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generateSummary.sh
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generateSummary.sh
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#!/bin/bash -e
headerFile=$1
variantDBfile=$2
projectDir=$3
summaryfile=./summary.html
cp $headerFile $summaryfile
# This-preprocessing step is necessary to ensure that the results will be reported alphanumerically sorted
# whereas Nextflow reports them in the order of job completion by default.
echo Renaming the output files nicely...
for subdir in $(ls */ -d | tr -d '/'); do
reportFileName=$subdir/report/report.html
# Check if this report file exists, else, its contents were already relabelled before
if [[ -f $reportFileName ]]; then
# Rename the files/folders such that sample name is a prefix
sampleName=$(cat $reportFileName | grep "Sample name:" | awk -F'>' '{ print $4 }' | awk -F'<' '{print $1}')
for file in $(ls $subdir); do
mv $subdir/$file $subdir/${sampleName}_${file}
done
else
sampleName=$(ls $subdir | head -n 1 | awk -F '_' '{print $1}')
fi
mv $subdir $sampleName
done
#######################################################
# Generate a table summarising amplification specificity and the completeness of the genomic coverage.
echo Gathering and tabulating summary statistics...
echo "<h2>Summary</h2>" >> $summaryfile
echo '<table>' >> $summaryfile
echo "<tr>" >> $summaryfile
echo "<th>Sample#</th><th>Sample name</th><th>Total #reads</th><th>Reads aligned PF*</th><th>Genomic coordinates 0X</th><th>Genomic coordinates <10X</th>" >> $summaryfile
echo "</tr>" >> $summaryfile
# Also generate a separate csv file containing the summary table
csvFile=./summaryTable.csv
echo "Sample#,Sample name,Total #reads,Reads aligned PF,Genomic coordinates 0X,Genomic coordinates <10X,QC_issues" >> $csvFile
sampleNames=()
plottingData=()
coverageFiles=()
QCflags=()
for sampleName in $(ls */ -d | tr -d '/'); do
sampleNames+=($sampleName)
# Extract some of the statistics from individual report files
reportFileName=$sampleName/${sampleName}_report/report.html
numReads=$(cat $reportFileName | grep "Total number of reads:" | awk -F '>' '{print $4}' | awk -F '<' '{print $1}')
readsMapped=$(cat $reportFileName | grep "Reads passing filter" | awk -F '>' '{print $4}' | awk -F '<' '{print $1}')
pctMapped=$(echo $readsMapped | awk '{print $2}' | tr -d '()%')
numUncovered=$(cat $reportFileName | grep "All genomic coordinates:" | awk -F '>' '{print $4}' | awk -F '<' '{print $1}')
numPoorlyCovered=$(cat $reportFileName | grep "All genomic coordinates:" | awk -F '>' '{print $6}' | awk -F '<' '{print $1}')
plottingData+=($sampleName $(echo $readsMapped | awk '{print $1}' | tr -d 'nt'))
coverageFiles+=($sampleName/${sampleName}_pos-coverage-quality.tsv)
# Record as a row in the data table
echo "<tr>" >> $summaryfile
echo "<td>${#sampleNames[@]}</td><td><a href=\"./$sampleName/${sampleName}_report/report.html\">$sampleName</a></td>" >> $summaryfile
echo "<td>$numReads</td><td>$readsMapped</td><td>$numUncovered</td><td>$numPoorlyCovered</td>" >> $summaryfile
echo "</tr>" >> $summaryfile
flags=($(cat $sampleName/${sampleName}_qc-flags.txt | tr '\n' ',' | sed 's/,/<br>/g'))
if [[ -z $flags ]]; then
QC_flags+=(None)
else
QC_flags+=($flags)
fi
echo "${#sampleNames[@]},$sampleName,$numReads,$readsMapped,$numUncovered,$numPoorlyCovered,${flags[@]}" >> $csvFile
done
echo '</table>' >> $summaryfile
echo "<br><br><br>" >> $summaryfile
#######################################################
# Insert a bar plot comparing covid reads in all samples of this run.
echo Generating sample load comparison plots...
SNRs=($( $projectDir/plotSNR.py ${plottingData[@]} | tr -d ',[]' ))
echo "<div>" >> $summaryfile
echo " <div id=\"figdiv\">" >> $summaryfile
echo " <img src=\"./covidReadsSummary.png\" alt=\"Num. SC2 reads\" width=\"100%\" class=\"center\">" >> $summaryfile
echo " </div>" >> $summaryfile
echo " <div id=\"figdiv\" style=\"padding-left: 5%; width: 45%;\">" >> $summaryfile
echo " *Quantity of raw reads that align to the reference sequence and pass filter, i.e. the read length after adaptor trimming ≥30 and minimum read quality ≥20 within a sliding window of width 4. SNR refers to the ratio of SC2-mapping reads aligned that pass filter in the sample vs. that in the auto-detected negative control samples (if any). The dashed line represents the baseline level of covid reads detected from the negative control or their average if multiple negative controls we included." >> $summaryfile
echo " </div>" >> $summaryfile
echo "</div>" >> $summaryfile
echo "<br><br><br>" >> $summaryfile
# Run the coverage stats through the machine learning-based QC model to predict the accuracy of the samples given their coverage pattern.
echo Executing QC-bot...
$projectDir/AI/predictAccuracy.py $projectDir/AI/trainedModel.pkl ${coverageFiles[@]}
echo >> $summaryfile
echo "<h2>QC-bot (Experimental)</h2>" >> $summaryfile
echo '<table>' >> $summaryfile
echo "<tr>" >> $summaryfile
echo "<th>QC category</th><th>Subjective definition</th><th>Objective metrics</th>" >> $summaryfile
echo "</tr>" >> $summaryfile
echo "<tr>" >> $summaryfile
echo " <td>A</td><td>No QC issues evident</td><td>0x coordinates <1% <br> 10x coordinates <5% <br> average coverage > 1000X <br> average quality score >35 for Illumina, >15 if ONT, >70 if PacBio HiFi <br> most abundant taxon is coronovirinae</td>" >> $summaryfile
echo "</tr>" >> $summaryfile
echo "<tr>" >> $summaryfile
echo " <td>B</td><td>Some QC issues, but accurate variant calling possible</td><td>0x coordinates <20% <br> 10X coordinates < 40% <br> >80% of diverse SNPs covered <br> average coverage > 100X <br> average quality score >35 for Illumina <br> >15 if ONT, >70 if PacBio HiFi</td>" >> $summaryfile
echo "</tr>" >> $summaryfile
echo "<tr>" >> $summaryfile
echo " <td>C</td><td>Some QC issues, and accurate variant calling impossible</td><td>0x coordinates <99% <br> 10X coordinates <95%</td>" >> $summaryfile
echo "</tr>" >> $summaryfile
echo "<tr>" >> $summaryfile
echo " <td>F</td><td>Significant QC/study design issues</td><td>Contamination (SNR<50) <br> No/negligible coverage (< 1X) <br> Biological/technical replicates' results are irreconcileable.</td>" >> $summaryfile
echo "</tr>" >> $summaryfile
echo '</table>' >> $summaryfile
echo "<br><br><br>" >> $summaryfile
echo '<table>' >> $summaryfile
echo "<tr>" >> $summaryfile
echo "<th>Sample Number</th><th>Suggested category</th><th>Suggested QC flags</th>" >> $summaryfile
echo "</tr>" >> $summaryfile
for (( sample_id=1; sample_id<=${#sampleNames[@]}; sample_id++ )); do
flags=${QC_flags[${sample_id}-1]}
if [[ ${SNRs[${sample_id}-1]} -lt 50 || $flags == *"insufficient_average_coverage"* ]]; then
flags=sample_contamination
category=F
else
if [[ $flags == None ]]; then
category=A
else
if [[ $flags == *"low_average_coverage"* ]]; then
category=C
else
category=B/C
# TODO: Incorporate some machine learning output here
fi
fi
fi
echo "<tr>" >> $summaryfile
echo " <td>${sample_id}</td><td>$category</td><td>$flags</td>" >> $summaryfile
echo "</tr>" >> $summaryfile
done
echo '</table>' >> $summaryfile
echo "<br><br><br>" >> $summaryfile
echo "<div>" >> $summaryfile
echo " <div id=\"figdiv\">" >> $summaryfile
echo " <img src=\"./QC_predictions.png\" alt=\"Num. SC2 reads\" width=\"100%\" class=\"center\">" >> $summaryfile
echo " </div>" >> $summaryfile
echo " <div id=\"figdiv\" style=\"padding-left: 5%; width: 45%;\">" >> $summaryfile
echo " Machine-learning based prediction of the SC2 variant calling accuracy of Freyja of this dataset. The model is a random forest trained on FDA/CFSAN's experimental wastewater WGS data obtained in January 2022 and aims to assess the impact of the potential coverage gaps on the variant abundance estimates. The plotted values represent the predicted deviation of the omicron percentage points from the value that would have been obtained if the coverage was near-complete." >> $summaryfile
echo " </div>" >> $summaryfile
echo "</div>" >> $summaryfile
echo "<br><br><br>" >> $summaryfile
#######################################################
# Place the quality plots, one figure per sample on a nx2 grid.
echo Adding figures...
for ((i = 0; i < ${#sampleNames[@]}; i+=2)); do
echo >> $summaryfile
echo "<span style=\"float:left;\"><a href=\"./${sampleNames[$i]}/${sampleNames[$i]}_report/report.html\">${sampleNames[$i]}</a></span>" >> $summaryfile
echo "<span style=\"float:right;\"><a href=\"./${sampleNames[$(expr $i + 1)]}/${sampleNames[$(expr $i + 1)]}_report/report.html\">" >> $summaryfile
echo "${sampleNames[$(expr $i + 1)]}</a></span><br>" >> $summaryfile
echo "<img src=\"./${sampleNames[$i]}/${sampleNames[$i]}_report/quality.png\" width=\"49%\" class=\"center\">" >> $summaryfile
echo "<img src=\"./${sampleNames[$(expr $i + 1)]}/${sampleNames[$(expr $i + 1)]}_report/quality.png\" width=\"49%\" class=\"center\">" >> $summaryfile
echo "<br><br><br>" >> $summaryfile
done
#######################################################
#echo Appending a list of data analysis parameters...
#echo >> $summaryfile
#echo "<h2>Software configuration</h2>" >> $summaryfile
#pangolinVersion=$(pangolin -v)
#pangolearnVersion=$(pangolin -pv)
#if [[ $platform == "Illumina" ]]; then
# echo "Bowtie2 v)bowtie2 --version | head -n 1 | awk '{ print $3 }'), " >> $summaryfile
#else
# echo "Minimap2:" $(minimap2 --version) >> $summaryfile
#fi
#echo $(samtools --version | head -n 2)", " >> $summaryfile
#echo $(ivar version | head -n 1)", " >> $summaryfile
#echo $(kraken2 -v | head -n 1)", " >> $summaryfile
#echo $(kallisto version)"." >> $summaryfile
#allIncludedLineages=$($projectDir/listVariantsAvail.py $variantDBfile)
#echo Lineage definitions were compiled on $(date +%Y-%m-%d -r $variantDBfile) \
# "from <a href=\"https://github.com/cov-lineages/constellations/tree/main/constellations/definitions\">constellations</a>." >> $summaryfile
#echo Lineage signature file was compiled on $(date +%Y-%m-%d -r $variantDBfile) \
# and includes lineages: $allIncludedLineages. >> $summaryfile
#echo Lineage assignment to the consensus sequence was performed by $pangolinVersion using the classification tree of $pangolearnVersion. >> $summaryfile
echo "</body>" >> $summaryfile
echo "</html>" >> $summaryfile
#######################################################
mkdir analysisResults
shopt -s extglob
mv ./!(analysisResults) ./analysisResults/