :32.0 Max. :31.1
## s2018_0726Briken17 s2018_0726Briken18 s2018_0726Briken19
## Min. :10.8 Min. :11.2 Min. :12.0
## 1st Qu.:15.6 1st Qu.:15.9 1st Qu.:16.4
## Median :17.2 Median :17.6 Median :17.9
## Mean :18.1 Mean :18.4 Mean :18.4
## 3rd Qu.:19.7 3rd Qu.:20.8 3rd Qu.:19.6
## Max. :31.7 Max. :30.4 Max. :30.6
## s2018_0817BrikenTrypsinDIA01 s2018_0817BrikenTrypsinDIA02
## Min. :13.0 Min. :13.4
## 1st Qu.:17.5 1st Qu.:17.6
## Median :18.9 Median :19.0
## Mean :19.3 Mean :19.4
## 3rd Qu.:20.7 3rd Qu.:20.7
## Max. :33.8 Max. :33.5
## s2018_0817BrikenTrypsinDIA03 s2018_0817BrikenTrypsinDIA04
## Min. :12.6 Min. :11.8
## 1st Qu.:17.4 1st Qu.:16.9
## Median :18.8 Median :18.4
## Mean :19.3 Mean :19.1
## 3rd Qu.:20.7 3rd Qu.:20.9
## Max. :33.7 Max. :33.3
## s2018_0817BrikenTrypsinDIA05 s2018_0817BrikenTrypsinDIA06
## Min. :11.0 Min. :12.5
## 1st Qu.:17.4 1st Qu.:17.4
## Median :18.9 Median :18.9
## Mean :19.2 Mean :19.3
## 3rd Qu.:20.6 3rd Qu.:20.7
## Max. :32.7 Max. :32.5
## s2018_0817BrikenTrypsinDIA07 s2018_0817BrikenTrypsinDIA08
## Min. :14.2 Min. :14.2
## 1st Qu.:19.1 1st Qu.:19.3
## Median :20.4 Median :20.7
## Mean :20.6 Mean :20.9
## 3rd Qu.:21.9 3rd Qu.:22.2
## Max. :34.1 Max. :34.6
## s2018_0817BrikenTrypsinDIA09 s2018_0817BrikenTrypsinDIA11
## Min. :12.9 Min. :11.6
## 1st Qu.:18.3 1st Qu.:17.4
## Median :19.6 Median :20.2
## Mean :19.9 Mean :19.9
## 3rd Qu.:21.2 3rd Qu.:22.3
## Max. :33.8 Max. :30.1
## s2018_0817BrikenTrypsinDIA12 s2018_0817BrikenTrypsinDIA13
## Min. :11.5 Min. :10.4
## 1st Qu.:17.7 1st Qu.:17.7
## Median :20.3 Median :20.4
## Mean :20.0 Mean :20.1
## 3rd Qu.:22.3 3rd Qu.:22.5
## Max. :30.5 Max. :30.2
## s2018_0817BrikenTrypsinDIA14 s2018_0817BrikenTrypsinDIA15
## Min. :11.7 Min. :11.9
## 1st Qu.:17.7 1st Qu.:18.0
## Median :20.4 Median :20.6
## Mean :20.1 Mean :20.3
## 3rd Qu.:22.4 3rd Qu.:22.5
## Max. :30.8 Max. :30.3
## s2018_0817BrikenTrypsinDIA16 s2018_0817BrikenTrypsinDIA17
## Min. :11.6 Min. :10.9
## 1st Qu.:17.8 1st Qu.:16.6
## Median :20.5 Median :18.8
## Mean :20.1 Mean :19.0
## 3rd Qu.:22.4 3rd Qu.:21.3
## Max. :30.2 Max. :30.1
## s2018_0817BrikenTrypsinDIA18 s2018_0817BrikenTrypsinDIA19
## Min. :10.4 Min. :11.1
## 1st Qu.:17.3 1st Qu.:17.3
## Median :20.0 Median :20.3
## Mean :19.8 Mean :19.9
## 3rd Qu.:22.3 3rd Qu.:22.5
## Max. :30.6 Max. :30.2## s2018_0315Briken01 s2018_0315Briken02 s2018_0315Briken03
## Min. :14.7 Min. :13.9 Min. :14.3
## 1st Qu.:17.0 1st Qu.:16.8 1st Qu.:17.0
## Median :17.9 Median :17.8 Median :18.0
## Mean :19.4 Mean :19.3 Mean :19.6
## 3rd Qu.:21.4 3rd Qu.:21.5 3rd Qu.:22.0
## Max. :36.2 Max. :35.8 Max. :36.0
## s2018_0315Briken04 s2018_0315Briken05 s2018_0315Briken06
## Min. :14.8 Min. :14.2 Min. :13.4
## 1st Qu.:18.2 1st Qu.:18.6 1st Qu.:18.3
## Median :21.1 Median :21.5 Median :21.2
## Mean :21.1 Mean :21.4 Mean :21.2
## 3rd Qu.:23.6 3rd Qu.:23.7 3rd Qu.:23.5
## Max. :32.4 Max. :31.4 Max. :31.6
## s2018_0315Briken21 s2018_0315Briken22 s2018_0315Briken23
## Min. :14.3 Min. :13.0 Min. :14.0
## 1st Qu.:18.6 1st Qu.:18.9 1st Qu.:16.7
## Median :21.5 Median :21.5 Median :17.8
## Mean :21.4 Mean :21.4 Mean :19.3
## 3rd Qu.:23.8 3rd Qu.:23.7 3rd Qu.:21.5
## Max. :32.2 Max. :31.8 Max. :35.6
## s2018_0315Briken24 s2018_0315Briken25 s2018_0315Briken26
## Min. :14.2 Min. :12.9 Min. :13.8
## 1st Qu.:17.0 1st Qu.:17.0 1st Qu.:18.7
## Median :18.0 Median :17.9 Median :21.4
## Mean :19.6 Mean :19.3 Mean :21.4
## 3rd Qu.:21.9 3rd Qu.:21.3 3rd Qu.:23.5
## Max. :35.2 Max. :35.4 Max. :31.7
## s2018_0502BrikenDIA01 s2018_0502BrikenDIA02 s2018_0502BrikenDIA03
## Min. :13.6 Min. :14.0 Min. :14.3
## 1st Qu.:16.9 1st Qu.:16.7 1st Qu.:17.0
## Median :17.8 Median :17.7 Median :17.7
## Mean :19.1 Mean :19.0 Mean :18.9
## 3rd Qu.:20.3 3rd Qu.:20.8 3rd Qu.:20.0
## Max. :36.1 Max. :35.7 Max. :35.9
## s2018_0502BrikenDIA04 s2018_0502BrikenDIA05 s2018_0502BrikenDIA06
## Min. :14.5 Min. :13.2 Min. :14.5
## 1st Qu.:16.9 1st Qu.:16.8 1st Qu.:17.0
## Median :17.8 Median :17.8 Median :17.8
## Mean :19.2 Mean :19.2 Mean :19.1
## 3rd Qu.:21.1 3rd Qu.:21.4 3rd Qu.:20.9
## Max. :36.2 Max. :35.0 Max. :35.9
## s2018_0502BrikenDIA07 s2018_0502BrikenDIA08 s2018_0502BrikenDIA09
## Min. :14.0 Min. :15.1 Min. :16.1
## 1st Qu.:18.6 1st Qu.:18.2 1st Qu.:18.3
## Median :21.6 Median :21.1 Median :19.6
## Mean :21.5 Mean :21.1 Mean :20.6
## 3rd Qu.:23.9 3rd Qu.:23.5 3rd Qu.:22.5
## Max. :31.8 Max. :32.0 Max. :32.1
## s2018_0502BrikenDIA10 s2018_0502BrikenDIA11 s2018_0502BrikenDIA12
## Min. :19.8 Min. :13.5 Min. :13.9
## 1st Qu.:21.3 1st Qu.:18.4 1st Qu.:18.1
## Median :21.7 Median :21.1 Median :20.7
## Mean :21.9 Mean :21.2 Mean :20.9
## 3rd Qu.:22.1 3rd Qu.:23.5 3rd Qu.:23.3
## Max. :32.4 Max. :31.8 Max. :31.3
## s2018_0726Briken01 s2018_0726Briken02 s2018_0726Briken03
## Min. :18.3 Min. :15.4 Min. :16.9
## 1st Qu.:20.6 1st Qu.:18.5 1st Qu.:19.6
## Median :21.2 Median :19.1 Median :20.2
## Mean :21.5 Mean :19.6 Mean :20.4
## 3rd Qu.:21.9 3rd Qu.:19.9 3rd Qu.:20.9
## Max. :35.1 Max. :35.5 Max. :34.4
## s2018_0726Briken04 s2018_0726Briken05 s2018_0726Briken06
## Min. :18.8 Min. :16.8 Min. :16.0
## 1st Qu.:20.9 1st Qu.:19.1 1st Qu.:18.4
## Median :21.4 Median :19.7 Median :19.1
## Mean :21.7 Mean :20.0 Mean :19.5
## 3rd Qu.:22.1 3rd Qu.:20.3 3rd Qu.:19.8
## Max. :36.1 Max. :34.6 Max. :34.1
## s2018_0726Briken07 s2018_0726Briken08 s2018_0726Briken09
## Min. :16.1 Min. :17.0 Min. :15.3
## 1st Qu.:18.5 1st Qu.:19.2 1st Qu.:17.9
## Median :19.1 Median :19.9 Median :18.6
## Mean :19.7 Mean :20.3 Mean :19.2
## 3rd Qu.:19.9 3rd Qu.:20.6 3rd Qu.:19.4
## Max. :37.3 Max. :37.0 Max. :36.4
## s2018_0726Briken11 s2018_0726Briken12 s2018_0726Briken13
## Min. :15.5 Min. :15.7 Min. :16.4
## 1st Qu.:18.1 1st Qu.:18.0 1st Qu.:18.4
## Median :18.7 Median :18.7 Median :18.9
## Mean :19.5 Mean :19.6 Mean :19.6
## 3rd Qu.:19.7 3rd Qu.:20.5 3rd Qu.:19.8
## Max. :31.2 Max. :31.4 Max. :31.5
## s2018_0726Briken14 s2018_0726Briken15 s2018_0726Briken16
## Min. :16.3 Min. :16.1 Min. :14.8
## 1st Qu.:18.2 1st Qu.:18.4 1st Qu.:17.5
## Median :18.8 Median :19.0 Median :18.2
## Mean :19.6 Mean :19.8 Mean :19.2
## 3rd Qu.:19.7 3rd Qu.:20.0 3rd Qu.:19.7
## Max. :32.0 Max. :32.0 Max. :31.1
## s2018_0726Briken17 s2018_0726Briken18 s2018_0726Briken19
## Min. :13.6 Min. :14.6 Min. :15.6
## 1st Qu.:17.7 1st Qu.:17.4 1st Qu.:18.6
## Median :18.4 Median :18.1 Median :19.2
## Mean :19.3 Mean :19.2 Mean :19.8
## 3rd Qu.:19.6 3rd Qu.:20.8 3rd Qu.:20.0
## Max. :31.7 Max. :30.4 Max. :30.6
## s2018_0817BrikenTrypsinDIA01 s2018_0817BrikenTrypsinDIA02
## Min. :15.5 Min. :16.2
## 1st Qu.:17.7 1st Qu.:18.1
## Median :18.3 Median :18.6
## Mean :19.1 Mean :19.4
## 3rd Qu.:19.3 3rd Qu.:19.7
## Max. :33.8 Max. :33.5
## s2018_0817BrikenTrypsinDIA03 s2018_0817BrikenTrypsinDIA04
## Min. :16.2 Min. :16.1
## 1st Qu.:18.2 1st Qu.:18.0
## Median :18.8 Median :18.7
## Mean :19.6 Mean :19.6
## 3rd Qu.:19.8 3rd Qu.:20.6
## Max. :33.7 Max. :33.3
## s2018_0817BrikenTrypsinDIA05 s2018_0817BrikenTrypsinDIA06
## Min. :15.8 Min. :15.9
## 1st Qu.:17.6 1st Qu.:17.7
## Median :18.2 Median :18.3
## Mean :19.1 Mean :19.2
## 3rd Qu.:19.6 3rd Qu.:19.8
## Max. :32.7 Max. :32.5
## s2018_0817BrikenTrypsinDIA07 s2018_0817BrikenTrypsinDIA08
## Min. :16.9 Min. :17.6
## 1st Qu.:19.1 1st Qu.:19.5
## Median :19.6 Median :20.0
## Mean :20.2 Mean :20.7
## 3rd Qu.:20.4 3rd Qu.:20.8
## Max. :34.1 Max. :34.6
## s2018_0817BrikenTrypsinDIA09 s2018_0817BrikenTrypsinDIA11
## Min. :16.2 Min. :14.7
## 1st Qu.:18.4 1st Qu.:18.1
## Median :18.9 Median :20.2
## Mean :19.7 Mean :20.3
## 3rd Qu.:19.9 3rd Qu.:22.3
## Max. :33.8 Max. :30.1
## s2018_0817BrikenTrypsinDIA12 s2018_0817BrikenTrypsinDIA13
## Min. :14.9 Min. :14.3
## 1st Qu.:18.1 1st Qu.:18.2
## Median :20.2 Median :20.4
## Mean :20.4 Mean :20.5
## 3rd Qu.:22.3 3rd Qu.:22.5
## Max. :30.5 Max. :30.2
## s2018_0817BrikenTrypsinDIA14 s2018_0817BrikenTrypsinDIA15
## Min. :14.7 Min. :15.0
## 1st Qu.:18.1 1st Qu.:18.3
## Median :20.3 Median :20.6
## Mean :20.4 Mean :20.6
## 3rd Qu.:22.4 3rd Qu.:22.5
## Max. :30.8 Max. :30.3
## s2018_0817BrikenTrypsinDIA16 s2018_0817BrikenTrypsinDIA17
## Min. :14.7 Min. :13.4
## 1st Qu.:18.2 1st Qu.:17.6
## Median :20.5 Median :18.5
## Mean :20.5 Mean :19.5
## 3rd Qu.:22.4 3rd Qu.:21.3
## Max. :30.2 Max. :30.1
## s2018_0817BrikenTrypsinDIA18 s2018_0817BrikenTrypsinDIA19
## Min. :14.9 Min. :15.4
## 1st Qu.:18.0 1st Qu.:18.1
## Median :20.0 Median :20.2
## Mean :20.3 Mean :20.4
## 3rd Qu.:22.3 3rd Qu.:22.5
## Max. :30.6 Max. :30.2## s2018_0315Briken01 s2018_0315Briken02
## 18787 19711
## s2018_0315Briken03 s2018_0315Briken04
## 21308 37463
## s2018_0315Briken05 s2018_0315Briken06
## 40130 38550
## s2018_0315Briken21 s2018_0315Briken22
## 40749 41648
## s2018_0315Briken23 s2018_0315Briken24
## 20354 21682
## s2018_0315Briken25 s2018_0315Briken26
## 19059 40686
## s2018_0502BrikenDIA01 s2018_0502BrikenDIA02
## 16065 18204
## s2018_0502BrikenDIA03 s2018_0502BrikenDIA04
## 15465 19049
## s2018_0502BrikenDIA05 s2018_0502BrikenDIA06
## 20713 18498
## s2018_0502BrikenDIA07 s2018_0502BrikenDIA08
## 38739 37680
## s2018_0502BrikenDIA09 s2018_0502BrikenDIA10
## 27574 4725
## s2018_0502BrikenDIA11 s2018_0502BrikenDIA12
## 38297 34740
## s2018_0726Briken01 s2018_0726Briken02
## 3775 5687
## s2018_0726Briken03 s2018_0726Briken04
## 3029 3150
## s2018_0726Briken05 s2018_0726Briken06
## 4600 6253
## s2018_0726Briken07 s2018_0726Briken08
## 6483 6277
## s2018_0726Briken09 s2018_0726Briken11
## 7660 13263
## s2018_0726Briken12 s2018_0726Briken13
## 15807 12023
## s2018_0726Briken14 s2018_0726Briken15
## 11908 13331
## s2018_0726Briken16 s2018_0726Briken17
## 14817 13598
## s2018_0726Briken18 s2018_0726Briken19
## 17738 9966
## s2018_0817BrikenTrypsinDIA01 s2018_0817BrikenTrypsinDIA02
## 12643 13059
## s2018_0817BrikenTrypsinDIA03 s2018_0817BrikenTrypsinDIA04
## 13026 16118
## s2018_0817BrikenTrypsinDIA05 s2018_0817BrikenTrypsinDIA06
## 14301 14682
## s2018_0817BrikenTrypsinDIA07 s2018_0817BrikenTrypsinDIA08
## 10812 12111
## s2018_0817BrikenTrypsinDIA09 s2018_0817BrikenTrypsinDIA11
## 12316 32394
## s2018_0817BrikenTrypsinDIA12 s2018_0817BrikenTrypsinDIA13
## 32368 34396
## s2018_0817BrikenTrypsinDIA14 s2018_0817BrikenTrypsinDIA15
## 33222 35731
## s2018_0817BrikenTrypsinDIA16 s2018_0817BrikenTrypsinDIA17
## 35382 24299
## s2018_0817BrikenTrypsinDIA18 s2018_0817BrikenTrypsinDIA19
## 32064 32437## s2018_0315Briken01 s2018_0315Briken02
## 44142 44508
## s2018_0315Briken03 s2018_0315Briken04
## 44607 47599
## s2018_0315Briken05 s2018_0315Briken06
## 48416 47737
## s2018_0315Briken21 s2018_0315Briken22
## 48367 48324
## s2018_0315Briken23 s2018_0315Briken24
## 43908 44840
## s2018_0315Briken25 s2018_0315Briken26
## 43375 48116
## s2018_0502BrikenDIA01 s2018_0502BrikenDIA02
## 44623 43961
## s2018_0502BrikenDIA03 s2018_0502BrikenDIA04
## 43897 44772
## s2018_0502BrikenDIA05 s2018_0502BrikenDIA06
## 44773 44136
## s2018_0502BrikenDIA07 s2018_0502BrikenDIA08
## 48339 47675
## s2018_0502BrikenDIA09 s2018_0502BrikenDIA10
## 47076 51302
## s2018_0502BrikenDIA11 s2018_0502BrikenDIA12
## 47798 46538
## s2018_0726Briken01 s2018_0726Briken02
## 49165 43236
## s2018_0726Briken03 s2018_0726Briken04
## 48912 50227
## s2018_0726Briken05 s2018_0726Briken06
## 45579 44054
## s2018_0726Briken07 s2018_0726Briken08
## 43388 47556
## s2018_0726Briken09 s2018_0726Briken11
## 43215 43641
## s2018_0726Briken12 s2018_0726Briken13
## 42994 44536
## s2018_0726Briken14 s2018_0726Briken15
## 43918 44745
## s2018_0726Briken16 s2018_0726Briken17
## 41722 41243
## s2018_0726Briken18 s2018_0726Briken19
## 42090 42122
## s2018_0817BrikenTrypsinDIA01 s2018_0817BrikenTrypsinDIA02
## 44180 44239
## s2018_0817BrikenTrypsinDIA03 s2018_0817BrikenTrypsinDIA04
## 44079 43640
## s2018_0817BrikenTrypsinDIA05 s2018_0817BrikenTrypsinDIA06
## 43872 44005
## s2018_0817BrikenTrypsinDIA07 s2018_0817BrikenTrypsinDIA08
## 47115 47749
## s2018_0817BrikenTrypsinDIA09 s2018_0817BrikenTrypsinDIA11
## 45503 45549
## s2018_0817BrikenTrypsinDIA12 s2018_0817BrikenTrypsinDIA13
## 45785 45820
## s2018_0817BrikenTrypsinDIA14 s2018_0817BrikenTrypsinDIA15
## 45830 46285
## s2018_0817BrikenTrypsinDIA16 s2018_0817BrikenTrypsinDIA17
## 45903 43394
## s2018_0817BrikenTrypsinDIA18 s2018_0817BrikenTrypsinDIA19
## 45218 45495## 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_wholemtb_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_wholemtb_dep <- DEP::add_rejections(mtb_diff, alpha=0.5, lfc=0.6)
##mtb_pca <- DEP::plot_pca(mtb_dep)
## The PCA plotter provided by DEP has some problems.
DEP::plot_cor(mtb_dep)
## Warning: The input is a data frame, convert it to the matrix.
## Saving to: excel/dep_result.xlsx## Note: zip::zip() is deprecated, please use zip::zipr() instead7 Minor change to plot_missval
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)## s2018_0315Briken01 s2018_0315Briken02 s2018_0315Briken03
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000
## Mean :0.355 Mean :0.374 Mean :0.403
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000
## s2018_0315Briken04 s2018_0315Briken05 s2018_0315Briken06
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:1.000
## Median :1.000 Median :1.000 Median :1.000
## Mean :0.731 Mean :0.783 Mean :0.757
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000
## s2018_0315Briken21 s2018_0315Briken22 s2018_0315Briken23
## Min. :0.000 Min. :0.000 Min. :0.00
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:0.00
## Median :1.000 Median :1.000 Median :0.00
## Mean :0.795 Mean :0.817 Mean :0.39
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.00
## Max. :1.000 Max. :1.000 Max. :1.00
## s2018_0315Briken24 s2018_0315Briken25 s2018_0315Briken26
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:1.000
## Median :0.000 Median :0.000 Median :1.000
## Mean :0.412 Mean :0.362 Mean :0.799
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000
## s2018_0502BrikenDIA01 s2018_0502BrikenDIA02 s2018_0502BrikenDIA03
## Min. :0.000 Min. :0.00 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000
## Median :0.000 Median :0.00 Median :0.000
## Mean :0.305 Mean :0.35 Mean :0.296
## 3rd Qu.:1.000 3rd Qu.:1.00 3rd Qu.:1.000
## Max. :1.000 Max. :1.00 Max. :1.000
## s2018_0502BrikenDIA04 s2018_0502BrikenDIA05 s2018_0502BrikenDIA06
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000
## Mean :0.366 Mean :0.401 Mean :0.359
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000
## s2018_0502BrikenDIA07 s2018_0502BrikenDIA08 s2018_0502BrikenDIA09
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :1.000 Median :1.000 Median :1.000
## Mean :0.747 Mean :0.738 Mean :0.535
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000
## s2018_0502BrikenDIA10 s2018_0502BrikenDIA11 s2018_0502BrikenDIA12
## Min. :0.0000 Min. :0.00 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:1.00 1st Qu.:0.000
## Median :0.0000 Median :1.00 Median :1.000
## Mean :0.0814 Mean :0.75 Mean :0.679
## 3rd Qu.:0.0000 3rd Qu.:1.00 3rd Qu.:1.000
## Max. :1.0000 Max. :1.00 Max. :1.000
## s2018_0726Briken01 s2018_0726Briken02 s2018_0726Briken03
## Min. :0.000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.000 Median :0.000 Median :0.0000
## Mean :0.063 Mean :0.102 Mean :0.0517
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.000 Max. :1.0000
## s2018_0726Briken04 s2018_0726Briken05 s2018_0726Briken06
## Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.0000 Median :0.000
## Mean :0.0517 Mean :0.0823 Mean :0.113
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :1.0000 Max. :1.0000 Max. :1.000
## s2018_0726Briken07 s2018_0726Briken08 s2018_0726Briken09
## Min. :0.000 Min. :0.000 Min. :0.00
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00
## Median :0.000 Median :0.000 Median :0.00
## Mean :0.115 Mean :0.109 Mean :0.14
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00
## Max. :1.000 Max. :1.000 Max. :1.00
## s2018_0726Briken11 s2018_0726Briken12 s2018_0726Briken13
## Min. :0.000 Min. :0.000 Min. :0.00
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00
## Median :0.000 Median :0.000 Median :0.00
## Mean :0.255 Mean :0.304 Mean :0.23
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:0.00
## Max. :1.000 Max. :1.000 Max. :1.00
## s2018_0726Briken14 s2018_0726Briken15 s2018_0726Briken16
## Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000 Median :0.000
## Mean :0.226 Mean :0.255 Mean :0.288
## 3rd Qu.:0.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000 Max. :1.000
## s2018_0726Briken17 s2018_0726Briken18 s2018_0726Briken19
## Min. :0.000 Min. :0.00 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000
## Median :0.000 Median :0.00 Median :0.000
## Mean :0.262 Mean :0.35 Mean :0.186
## 3rd Qu.:1.000 3rd Qu.:1.00 3rd Qu.:0.000
## Max. :1.000 Max. :1.00 Max. :1.000
## s2018_0817BrikenTrypsinDIA01 s2018_0817BrikenTrypsinDIA02
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.246 Mean :0.254
## 3rd Qu.:0.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## s2018_0817BrikenTrypsinDIA03 s2018_0817BrikenTrypsinDIA04
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.251 Mean :0.311
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## s2018_0817BrikenTrypsinDIA05 s2018_0817BrikenTrypsinDIA06
## Min. :0.000 Min. :0.00
## 1st Qu.:0.000 1st Qu.:0.00
## Median :0.000 Median :0.00
## Mean :0.283 Mean :0.29
## 3rd Qu.:1.000 3rd Qu.:1.00
## Max. :1.000 Max. :1.00
## s2018_0817BrikenTrypsinDIA07 s2018_0817BrikenTrypsinDIA08
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.000
## Mean :0.201 Mean :0.222
## 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.000
## s2018_0817BrikenTrypsinDIA09 s2018_0817BrikenTrypsinDIA11
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :1.000
## Mean :0.236 Mean :0.656
## 3rd Qu.:0.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## s2018_0817BrikenTrypsinDIA12 s2018_0817BrikenTrypsinDIA13
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :1.000 Median :1.000
## Mean :0.655 Mean :0.697
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## s2018_0817BrikenTrypsinDIA14 s2018_0817BrikenTrypsinDIA15
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :1.000 Median :1.000
## Mean :0.671 Mean :0.723
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## s2018_0817BrikenTrypsinDIA16 s2018_0817BrikenTrypsinDIA17
## Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.000
## Median :1.000 Median :0.000
## Mean :0.719 Mean :0.499
## 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :1.000 Max. :1.000
## s2018_0817BrikenTrypsinDIA18 s2018_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## s2018_0315Briken01 s2018_0315Briken02
## 810 855
## s2018_0315Briken03 s2018_0315Briken04
## 920 1669
## s2018_0315Briken05 s2018_0315Briken06
## 1789 1728
## s2018_0315Briken21 s2018_0315Briken22
## 1817 1867
## s2018_0315Briken23 s2018_0315Briken24
## 891 941
## s2018_0315Briken25 s2018_0315Briken26
## 828 1825
## s2018_0502BrikenDIA01 s2018_0502BrikenDIA02
## 697 799
## s2018_0502BrikenDIA03 s2018_0502BrikenDIA04
## 677 835
## s2018_0502BrikenDIA05 s2018_0502BrikenDIA06
## 915 819
## s2018_0502BrikenDIA07 s2018_0502BrikenDIA08
## 1706 1685
## s2018_0502BrikenDIA09 s2018_0502BrikenDIA10
## 1223 186
## s2018_0502BrikenDIA11 s2018_0502BrikenDIA12
## 1714 1552
## s2018_0726Briken01 s2018_0726Briken02
## 144 233
## s2018_0726Briken03 s2018_0726Briken04
## 118 118
## s2018_0726Briken05 s2018_0726Briken06
## 188 259
## s2018_0726Briken07 s2018_0726Briken08
## 263 248
## s2018_0726Briken09 s2018_0726Briken11
## 319 583
## s2018_0726Briken12 s2018_0726Briken13
## 694 526
## s2018_0726Briken14 s2018_0726Briken15
## 516 582
## s2018_0726Briken16 s2018_0726Briken17
## 658 598
## s2018_0726Briken18 s2018_0726Briken19
## 800 426
## s2018_0817BrikenTrypsinDIA01 s2018_0817BrikenTrypsinDIA02
## 561 580
## s2018_0817BrikenTrypsinDIA03 s2018_0817BrikenTrypsinDIA04
## 574 711
## s2018_0817BrikenTrypsinDIA05 s2018_0817BrikenTrypsinDIA06
## 646 662
## s2018_0817BrikenTrypsinDIA07 s2018_0817BrikenTrypsinDIA08
## 459 508
## s2018_0817BrikenTrypsinDIA09 s2018_0817BrikenTrypsinDIA11
## 538 1498
## s2018_0817BrikenTrypsinDIA12 s2018_0817BrikenTrypsinDIA13
## 1495 1592
## s2018_0817BrikenTrypsinDIA14 s2018_0817BrikenTrypsinDIA15
## 1532 1652
## s2018_0817BrikenTrypsinDIA16 s2018_0817BrikenTrypsinDIA17
## 1642 1139
## s2018_0817BrikenTrypsinDIA18 s2018_0817BrikenTrypsinDIA19
## 1485 14948 Request 20190523
Compare our ‘normal’ openswath output via hpgltools analysis vs. the umpire version. Secondary goal: With and without imputation.
ver <- "20180913"
tric_data <- read.csv(
paste0("results/tric/", ver, "/whole_8mz_tuberculist/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_", ver, ".xlsx"))
kept <- ! grepl(x=rownames(sample_annot), pattern="^s\\.\\.")
sample_annot <- sample_annot[kept, ]
devtools::load_all("~/scratch/git/SWATH2stats_myforked")## Loading SWATH2statss2s_exp <- sample_annotation(data=tric_data,
sample_annotation=sample_annot,
fullpeptidename_column="fullpeptidename")## Found the same mzXML files in the annotations and data.## Number of non-decoy peptides: 21557
## Number of decoy peptides: 939
## Decoy rate: 0.0436## 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 0.009## The average FDR by run on peptide level is 0.01## The average FDR by run on protein level is 0.047
## Target assay FDR: 0.02## Required overall m-score cutoff: 0.0070795
## achieving assay FDR: 0.0181## Target protein FDR: 0.02## Required overall m-score cutoff: 0.00089125
## achieving protein FDR: 0.0182## Original dimension: 133447, new dimension: 128204, difference: 5243.## Peptides need to have been quantified in more conditions than: 9.6 in order to pass this percentage-based threshold.## Fraction of peptides selected: 0.11## Original dimension: 135427, new dimension: 33028, difference: 102399.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: 0.000891250938133746## 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: 2999
## Final target proteins: 2999
## Final decoy proteins: 0## Peptides mapping to these protein entries selected:
## Total mapping peptides: 20921
## Final target peptides: 20921
## Final decoy peptides: 0## Total peptides selected from:
## Total peptides: 20921
## Final target peptides: 20921
## 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: 3016
## Protein identifiers: Rv2524c, Rv3716c, Rv1270c, Rv0724, Rv0161, Rv2535c## Number of proteins detected that are supported by a proteotypic peptide: 2888
## Number of proteotypic peptides detected: 20772## Number of proteins detected: 2890## First 6 protein identifiers: Rv2524c, Rv3716c, Rv1270c, Rv0724, Rv0161, Rv2535c## Before filtering:
## Number of proteins: 2888
## Number of peptides: 20772
##
## Percentage of peptides removed: 21.87%
##
## After filtering:
## Number of proteins: 2861
## Number of peptides: 16230## Before filtering:
## Number of proteins: 2861
## Number of peptides: 16230
##
## Percentage of peptides removed: 0.04%
##
## After filtering:
## Number of proteins: 2603
## Number of peptides: 16223matrix_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, "osw_protein_all.csv"))## Protein overview matrix results/swath2stats/20180913/osw_protein_all.csv written to working folder.## [1] 3873 13protein_matrix_mscore <- write_matrix_proteins(
filtered_ms, write.csv=TRUE,
filename=file.path(matrix_prefix, "osw_protein_matrix_mscore.csv"))## Protein overview matrix results/swath2stats/20180913/osw_protein_matrix_mscore.csv written to working folder.## [1] 2999 13peptide_matrix_mscore <- write_matrix_peptides(
filtered_ms, write.csv=TRUE,
filename=file.path(matrix_prefix, "osw_peptide_matrix_mscore.csv"))## Peptide overview matrix results/swath2stats/20180913/osw_peptide_matrix_mscore.csv written to working folder.## [1] 20921 13protein_matrix_filtered <- write_matrix_proteins(
filtered_all_filters, write.csv=TRUE,
filename=file.path(matrix_prefix, "osw_protein_matrix_filtered.csv"))## Protein overview matrix results/swath2stats/20180913/osw_protein_matrix_filtered.csv written to working folder.## [1] 2603 13peptide_matrix_filtered <- write_matrix_peptides(
filtered_all_filters, write.csv=TRUE,
filename=file.path(matrix_prefix, "osw_peptide_matrix_filtered.csv"))## Peptide overview matrix results/swath2stats/20180913/osw_peptide_matrix_filtered.csv written to working folder.## [1] 93860 13cols <- colnames(filtered_all_filters)
disaggregated <- disaggregate(filtered_all_filters, all.columns=TRUE)## The library contains 5 transitions per precursor.
## The data table was transformed into a table containing one row per transition.## One or several columns required by MSstats were not in the data. The columns were created and filled with NAs.
## Missing columns: productcharge, isotopelabeltype## isotopelabeltype was filled with light.prot_mtrx <- protein_matrix_filtered
rownames(prot_mtrx) <- gsub(pattern="^1\\/", replacement="", x=prot_mtrx[["proteinname"]])
prot_mtrx <- prot_mtrx[, -1]
## Important question: Did SWATH2stats reorder my data?
colnames(prot_mtrx) <- gsub(pattern="^(.*)(2018.*)$", replacement="s\\2", x=colnames(prot_mtrx))
reordered <- colnames(prot_mtrx)
metadata <- sample_annot[reordered, ]
osw_expt <- sm(create_expt(metadata,
count_dataframe=prot_mtrx,
gene_info=mtb_annotations))ver <- "20190327"
enc_metadata <- hpgltools:::read_metadata("sample_sheets/Mtb_dia_samples_encyclopedia_20190327.xlsx")
rownames(enc_metadata) <- paste0("s", enc_metadata[["sampleid"]])
enc_matrix <- read.table("encyclopedia/most_samples_quant_report.elib.proteins.txt", header=TRUE)
enc_pep_matrix <- read.table("encyclopedia/most_samples_quant_report.elib.peptides.txt", header=TRUE)
rownames(enc_matrix) <- enc_matrix[["Protein"]]
enc_matrix <- enc_matrix[, -1]
enc_matrix <- enc_matrix[, -1]
enc_matrix <- enc_matrix[, -1]
colnames(enc_matrix)## [1] "X2018_0502BrikenDIA01.mzML"
## [2] "X2018_0502BrikenDIA02.mzML"
## [3] "X2018_0502BrikenDIA03.mzML"
## [4] "X2018_0502BrikenDIA04.mzML"
## [5] "X2018_0502BrikenDIA05.mzML"
## [6] "X2018_0502BrikenDIA06.mzML"
## [7] "X2018_0502BrikenDIA07.mzML"
## [8] "X2018_0502BrikenDIA08.mzML"
## [9] "X2018_0502BrikenDIA09.mzML"
## [10] "X2018_0502BrikenDIA10.mzML"
## [11] "X2018_0502BrikenDIA11.mzML"
## [12] "X2018_0502BrikenDIA12.mzML"
## [13] "X2018_0726Briken01.mzML"
## [14] "X2018_0726Briken02.mzML"
## [15] "X2018_0726Briken03.mzML"
## [16] "X2018_0726Briken04.mzML"
## [17] "X2018_0726Briken05.mzML"
## [18] "X2018_0726Briken06.mzML"
## [19] "X2018_0726Briken07.mzML"
## [20] "X2018_0726Briken08.mzML"
## [21] "X2018_0726Briken09.mzML"
## [22] "X2018_0726Briken11.mzML"
## [23] "X2018_0726Briken12.mzML"
## [24] "X2018_0726Briken13.mzML"
## [25] "X2018_0726Briken14.mzML"
## [26] "X2018_0726Briken15.mzML"
## [27] "X2018_0726Briken16.mzML"
## [28] "X2018_0726Briken17.mzML"
## [29] "X2018_0726Briken18.mzML"
## [30] "X2018_0726Briken19.mzML"
## [31] "X2018_0817BrikenTrypsinDIA01.mzML"
## [32] "X2018_0817BrikenTrypsinDIA02.mzML"
## [33] "X2018_0817BrikenTrypsinDIA03.mzML"
## [34] "X2018_0817BrikenTrypsinDIA04.mzML"
## [35] "X2018_0817BrikenTrypsinDIA05.mzML"
## [36] "X2018_0817BrikenTrypsinDIA06.mzML"
## [37] "X2018_0817BrikenTrypsinDIA07.mzML"
## [38] "X2018_0817BrikenTrypsinDIA08.mzML"
## [39] "X2018_0817BrikenTrypsinDIA09.mzML"
## [40] "X2018_0817BrikenTrypsinDIA11.mzML"
## [41] "X2018_0817BrikenTrypsinDIA12.mzML"
## [42] "X2018_0817BrikenTrypsinDIA13.mzML"
## [43] "X2018_0817BrikenTrypsinDIA14.mzML"
## [44] "X2018_0817BrikenTrypsinDIA15.mzML"
## [45] "X2018_0817BrikenTrypsinDIA16.mzML"
## [46] "X2018_0817BrikenTrypsinDIA17.mzML"
## [47] "X2018_0817BrikenTrypsinDIA18.mzML"
## [48] "X2018_0817BrikenTrypsinDIA19.mzML"colnames(enc_matrix) <- gsub(pattern="X", replacement="s", x=colnames(enc_matrix))
colnames(enc_matrix) <- gsub(pattern="\\.mzML", replacement="", x=colnames(enc_matrix))
colnames(enc_matrix) <- gsub(pattern="^X", replacement="s", x=colnames(enc_matrix))
na_idx <- is.na(enc_matrix)
enc_matrix[na_idx] <- 0
enc_expt <- create_expt(metadata=enc_metadata, count_dataframe=enc_matrix, gene_info=NULL)## Reading the sample metadata.## The sample definitions comprises: 48 rows(samples) and 28 columns(metadata fields).## Matched 2632 annotations and counts.## Bringing together the count matrix and gene information.## The final expressionset has 2632 rows and 48 columns.9 Perform comparisons
For the first and simplest comparison, I will take the median by condition for these three data sets and see how they compare. Then I will subset the data into whole vs. filtered and do the logFC comparisons and compare again. Finally I will repeat these processes with my version of the imputation provided by DEP.
## The factor delta_filtrate has 3 rows.## The factor delta_whole has 3 rows.## The factor wt_filtrate has 3 rows.## The factor wt_whole has 3 rows.## The factor delta_filtrate has 9 rows.## The factor comp_filtrate has 10 rows.## The factor delta_whole has 8 rows.## The factor comp_whole has 9 rows.## The factor wt_filtrate has 6 rows.## The factor wt_whole has 6 rows.## The factor wt_filtrate has 12 rows.## The factor wt_whole has 12 rows.## The factor delta_filtrate has 9 rows.## The factor comp_filtrate has 9 rows.## The factor delta_whole has 9 rows.## The factor comp_whole has 9 rows.all <- merge(osw_medians, enc_medians, by="row.names")
rownames(all) <- all[["Row.names"]]
all[["Row.names"]] <- NULL
all <- merge(all, ump_medians, by="row.names")
rownames(all) <- all[["Row.names"]]
all[["Row.names"]] <- NULL
## OpenSWATH 'normal' vs. EncyclopeDIA
test_df <- all[, c("delta_filtrate", "delta_filtrate.x", "delta_filtrate.y")]
cor.test(test_df[[1]], test_df[[2]], method="spearman")## Warning in cor.test.default(test_df[[1]], test_df[[2]], method =
## "spearman"): Cannot compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: test_df[[1]] and test_df[[2]]
## S = 5.8e+08, p-value <2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.6746## Warning in cor.test.default(test_df[[1]], test_df[[3]], method =
## "spearman"): Cannot compute exact p-value with ties##
## Spearman's rank correlation rho
##
## data: test_df[[1]] and test_df[[3]]
## S = 6e+08, p-value <2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.6628## OpenSWATH 'normal' vs. DIAUmpire
test_df <- all[, c("delta_filtrate", "delta_filtrate.y")]
cor.test(test_df[[1]], test_df[[2]])##
## Pearson's product-moment correlation
##
## data: test_df[[1]] and test_df[[2]]
## t = 320, df = 2200, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9885 0.9903
## sample estimates:
## cor
## 0.9894keepers <- list(
"wt_cfwhole" = c("wt_filtrate", "wt_whole"),
"delta_cfwhole" = c("delta_filtrate", "delta_whole"),
"whole_deltawt" = c("delta_whole", "wt_whole"),
"cf_deltawt" = c("delta_filtrate", "wt_filtrate"))
ump_norm <- normalize_expt(ump_expt, filter=TRUE)## This function will replace the expt$expressionset slot with:## cbcb(data)## It will save copies of each step along the way
## in expt$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$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 8 low-count genes (2276 remaining).## Step 2: not normalizing the data.## Step 3: not converting the data.## Step 4: not transforming the data.## Step 5: not doing batch correction.## This function will replace the expt$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 (2276 remaining).## Step 2: normalizing the data with quant.## 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.## Plotting a PCA before surrogates/batch inclusion.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Finished running DE analyses, collecting outputs.## Comparing analyses.
whole_tables <- combine_de_tables(
ump_de,
keepers=keepers,
excel=paste0("excel/diaumpire_tables-v", ver, ".xlsx"))## Deleting the file excel/diaumpire_tables-v20190327.xlsx before writing the tables.## Writing a legend of columns.## Printing a pca plot before/after surrogates/batch estimation.## Working on 1/4: wt_cfwhole which is: wt_filtrate/wt_whole.## Found inverse table with wt_whole_vs_wt_filtrate## The ebseq table is null.## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.## Working on 2/4: delta_cfwhole which is: delta_filtrate/delta_whole.## Found inverse table with delta_whole_vs_delta_filtrate## The ebseq table is null.## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.## Working on 3/4: whole_deltawt which is: delta_whole/wt_whole.## Found inverse table with wt_whole_vs_delta_whole## The ebseq table is null.## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.## Working on 4/4: cf_deltawt which is: delta_filtrate/wt_filtrate.## Found inverse table with wt_filtrate_vs_delta_filtrate## The ebseq table is null.## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.
## Used Bon Ferroni corrected t test(s) between columns.## Adding venn plots for wt_cfwhole.
## Limma expression coefficients for wt_cfwhole; R^2: 0.507; equation: y = 0.399x - 4.78## Edger expression coefficients for wt_cfwhole; R^2: 0.475; equation: y = 1x - 7.81## DESeq2 expression coefficients for wt_cfwhole; R^2: 0.26; equation: y = 0.833x - 14.2## Adding venn plots for delta_cfwhole.
## Limma expression coefficients for delta_cfwhole; R^2: 0.704; equation: y = 0.526x - 3.16## Edger expression coefficients for delta_cfwhole; R^2: 0.38; equation: y = 0.894x - 5.55## DESeq2 expression coefficients for delta_cfwhole; R^2: 0.202; equation: y = 0.663x - 12.9## Warning: Removed 1 rows containing missing values (geom_hline).## Warning: Removed 1 rows containing missing values (geom_hline).## Adding venn plots for whole_deltawt.
## Limma expression coefficients for whole_deltawt; R^2: 0.918; equation: y = 0.995x - 0.168## Edger expression coefficients for whole_deltawt; R^2: 0.977; equation: y = 0.99x + 0.448## DESeq2 expression coefficients for whole_deltawt; R^2: 0.935; equation: y = 1.01x - 0.708## Adding venn plots for cf_deltawt.
## Limma expression coefficients for cf_deltawt; R^2: 0.905; equation: y = 0.896x - 0.825## Edger expression coefficients for cf_deltawt; R^2: 0.706; equation: y = 0.808x + 6.06## DESeq2 expression coefficients for cf_deltawt; R^2: 0.784; equation: y = 0.869x - 1.25## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 1 rows containing missing values (geom_hline).## Writing summary information.## Performing save of excel/diaumpire_tables-v20190327.xlsx.
10 Add imputation
## Found 11011 zeros in the data.## The data has not been filtered.## Filtering the data, turn on force to stop this.## This function will replace the expt$expressionset slot with:## pofa(data)## It will save copies of each step along the way
## in expt$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$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: pofa## Removing 471 low-count genes (2132 remaining).## Step 2: not normalizing the data.## Step 3: not converting the data.## Step 4: not transforming the data.## Step 5: not doing batch correction.## Invoking impute from MSnbase with the bpca method.## Found 46806 zeros in the data.## The data has not been filtered.## Filtering the data, turn on force to stop this.## This function will replace the expt$expressionset slot with:## pofa(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## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## 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: pofa## Removing 868 low-count genes (1764 remaining).## Step 2: not normalizing the data.## Step 3: not converting the data.## Step 4: not transforming the data.## Step 5: not doing batch correction.## Invoking impute from MSnbase with the bpca method.## Found 82251 zeros in the data.## The data has not been filtered.## Filtering the data, turn on force to stop this.## This function will replace the expt$expressionset slot with:## pofa(data)## It will save copies of each step along the way
## in expt$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$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: pofa## Removing 1558 low-count genes (726 remaining).## Step 2: not normalizing the data.## Step 3: not converting the data.## Step 4: not transforming the data.## Step 5: not doing batch correction.## Invoking impute from MSnbase with the bpca method.## This function will replace the expt$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 23 low-count genes (2580 remaining).## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## Step 4: transforming the data with log2.## transform_counts: Found 838 values equal to 0, adding 1 to the matrix.## Step 5: not doing batch correction.
## This function will replace the expt$expressionset slot with:## cbcb(data)## It will save copies of each step along the way
## in expt$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$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Leaving the data unconverted. It is often advisable to cpm/rpkm
## the data to normalize for sampling differences, keep in mind though that rpkm
## has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
## will try to detect this).## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Warning in t(log2(t(qcounts + 0.5)/(libsize + 1) * 1e+06)): NaNs produced## Removing 0 low-count genes (2132 remaining).## Step 2: not normalizing the data.## Step 3: not converting the data.## Step 4: not transforming the data.## Step 5: not doing batch correction.## This function will replace the expt$expressionset slot with:## 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## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## 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 (1869 remaining).## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## Step 4: not transforming the data.## Step 5: not doing batch correction.
## This function will replace the expt$expressionset slot with:## log2(svaseq(cpm(quant(data))))## It will save copies of each step along the way
## in expt$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$best_libsize## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Warning in normalize_expt(enc_expt, transform = "log2", norm = "quant", :
## Quantile normalization and sva do not always play well together.## Step 1: not doing count filtering.## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## Step 4: transforming the data with log2.## transform_counts: Found 2353 values equal to 0, adding 1 to the matrix.## Step 5: doing batch correction with svaseq.## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.## Passing off to all_adjusters.## batch_counts: Before batch/surrogate estimation, 120350 entries are x>1: 95.3%.## batch_counts: Before batch/surrogate estimation, 2353 entries are x==0: 1.86%.## batch_counts: Before batch/surrogate estimation, 3633 entries are 0<x<1: 2.88%.## The be method chose 6 surrogate variable(s).## Attempting svaseq estimation with 6 surrogates.## There are 286 (0.226%) elements which are < 0 after batch correction.
enc_imp_norm <- normalize_expt(enc_imputed, transform="log2", norm="quant",
convert="cpm", batch="svaseq")## This function will replace the expt$expressionset slot with:## log2(svaseq(cpm(quant(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## Filter is false, this should likely be set to something, good
## choices include cbcb, kofa, pofa (anything but FALSE). If you want this to
## stay FALSE, keep in mind that if other normalizations are performed, then the
## resulting libsizes are likely to be strange (potentially negative!)## Warning in normalize_expt(enc_imputed, transform = "log2", norm =
## "quant", : Quantile normalization and sva do not always play well together.## Step 1: not doing count filtering.## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## Warning in convert_counts(count_table, ...): There are 4800 negative values
## in the expressionset, modifying it.## Step 4: transforming the data with log2.## transform_counts: Found 4800 values equal to 0, adding 1 to the matrix.## 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, 79584 entries are x>1: 94.0%.## batch_counts: Before batch/surrogate estimation, 4800 entries are x==0: 5.67%.## batch_counts: Before batch/surrogate estimation, 288 entries are 0<x<1: 0.340%.## The be method chose 8 surrogate variable(s).## Attempting svaseq estimation with 8 surrogates.## There are 148 (0.175%) elements which are < 0 after batch correction.
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 8b0982a32ca67b6e0038facd2536a24e06bd4da8## This is hpgltools commit: Fri Jun 21 10:35:35 2019 -0400: 8b0982a32ca67b6e0038facd2536a24e06bd4da8## Saving to dia_umpire_20190308-v20190327.rda.xzR version 3.6.0 (2019-04-26)
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: ruv(v.0.9.7), SWATH2stats(v.1.13.5), imputeLCMD(v.2.0), impute(v.1.58.0), pcaMethods(v.1.76.0), 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.1.0-11), SummarizedExperiment(v.1.14.0), DelayedArray(v.0.10.0), BiocParallel(v.1.18.0), matrixStats(v.0.54.0), GenomicRanges(v.1.36.0), GenomeInfoDb(v.1.20.0), IRanges(v.2.18.1), S4Vectors(v.0.22.0), DEP(v.1.5.3), dplyr(v.0.8.1), testthat(v.2.1.1), hpgltools(v.1.0), Biobase(v.2.44.0) and BiocGenerics(v.0.30.0)
loaded via a namespace (and not attached): rtracklayer(v.1.44.0), tidyr(v.0.8.3), acepack(v.1.4.1), ggplot2(v.3.2.0), bit64(v.0.9-7), knitr(v.1.23), rpart(v.4.1-15), data.table(v.1.12.2), RCurl(v.1.95-4.12), doParallel(v.1.0.14), GenomicFeatures(v.1.36.3), preprocessCore(v.1.46.0), callr(v.3.2.0), cowplot(v.0.9.4), usethis(v.1.5.0), RSQLite(v.2.1.1), europepmc(v.0.3), bit(v.1.1-14), enrichplot(v.1.4.0), xml2(v.1.2.0), httpuv(v.1.5.1), assertthat(v.0.2.1), viridis(v.0.5.1), xfun(v.0.8), hms(v.0.4.2), evaluate(v.0.14), promises(v.1.0.1), DEoptimR(v.1.0-8), progress(v.1.2.2), caTools(v.1.17.1.2), geneplotter(v.1.62.0), igraph(v.1.2.4.1), DBI(v.1.0.0), htmlwidgets(v.1.3), purrr(v.0.3.2), selectr(v.0.4-1), backports(v.1.1.4), annotate(v.1.62.0), biomaRt(v.2.40.0), remotes(v.2.1.0), BRAIN(v.1.30.0), withr(v.2.1.2), ggforce(v.0.2.2), triebeard(v.0.3.0), robustbase(v.0.93-5), checkmate(v.1.9.3), GenomicAlignments(v.1.20.1), fdrtool(v.1.2.15), prettyunits(v.1.0.2), cluster(v.2.1.0), DOSE(v.3.10.2), lazyeval(v.0.2.2), crayon(v.1.3.4), genefilter(v.1.66.0), edgeR(v.3.26.5), pkgconfig(v.2.0.2), labeling(v.0.3), tweenr(v.1.0.1), nlme(v.3.1-140), PolynomF(v.2.0-2), pkgload(v.1.0.2), ProtGenerics(v.1.16.0), nnet(v.7.3-12), devtools(v.2.0.2), rlang(v.0.4.0), affyio(v.1.54.0), rprojroot(v.1.3-2), polyclip(v.1.10-0), graph(v.1.62.0), urltools(v.1.7.3), boot(v.1.3-22), zoo(v.1.8-6), base64enc(v.0.1-3), ggridges(v.0.5.1), GlobalOptions(v.0.1.0), processx(v.3.3.1), png(v.0.1-7), viridisLite(v.0.3.0), rjson(v.0.2.20), mzR(v.2.18.0), bitops(v.1.0-6), shinydashboard(v.0.7.1), KernSmooth(v.2.23-15), pander(v.0.6.3), Biostrings(v.2.52.0), blob(v.1.1.1), shape(v.1.4.4), stringr(v.1.4.0), qvalue(v.2.16.0), readr(v.1.3.1), gridGraphics(v.0.4-1), scales(v.1.0.0), memoise(v.1.1.0), magrittr(v.1.5), plyr(v.1.8.4), gplots(v.3.0.1.1), gdata(v.2.18.0), zlibbioc(v.1.30.0), compiler(v.3.6.0), RColorBrewer(v.1.1-2), clue(v.0.3-57), lme4(v.1.1-21), DESeq2(v.1.24.0), Rsamtools(v.2.0.0), cli(v.1.1.0), affy(v.1.62.0), XVector(v.0.24.0), ps(v.1.3.0), htmlTable(v.1.13.1), Formula(v.1.2-3), MASS(v.7.3-51.4), mgcv(v.1.8-28), tidyselect(v.0.2.5), vsn(v.3.52.0), stringi(v.1.4.3), yaml(v.2.2.0), GOSemSim(v.2.10.0), locfit(v.1.5-9.1), latticeExtra(v.0.6-28), MALDIquant(v.1.19.3), ggrepel(v.0.8.1), grid(v.3.6.0), fastmatch(v.1.1-0), tools(v.3.6.0), circlize(v.0.4.6), rstudioapi(v.0.10), foreign(v.0.8-71), foreach(v.1.4.4), gridExtra(v.2.3), farver(v.1.1.0), mzID(v.1.22.0), ggraph(v.1.0.2), digest(v.0.6.19), rvcheck(v.0.1.3), BiocManager(v.1.30.4), shiny(v.1.3.2), Rcpp(v.1.0.1), later(v.0.8.0), ncdf4(v.1.16.1), httr(v.1.4.0), MSnbase(v.2.10.1), AnnotationDbi(v.1.46.0), ComplexHeatmap(v.2.0.0), colorspace(v.1.4-1), rvest(v.0.3.4), XML(v.3.98-1.20), fs(v.1.3.1), splines(v.3.6.0), RBGL(v.1.60.0), ggplotify(v.0.0.3), sessioninfo(v.1.1.1), xtable(v.1.8-4), jsonlite(v.1.6), nloptr(v.1.2.1), corpcor(v.1.6.9), UpSetR(v.1.4.0), Vennerable(v.3.1.0.9000), R6(v.2.4.0), Hmisc(v.4.2-0), pillar(v.1.4.2), htmltools(v.0.3.6), mime(v.0.7), glue(v.1.3.1), minqa(v.1.2.4), clusterProfiler(v.3.12.0), DT(v.0.7), codetools(v.0.2-16), fgsea(v.1.10.0), pkgbuild(v.1.0.3), lattice(v.0.20-38), tibble(v.2.1.3), sva(v.3.32.1), pbkrtest(v.0.4-7), curl(v.3.3), colorRamps(v.2.3), gtools(v.3.8.1), zip(v.2.0.2), GO.db(v.3.8.2), openxlsx(v.4.1.0.1), survival(v.2.44-1.1), limma(v.3.40.2), rmarkdown(v.1.13), desc(v.1.2.0), munsell(v.0.5.0), DO.db(v.2.9), GetoptLong(v.0.1.7), GenomeInfoDbData(v.1.2.1), iterators(v.1.0.10), variancePartition(v.1.14.0), reshape2(v.1.4.3) and gtable(v.0.3.0)
---
title: "M. tuberculosis 20190327: DIA-Umpire based OpenSWATH workflow."
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
  html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
    number_sections: true
    self_contained: true
    theme: readable
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
  rmdformats::readthedown:
    code_download: true
    code_folding: show
    df_print: paged
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    width: 300
    keep_md: false
    mode: selfcontained
    toc_float: true
  BiocStyle::html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
    toc_float: true
---

<style type="text/css">
body, td {
  font-size: 16px;
}
code.r{
  font-size: 16px;
}
pre {
 font-size: 16px
}
</style>

```{r options, include=FALSE}
library("hpgltools")
tt <- devtools::load_all("/data/hpgltools")
knitr::opts_knit$set(width=120,
                     progress=TRUE,
                     verbose=TRUE,
                     echo=TRUE)
knitr::opts_chunk$set(error=TRUE,
                      dpi=96)
old_options <- options(digits=4,
                       stringsAsFactors=FALSE,
                       knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=10))
rundate <- format(Sys.Date(), format="%Y%m%d")
previous_file <- "02_estimation_infection_20180822.Rmd"
ver <- "20190327"

##tmp <- sm(loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz")))
rmd_file <- "dia_umpire_20190308.Rmd"
```

# Rework DIA-Umpire to feed OpenSWATH.

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.

## Invoke DIA Umpire

```{bash umpire, eval=FALSE}
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
```

## Convert DIA Umpire results

```{bash convert, eval=FALSE}
msconvert --mzXML results/02mgf/*.mgf
mv *.mzXML results/03_dia_umpire_mzxml
```

## Search the Umpire results

```{bash search_umpire, eval=FALSE}
comet \
    -Pparameters/comet_dia_umpire_params.txt \
    results/03_dia_umpire_mzxml/*.mzXML
```

## Merge them

```{bash merge_xinteract, eval=FALSE}
xinteract \
    -dDECOY_ \
    -OARPd \
    -Ninteract.comet.pep.xml \
    results/03_dia_umpire_mzxml/*.pep.xml

mv interact.comet.pep.xml results/04_dia_umpire_xinteract
```

## Combine the statistics

```{bash protein_prophet, eval=FALSE}
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
```

```{r extract_pct_mayu, eval=FALSE}
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
```


```{bash umpire_contd, eval=FALSE}
## 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"
```

# DEP usage

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.

## Protein annotations

```{r protein_annotations}
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)
```

## Preprocess intensities in preparation for DEP

```{r swath2stats, fig.show="hide"}
ver <- "20190327"
ump_data <- read.csv(
  paste0("results/tric/", ver, "/whole_8mz_dia_umpire/comet_HCD.tsv"), sep="\t")
ump_data[["ProteinName"]] <- gsub(pattern="^(.*)_.*$", replacement="\\1",
                                  x=ump_data[["ProteinName"]])
sample_annot <- extract_metadata(paste0("sample_sheets/Mtb_dia_samples_umpire_20190522.xlsx"))
colnames(sample_annot)
rownames(sample_annot)
levels(as.factor(ump_data[["filename"]]))

devtools::load_all("~/scratch/git/SWATH2stats_myforked")
ump_s2s <- SWATH2stats::sample_annotation(data=ump_data,
                                          sample_annotation=sample_annot,
                                          fullpeptidename_column="fullpeptidename")

decoy_lists <- assess_decoy_rate(ump_s2s)
## This seems a bit high to me, yesno?
ump_fdr <- assess_fdr_overall(ump_s2s, output="Rconsole", plot=TRUE)

ump_byrun_fdr <- assess_fdr_byrun(ump_s2s, FFT=0.7, plot=TRUE, output="Rconsole")
ump_chosen_mscore2 <- mscore4assayfdr(ump_s2s, FFT=0.7, fdr_target=0.02)
ump_prot_score <- mscore4protfdr(ump_s2s, FFT=0.7, fdr_target=0.02)

ump_filtered_ms <- filter_mscore(ump_s2s, ump_chosen_mscore2)
ump_filtered_fq <- filter_mscore_freqobs(ump_s2s, 0.01, 0.8, rm.decoy=FALSE)
ump_filtered_ms_fdr <- filter_mscore_fdr(ump_filtered_ms, FFT=0.7,
                                         overall_protein_fdr_target=ump_prot_score,
                                         upper_overall_peptide_fdr_limit=0.05)
ump_filtered_ms_fdr_pr <- filter_proteotypic_peptides(ump_filtered_ms_fdr)
ump_filtered_ms_fdr_pr_all <- filter_all_peptides(ump_filtered_ms_fdr_pr)
ump_filtered_ms_fdr_pr_all_str <- filter_on_max_peptides(data=ump_filtered_ms_fdr_pr_all, n_peptides=10)
ump_filtered_all_filters <- filter_on_min_peptides(data=ump_filtered_ms_fdr_pr_all_str, n_peptides=3)

ump_matrix_prefix <- file.path("results", "swath2stats", ver)
if (!file.exists(ump_matrix_prefix)) {
  dir.create(ump_matrix_prefix)
}
protein_matrix_all <- write_matrix_proteins(
  ump_s2s, write.csv=TRUE,
  filename=file.path(ump_matrix_prefix, "ump_protein_all.csv"))
dim(protein_matrix_all)
protein_matrix_mscore <- write_matrix_proteins(
  ump_filtered_ms, write.csv=TRUE,
  filename=file.path(ump_matrix_prefix, "ump_protein_matrix_mscore.csv"))
dim(protein_matrix_mscore)
peptide_matrix_mscore <- write_matrix_peptides(
  ump_filtered_ms, write.csv=TRUE,
  filename=file.path(ump_matrix_prefix, "ump_peptide_matrix_mscore.csv"))
dim(peptide_matrix_mscore)
protein_matrix_filtered <- write_matrix_proteins(
  ump_filtered_all_filters, write.csv=TRUE,
  filename=file.path(ump_matrix_prefix, "ump_protein_matrix_filtered.csv"))
dim(protein_matrix_filtered)
peptide_matrix_filtered <- write_matrix_peptides(
  ump_filtered_all_filters, write.csv=TRUE,
  filename=file.path(ump_matrix_prefix, "ump_peptide_matrix_filtered.csv"))
dim(peptide_matrix_filtered)
```

# Look at raw data

```{r msraw}
mzml_data <- extract_msraw_data(sample_annot, parallel=FALSE,
                                format="mzML",
                                allow_window_overlap=FALSE,
                                file_column="mzmlfile",
                                savefile="testing.rda")
```

# Get pyprophet data

```{r pyprophet_plots}
in_ver <- "20190522"
pyp_metadata <- glue::glue("sample_sheets/Mtb_dia_samples_umpire_{in_ver}.xlsx")
pyprophet_fun <- extract_pyprophet_data(metadata=pyp_metadata,
                                        pyprophet_column="diascored")

intensities_esxG <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename", scale="log",
                                           title="esxG Intensities", column="intensity", protein="Rv0287")
pp(file="images/ump_esxG_intensities.png", image=intensities_esxG)

intensities_esxH <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename",
                                           title="esxH Intensities",
                                           scale="log", column="intensity", protein="Rv0288")
pp(file="images/ump_esxH_intensities.png", image=intensities_esxH)

intensities_lpqH <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename", scale="log",
                                           title="lpqH_intensities", column="intensity", protein="Rv3763")
pp(file="images/ump_lpqh_intensities.png", image=intensities_lpqH)

intensities_groel1 <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename",
                                             title="groEL1 intensities", scale="log",
                                             column="intensity", protein="Rv3417")
pp(file="images/ump_groel1_intensities.png", image=intensities_groel1)

intensities_groel2 <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename",
                                             title="groEL2 intensities", scale="log",
                                             column="intensity", protein="Rv0440")
pp(file="images/ump_groel2_intensities.png", image=intensities_groel2)

intensities_fap <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename",
                                          title="fap intensities", scale="log",
                                          column="intensity", protein="Rv1860")
pp(file="images/ump_fap_intensities.png", image=intensities_fap)

intensities_katg <- plot_pyprophet_protein(pyprophet_fun, expt_names="figurename",
                                           title="katG intensities", scale="log",
                                           column="intensity", protein="Rv1908")
pp(file="images/ump_katg_intensities.png", image=intensities_katg)
```

# Get intensities

```{r tb_expt, fig.show="hide"}
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)
rownames(sample_annot) %in% colnames(intensities)
##rownames(sample_annot)
##colnames(intensities)

reordered <- colnames(intensities)
metadata <- sample_annot[reordered, ]
ump_expt <- create_expt(sample_annot,
                        count_dataframe=intensities,
                        gene_info=mtb_annotations)
```

# Pass the data to DEP and see what happens.

```{r make_se_shenanigans}
devtools::load_all("~/scratch/git/DEP")
library(dplyr)
my_make_se <- function (proteins_unique, columns, expdesign,
                        id_column="ID", name_column="name") {
  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_column]]
  raw <- proteins_unique[, columns]
  raw[raw == 0] <- NA
  raw <- log2(raw)
  ## Perhaps consider the following?
  ## raw <- log1p(raw) / log(2)
  expdesign <- dplyr::mutate(expdesign, condition=make.names(condition))
  ## I changed the following because it didn't make sense to me.
  if (is.null(expdesign[[id_column]])) {
    expdesign <- expdesign %>%
      tidyr::unite(condition, replicate, remove=FALSE)
  }
  rownames(expdesign) <- expdesign[[id_column]]
  matched <- match(make.names(DEP:::delete_prefix(expdesign[["label"]])),
                   make.names(DEP:::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_column]]
  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)
}
```

```{r start_DEP}
library(DEP)
devtools::load_all("~/scratch/git/DEP")
library(SummarizedExperiment)

design <- pData(ump_expt)
design[["label"]] <- design[["sampleid"]]
design[["replicate"]] <- design[["sampleid"]]

my_se <- SummarizedExperiment(
  assays=exprs(ump_expt),
  colData=design,
  rowData=fData(ump_expt))

mtb_unique <- as.data.frame(exprs(ump_expt))
mtb_unique[["name"]] <- rownames(mtb_unique)
mtb_unique[["ID"]] <- rownames(mtb_unique)
design[["ID"]] <- rownames(design)
intensity_columns <- 1:60
## mtb_se <- wtf(mtb_unique, intensity_columns, design)
mtb_se <- my_make_se(mtb_unique, intensity_columns, design)

DEP::plot_frequency(mtb_se)
dim(assay(mtb_se))
mtb_filt <- DEP::filter_missval(mtb_se, thr=2)
dim(assay(mtb_filt))
DEP::plot_numbers(mtb_se)
DEP::plot_coverage(mtb_se)

mtb_norm <- DEP::normalize_vsn(mtb_se)
DEP::plot_normalization(mtb_se, mtb_norm)

DEP::plot_missval(mtb_se)
DEP::plot_detect(mtb_se)

mtb_imp <- DEP::impute(mtb_norm, fun="MinProb", q=0.01)
mtb_imp_man <- DEP::impute(mtb_norm, fun="man", shift=1.8, scale=0.3)
DEP::plot_imputation(mtb_norm, mtb_imp)

norm_exprs <- assay(mtb_norm)
imp_exprs <- assay(mtb_imp)
man_exprs <- assay(mtb_imp_man)
summary(norm_exprs)
summary(imp_exprs)
summary(man_exprs)
colSums(norm_exprs, na.rm=TRUE)
colSums(imp_exprs)

mtb_diff <- DEP::test_diff(mtb_imp, type="all")
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"))

mtb_dep <- DEP::add_rejections(mtb_diff, alpha=0.5, lfc=0.6)
##mtb_pca <- DEP::plot_pca(mtb_dep)
## The PCA plotter provided by DEP has some problems.

DEP::plot_cor(mtb_dep)
DEP::plot_heatmap(mtb_dep, type="centered", kmeans=TRUE)
DEP::plot_heatmap(mtb_dep, type="contrast", kmeans=TRUE)

DEP::plot_volcano(mtb_dep, contrast="wt_whole_vs_delta_whole", adjusted=FALSE)
DEP::plot_volcano(mtb_dep, contrast="wt_filtrate_vs_delta_filtrate")
DEP::plot_cond(mtb_dep)
mtb_result <- DEP::get_results(mtb_dep)
plot_single(mtb_dep, proteins = c("Rv0287", "Rv0288"))
written_dep <- hpgltools::write_xls(data=mtb_result, excel="excel/dep_result.xlsx")
```

# Minor change to plot_missval

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.

```{r my_plot_missing}
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)
defined_by_protein <- rowSums(def_mtrx)
head(defined_by_protein)
defined_by_sample <- colSums(def_mtrx)
defined_by_sample
```

# Request 20190523

Compare our 'normal' openswath output via hpgltools analysis vs. the umpire
version.  Secondary goal: With and without imputation.

```{r compare umpire_vs_normal}
ver <- "20180913"
tric_data <- read.csv(
  paste0("results/tric/", ver, "/whole_8mz_tuberculist/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_", ver, ".xlsx"))
kept <- ! grepl(x=rownames(sample_annot), pattern="^s\\.\\.")
sample_annot <- sample_annot[kept, ]
devtools::load_all("~/scratch/git/SWATH2stats_myforked")
s2s_exp <- sample_annotation(data=tric_data,
                             sample_annotation=sample_annot,
                             fullpeptidename_column="fullpeptidename")

decoy_lists <- assess_decoy_rate(s2s_exp)
## This seems a bit high to me, yesno?
fdr_overall <- assess_fdr_overall(s2s_exp, output="Rconsole", plot=TRUE)
byrun_fdr <- assess_fdr_byrun(s2s_exp, FFT=0.7, plot=TRUE, output="Rconsole")
chosen_mscore <- mscore4assayfdr(s2s_exp, FFT=0.7, fdr_target=0.02)
prot_score <- mscore4protfdr(s2s_exp, FFT=0.7, fdr_target=0.02)
filtered_ms <- filter_mscore(s2s_exp, chosen_mscore)
filtered_fq <- filter_mscore_freqobs(s2s_exp, 0.01, 0.8, rm.decoy=FALSE)
filtered_ms_fdr <- filter_mscore_fdr(filtered_ms, FFT=0.7,
                                     overall_protein_fdr_target=prot_score,
                                     upper_overall_peptide_fdr_limit=0.05)
filtered_ms_fdr_pr <- filter_proteotypic_peptides(filtered_ms_fdr)
filtered_ms_fdr_pr_all <- filter_all_peptides(filtered_ms_fdr_pr)
filtered_ms_fdr_pr_all_str <- filter_on_max_peptides(data=filtered_ms_fdr_pr_all, n_peptides=10)
filtered_all_filters <- filter_on_min_peptides(data=filtered_ms_fdr_pr_all_str, n_peptides=3)

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, "osw_protein_all.csv"))
dim(protein_matrix_all)
protein_matrix_mscore <- write_matrix_proteins(
  filtered_ms, write.csv=TRUE,
  filename=file.path(matrix_prefix, "osw_protein_matrix_mscore.csv"))
dim(protein_matrix_mscore)
peptide_matrix_mscore <- write_matrix_peptides(
  filtered_ms, write.csv=TRUE,
  filename=file.path(matrix_prefix, "osw_peptide_matrix_mscore.csv"))
dim(peptide_matrix_mscore)
protein_matrix_filtered <- write_matrix_proteins(
  filtered_all_filters, write.csv=TRUE,
  filename=file.path(matrix_prefix, "osw_protein_matrix_filtered.csv"))
dim(protein_matrix_filtered)
peptide_matrix_filtered <- write_matrix_peptides(
  filtered_all_filters, write.csv=TRUE,
  filename=file.path(matrix_prefix, "osw_peptide_matrix_filtered.csv"))
dim(peptide_matrix_filtered)
cols <- colnames(filtered_all_filters)
disaggregated <- disaggregate(filtered_all_filters, all.columns=TRUE)
msstats_input <- convert_MSstats(disaggregated)

prot_mtrx <- protein_matrix_filtered
rownames(prot_mtrx) <- gsub(pattern="^1\\/", replacement="", x=prot_mtrx[["proteinname"]])
prot_mtrx <- prot_mtrx[, -1]
## Important question: Did SWATH2stats reorder my data?
colnames(prot_mtrx) <- gsub(pattern="^(.*)(2018.*)$", replacement="s\\2", x=colnames(prot_mtrx))
reordered <- colnames(prot_mtrx)
metadata <- sample_annot[reordered, ]
osw_expt <- sm(create_expt(metadata,
                           count_dataframe=prot_mtrx,
                           gene_info=mtb_annotations))
```

```{r compare_encylopedia}
ver <- "20190327"
enc_metadata <- hpgltools:::read_metadata("sample_sheets/Mtb_dia_samples_encyclopedia_20190327.xlsx")
rownames(enc_metadata) <- paste0("s", enc_metadata[["sampleid"]])
enc_matrix <- read.table("encyclopedia/most_samples_quant_report.elib.proteins.txt", header=TRUE)
enc_pep_matrix <- read.table("encyclopedia/most_samples_quant_report.elib.peptides.txt", header=TRUE)
rownames(enc_matrix) <- enc_matrix[["Protein"]]
enc_matrix <- enc_matrix[, -1]
enc_matrix <- enc_matrix[, -1]
enc_matrix <- enc_matrix[, -1]
colnames(enc_matrix)
colnames(enc_matrix) <- gsub(pattern="X", replacement="s", x=colnames(enc_matrix))
colnames(enc_matrix) <- gsub(pattern="\\.mzML", replacement="", x=colnames(enc_matrix))
colnames(enc_matrix) <- gsub(pattern="^X", replacement="s", x=colnames(enc_matrix))

na_idx <- is.na(enc_matrix)
enc_matrix[na_idx] <- 0

enc_expt <- create_expt(metadata=enc_metadata, count_dataframe=enc_matrix, gene_info=NULL)
```

# Perform comparisons

For the first and simplest comparison, I will take the median by condition for
these three data sets and see how they compare.  Then I will subset the data
into whole vs. filtered and do the logFC comparisons and compare again.  Finally
I will repeat these processes with my version of the imputation provided by DEP.

```{r first_comparisons}
osw_medians <- median_by_factor(osw_expt)
enc_medians <- median_by_factor(enc_expt)
ump_medians <- median_by_factor(ump_expt)

all <- merge(osw_medians, enc_medians, by="row.names")
rownames(all) <- all[["Row.names"]]
all[["Row.names"]] <- NULL
all <- merge(all, ump_medians, by="row.names")
rownames(all) <- all[["Row.names"]]
all[["Row.names"]] <- NULL

## OpenSWATH 'normal' vs. EncyclopeDIA
test_df <- all[, c("delta_filtrate", "delta_filtrate.x", "delta_filtrate.y")]
cor.test(test_df[[1]], test_df[[2]], method="spearman")
cor.test(test_df[[1]], test_df[[3]], method="spearman")

## OpenSWATH 'normal' vs. DIAUmpire
test_df <- all[, c("delta_filtrate", "delta_filtrate.y")]
cor.test(test_df[[1]], test_df[[2]])

keepers <- list(
  "wt_cfwhole" = c("wt_filtrate", "wt_whole"),
  "delta_cfwhole" = c("delta_filtrate", "delta_whole"),
  "whole_deltawt" = c("delta_whole", "wt_whole"),
  "cf_deltawt" = c("delta_filtrate", "wt_filtrate"))
ump_norm <- normalize_expt(ump_expt, filter=TRUE)
ump_de <- all_pairwise(ump_norm, force=TRUE)
whole_tables <- combine_de_tables(
  ump_de,
  keepers=keepers,
  excel=paste0("excel/diaumpire_tables-v", ver, ".xlsx"))
```

# Add imputation

```{r imputation_addition}
osw_imputed <- impute_expt(osw_expt)
enc_imputed <- impute_expt(enc_expt)
ump_imputed <- impute_expt(ump_expt)

osw_norm <- normalize_expt(osw_expt, transform="log2", norm="quant", convert="cpm", filter=TRUE)
hpgltools::plot_pca(osw_norm)$plot
osw_imp_norm <- normalize_expt(osw_imputed, filter=TRUE)

osw_imp_norm <- normalize_expt(osw_imp_norm, filter=TRUE, convert="cpm", norm="quant")
hpgltools::plot_pca(osw_imp_norm)$plot

enc_norm <- normalize_expt(enc_expt, transform="log2", norm="quant",
                           convert="cpm", batch="svaseq")
hpgltools::plot_pca(enc_norm)$plot
enc_imp_norm <- normalize_expt(enc_imputed, transform="log2", norm="quant",
                               convert="cpm", batch="svaseq")
hpgltools::plot_pca(enc_imp_norm)$plot
```

```{r saveme}
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())
}
```

```{r loadme, eval=FALSE}
tmp <- loadme(filename=this_save)
```
