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P8: GSE165845 validation scripts (prepare, §16.5 metrics) and cloud task

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scripts/p8/prepare.py lays out the 360 GEO IDATs under their header barcode/position;
scripts/p8/validate_p8.py computes concordance, NRD, no-call rate, per-class and per-MAF
sensitivity/specificity/precision/F1 vs NYGC 30x and GIAB HG001, replicate concordance,
systematic/allele-swap site errors, per-chip batch table and duplicate detection.
Cloud userdata can skip the Omni2.5 archive (OMNI=0).
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kemal committed 2026-09-28 17:43:31 +01:00
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scripts/cloud/tasks/p8.sh
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#!/bin/bash
# Phase P8 scientific validation on GEO GSE165845 (GSA-MD v1, 120 1000 Genomes individuals × 3 replicates):
# 1. fetch IDATs (GEO), GSA v1 C2 build38 BPM/CSV + C1 EGT, GRCh38 FASTA, NYGC 30x per-chromosome VCFs, GIAB HG001;
# 2. `simurg genotype` → `simurg gtc-to-vcf --merge`;
# 3. truth subsets at the array positions (bcftools, split + left-normalised like the array records);
# 4. scripts/p8/validate_p8.py (plan §16.5 metrics).
# Args: <unused data dir> <results dir>. Run with OMNI=0 (the Omni2.5 archive is not needed).
set -uo pipefail
OUT=$2
W=/work/p8; mkdir -p $W $OUT/p8
R=$OUT/p8
N=$(nproc)
export DEBIAN_FRONTEND=noninteractive
apt-get install -y -q bcftools tabix > /dev/null
bcftools --version | head -1
GSA=https://webdata.illumina.com/downloads/productfiles/global-screening-array/v1-0
NYGC=https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20220422_3202_phased_SNV_INDEL_SV
GIAB=https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/NA12878_HG001/NISTv4.2.1/GRCh38/HG001_GRCh38_1_22_v4.2.1_benchmark
CHROMS="$(seq 1 22) X"
echo "=== downloads $(date -u +%T) ==="
mkdir -p $W/nygc $W/gsa $W/giab
( for c in $CHROMS; do
s=""; [[ $c == X ]] && s=".v2"
f=1kGP_high_coverage_Illumina.chr$c.filtered.SNV_INDEL_SV_phased_panel$s.vcf.gz
echo "$NYGC/$f $W/nygc/chr$c.vcf.gz"
done | xargs -P 12 -n 2 sh -c 'curl -sSfL --retry 5 -o "$1" "$0" || echo "!! failed $0"'
echo "NYGC downloaded $(du -sh $W/nygc | cut -f1) at $(date -u +%T)" ) > $W/nygc.log 2>&1 &
NYGC_PID=$!
( for e in vcf.gz vcf.gz.tbi bed; do curl -sSfL --retry 5 -o $W/giab/hg001.$e $GIAB.$e; done ) &
( bash scripts/fetch_data.sh p6 ) > $W/fasta.log 2>&1 &
FASTA_PID=$!
cd $W/gsa
for z in infinium-global-screening-array-24-v1-0-c2-manifest-file-bpm-build38.zip \
infinium-global-screening-array-24-v1-0-c2-manifest-file-csv-build38.zip \
infinium-global-screening-array-v1-0-c1-cluster-file.zip; do
curl -sSfL --retry 5 -O "$GSA/$z"
done
sha256sum -c - <<'EOF' || { echo "!! GSA product file checksum mismatch"; exit 1; }
10ba8e692f6f14f9c50b3bed00d92a96af7ef8aa873df115abc52b5ae7c209b8 infinium-global-screening-array-24-v1-0-c2-manifest-file-bpm-build38.zip
fed9af6de6a8f000b42f44ff1aeecc9fde2a4ae29c834c542bedb2f28879a815 infinium-global-screening-array-24-v1-0-c2-manifest-file-csv-build38.zip
8bd33c286feded0e77b93cacc8533c27dd879a06e97ef9a575dff2037348cb67 infinium-global-screening-array-v1-0-c1-cluster-file.zip
EOF
for z in *.zip; do unzip -oq $z; done
cd - > /dev/null
BPM=$W/gsa/GSA-24v1-0_C2.bpm; CSV=$W/gsa/GSA-24v1-0_C2.csv; EGT=$W/gsa/GSA-24v1-0_C1_ClusterFile.egt
ls -la $W/gsa
/usr/bin/time -v .venv/bin/python scripts/p8/prepare.py --tsv scripts/p8/gse165845_samples.tsv --dl $W/geo \
--out $W/idats --workers 24 2> $R/prepare_time.txt || { tail -30 $R/prepare_time.txt; exit 1; }
cp $W/idats/samples_map.tsv $R/
echo "=== genotype $(date -u +%T) ==="
GT=$W/gt
/usr/bin/time -v .venv/bin/simurg genotype --bpm $BPM --egt $EGT --idats $W/idats --out $GT \
--threads "$N" --quiet 2> $R/genotype_time.txt || echo "genotype exit $?"
tail -22 $R/genotype_time.txt
cp $GT/sample_summary.tsv $GT/relatedness.tsv $GT/controls_qc.tsv $GT/run_manifest.json $R/ 2>/dev/null
cp -r $GT/logs $R/genotype_logs 2>/dev/null
wait $FASTA_PID; tail -2 $W/fasta.log
FA=data/p6/GRCh38.primary.fa
echo "=== gtc-to-vcf $(date -u +%T) ==="
/usr/bin/time -v .venv/bin/simurg gtc-to-vcf --bpm $BPM --csv $CSV --fasta $FA --gtcs $GT --out $W/vcf --merge \
--threads "$N" --quiet 2> $R/vcf_time.txt || echo "gtc-to-vcf exit $?"
tail -22 $R/vcf_time.txt
cp $W/vcf/run_manifest.json $R/vcf_run_manifest.json; cp -r $W/vcf/logs $R/vcf_logs 2>/dev/null
ARR=$W/vcf/cohort.snv.bcf
[[ -f $ARR ]] || { echo "!! no cohort BCF"; exit 1; }
echo "=== truth subsets $(date -u +%T) ==="
for c in $(seq 1 22) X Y M; do echo -e "chr$c\t$c"; done > $W/chrmap.txt
cut -f3 $W/idats/samples_map.tsv | tail -n +2 | sort -u | paste -sd, > $W/inds.txt
bcftools query -f '%CHROM\t%POS\n' $ARR | awk '{print "chr"$1"\t"$2}' | uniq > $W/sites_chr.tsv
wc -l < $W/sites_chr.tsv
bcftools norm -f $FA -m-any --check-ref w -Ob -o $W/array.norm.bcf $ARR 2> $R/norm_array.txt
wait $NYGC_PID; cat $W/nygc.log; df -h /work | tail -1
export W FA
echo $CHROMS | tr ' ' '\n' | xargs -P "$N" -I{} sh -c \
'bcftools view -T $W/sites_chr.tsv -s $(cat $W/inds.txt) --force-samples -Ou $W/nygc/chr{}.vcf.gz \
| bcftools annotate --rename-chrs $W/chrmap.txt -Ou \
| bcftools norm -f $FA -m-any --check-ref w -Ob -o $W/truth.{}.bcf 2> $W/truth.{}.log || echo "!! truth chr{} failed"'
bcftools concat -Ob -o $W/truth.bcf $(for c in $CHROMS; do echo $W/truth.$c.bcf; done)
bcftools view -T $W/sites_chr.tsv -Ou $W/giab/hg001.vcf.gz | bcftools annotate --rename-chrs $W/chrmap.txt -Ou \
| bcftools norm -f $FA -m-any --check-ref w -Ob -o $W/giab.bcf
echo "array $(bcftools view -H $W/array.norm.bcf | wc -l) records, truth $(bcftools view -H $W/truth.bcf | wc -l)," \
"giab $(bcftools view -H $W/giab.bcf | wc -l)"
echo "=== validate $(date -u +%T) ==="
/usr/bin/time -v .venv/bin/python scripts/p8/validate_p8.py --array $W/array.norm.bcf --truth $W/truth.bcf \
--giab $W/giab.bcf --giab-bed $W/giab/hg001.bed --map $W/idats/samples_map.tsv --gt-dir $GT --out $R \
2> $R/validate_time.txt || echo "validation exit $?"
tail -22 $R/validate_time.txt
gzip -f $R/replicate_pairs.tsv $R/systematic_sites.tsv 2>/dev/null
echo "=== task done $(date -u +%T) ==="
scripts/cloud/trial.sh
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git archive --format=tar.gz -o /tmp/simurg-repo.tar.gz HEAD git archive --format=tar.gz -o /tmp/simurg-repo.tar.gz HEAD
$AWS s3 cp /tmp/simurg-repo.tar.gz "s3://$BUCKET/$RUN/repo.tar.gz" --only-show-errors $AWS s3 cp /tmp/simurg-repo.tar.gz "s3://$BUCKET/$RUN/repo.tar.gz" --only-show-errors
[[ -f scripts/cloud/tasks/$TASK.sh ]] || { echo "unknown TASK $TASK"; exit 2; } [[ -f scripts/cloud/tasks/$TASK.sh ]] || { echo "unknown TASK $TASK"; exit 2; }
OMNI=${OMNI:-1} # 0: the task does not need the 107 GB Omni2.5 archive
sed -e "s/__BUCKET__/$BUCKET/" -e "s/__RUN__/$RUN/" -e "s/__MAX_MINUTES__/$MAX_MINUTES/" -e "s/__TASK__/$TASK/g" \ sed -e "s/__BUCKET__/$BUCKET/" -e "s/__RUN__/$RUN/" -e "s/__MAX_MINUTES__/$MAX_MINUTES/" -e "s/__TASK__/$TASK/g" \
-e "s/__OMNI__/$OMNI/" \
scripts/cloud/userdata.sh > /tmp/simurg-userdata.sh scripts/cloud/userdata.sh > /tmp/simurg-userdata.sh
AMI=$($AWS ssm get-parameter --name /aws/service/canonical/ubuntu/server/24.04/stable/current/arm64/hvm/ebs-gp3/ami-id \ AMI=$($AWS ssm get-parameter --name /aws/service/canonical/ubuntu/server/24.04/stable/current/arm64/hvm/ebs-gp3/ami-id \
--query Parameter.Value --output text) --query Parameter.Value --output text)
scripts/cloud/userdata.sh
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SRC=s3://1000genomes/release/20130502/supporting/hd_genotype_chip/broad_intensities SRC=s3://1000genomes/release/20130502/supporting/hd_genotype_chip/broad_intensities
D=/work/omni && mkdir -p $D/data D=/work/omni && mkdir -p $D/data
for f in HumanOmni2.5-4v1-Multi_B.bpm HumanOmni2.5-4v1-Multi_B.egt HumanOmni2.5-4v1_B.csv; do if [[ "__OMNI__" == "1" ]]; then
$S3 cp --no-sign-request "$SRC/$f" $D/ --only-show-errors for f in HumanOmni2.5-4v1-Multi_B.bpm HumanOmni2.5-4v1-Multi_B.egt HumanOmni2.5-4v1_B.csv; do
done $S3 cp --no-sign-request "$SRC/$f" $D/ --only-show-errors
echo "=== streaming 107 GB IDAT+GTC archive (no copy of the .tgz is kept) $(date -u +%T) ===" done
$S3 cp --no-sign-request "$SRC/Omni25_idats_gtcs_2141_samples.tgz" - | pigz -dc | tar -x -C $D/data echo "=== streaming 107 GB IDAT+GTC archive (no copy of the .tgz is kept) $(date -u +%T) ==="
echo "=== extracted $(du -sh $D/data | cut -f1) at $(date -u +%T) ===" $S3 cp --no-sign-request "$SRC/Omni25_idats_gtcs_2141_samples.tgz" - | pigz -dc | tar -x -C $D/data
echo "=== extracted $(du -sh $D/data | cut -f1) at $(date -u +%T) ==="
fi
echo "=== unit tests $(date -u +%T) ===" echo "=== unit tests $(date -u +%T) ==="
.venv/bin/python -m pytest tests/unit -q -p no:cacheprovider || echo "unit tests reported failures (continuing)" .venv/bin/python -m pytest tests/unit -q -p no:cacheprovider || echo "unit tests reported failures (continuing)"
scripts/p8/gse165845_samples.tsv
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gsm individual replicate grn_url red_url
GSM5050990 HG00096 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050990/suppl/GSM5050990_HG00096_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050990/suppl/GSM5050990_HG00096_1_Red.idat.gz
GSM5050991 HG00096 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050991/suppl/GSM5050991_HG00096_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050991/suppl/GSM5050991_HG00096_2_Red.idat.gz
GSM5050992 HG00096 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050992/suppl/GSM5050992_HG00096_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050992/suppl/GSM5050992_HG00096_3_Red.idat.gz
GSM5050993 HG00101 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050993/suppl/GSM5050993_HG00101_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050993/suppl/GSM5050993_HG00101_1_Red.idat.gz
GSM5050994 HG00101 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050994/suppl/GSM5050994_HG00101_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050994/suppl/GSM5050994_HG00101_2_Red.idat.gz
GSM5050995 HG00101 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050995/suppl/GSM5050995_HG00101_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050995/suppl/GSM5050995_HG00101_3_Red.idat.gz
GSM5050996 HG00102 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050996/suppl/GSM5050996_HG00102_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050996/suppl/GSM5050996_HG00102_1_Red.idat.gz
GSM5050997 HG00102 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050997/suppl/GSM5050997_HG00102_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050997/suppl/GSM5050997_HG00102_2_Red.idat.gz
GSM5050998 HG00102 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050998/suppl/GSM5050998_HG00102_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050998/suppl/GSM5050998_HG00102_3_Red.idat.gz
GSM5050999 HG00105 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050999/suppl/GSM5050999_HG00105_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050999/suppl/GSM5050999_HG00105_1_Red.idat.gz
GSM5051000 HG00105 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051000/suppl/GSM5051000_HG00105_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051000/suppl/GSM5051000_HG00105_2_Red.idat.gz
GSM5051001 HG00105 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051001/suppl/GSM5051001_HG00105_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051001/suppl/GSM5051001_HG00105_3_Red.idat.gz
GSM5051002 HG00107 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051002/suppl/GSM5051002_HG00107_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051002/suppl/GSM5051002_HG00107_1_Red.idat.gz
GSM5051003 HG00107 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051003/suppl/GSM5051003_HG00107_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051003/suppl/GSM5051003_HG00107_2_Red.idat.gz
GSM5051004 HG00107 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051004/suppl/GSM5051004_HG00107_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051004/suppl/GSM5051004_HG00107_3_Red.idat.gz
GSM5051005 HG00108 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051005/suppl/GSM5051005_HG00108_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051005/suppl/GSM5051005_HG00108_1_Red.idat.gz
GSM5051006 HG00108 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051006/suppl/GSM5051006_HG00108_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051006/suppl/GSM5051006_HG00108_2_Red.idat.gz
GSM5051007 HG00108 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051007/suppl/GSM5051007_HG00108_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051007/suppl/GSM5051007_HG00108_3_Red.idat.gz
GSM5051008 HG00110 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051008/suppl/GSM5051008_HG00110_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051008/suppl/GSM5051008_HG00110_1_Red.idat.gz
GSM5051009 HG00110 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051009/suppl/GSM5051009_HG00110_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051009/suppl/GSM5051009_HG00110_2_Red.idat.gz
GSM5051010 HG00110 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051010/suppl/GSM5051010_HG00110_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051010/suppl/GSM5051010_HG00110_3_Red.idat.gz
GSM5051011 HG00111 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051011/suppl/GSM5051011_HG00111_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051011/suppl/GSM5051011_HG00111_1_Red.idat.gz
GSM5051012 HG00111 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051012/suppl/GSM5051012_HG00111_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051012/suppl/GSM5051012_HG00111_2_Red.idat.gz
GSM5051013 HG00111 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051013/suppl/GSM5051013_HG00111_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051013/suppl/GSM5051013_HG00111_3_Red.idat.gz
GSM5051014 HG00116 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051014/suppl/GSM5051014_HG00116_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051014/suppl/GSM5051014_HG00116_1_Red.idat.gz
GSM5051015 HG00116 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051015/suppl/GSM5051015_HG00116_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051015/suppl/GSM5051015_HG00116_2_Red.idat.gz
GSM5051016 HG00116 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051016/suppl/GSM5051016_HG00116_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051016/suppl/GSM5051016_HG00116_3_Red.idat.gz
GSM5051017 HG00119 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051017/suppl/GSM5051017_HG00119_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051017/suppl/GSM5051017_HG00119_1_Red.idat.gz
GSM5051018 HG00119 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051018/suppl/GSM5051018_HG00119_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051018/suppl/GSM5051018_HG00119_2_Red.idat.gz
GSM5051019 HG00119 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051019/suppl/GSM5051019_HG00119_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051019/suppl/GSM5051019_HG00119_3_Red.idat.gz
GSM5051020 HG00131 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051020/suppl/GSM5051020_HG00131_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051020/suppl/GSM5051020_HG00131_1_Red.idat.gz
GSM5051021 HG00131 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051021/suppl/GSM5051021_HG00131_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051021/suppl/GSM5051021_HG00131_2_Red.idat.gz
GSM5051022 HG00131 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051022/suppl/GSM5051022_HG00131_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051022/suppl/GSM5051022_HG00131_3_Red.idat.gz
GSM5051023 HG00132 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051023/suppl/GSM5051023_HG00132_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051023/suppl/GSM5051023_HG00132_1_Red.idat.gz
GSM5051024 HG00132 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051024/suppl/GSM5051024_HG00132_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051024/suppl/GSM5051024_HG00132_2_Red.idat.gz
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GSM5051313 NA20412 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051313/suppl/GSM5051313_NA20412_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051313/suppl/GSM5051313_NA20412_3_Red.idat.gz
GSM5051314 NA20502 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051314/suppl/GSM5051314_NA20502_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051314/suppl/GSM5051314_NA20502_1_Red.idat.gz
GSM5051315 NA20502 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051315/suppl/GSM5051315_NA20502_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051315/suppl/GSM5051315_NA20502_2_Red.idat.gz
GSM5051316 NA20502 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051316/suppl/GSM5051316_NA20502_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051316/suppl/GSM5051316_NA20502_3_Red.idat.gz
GSM5051317 NA20503 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051317/suppl/GSM5051317_NA20503_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051317/suppl/GSM5051317_NA20503_1_Red.idat.gz
GSM5051318 NA20503 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051318/suppl/GSM5051318_NA20503_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051318/suppl/GSM5051318_NA20503_2_Red.idat.gz
GSM5051319 NA20503 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051319/suppl/GSM5051319_NA20503_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051319/suppl/GSM5051319_NA20503_3_Red.idat.gz
GSM5051320 NA20507 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051320/suppl/GSM5051320_NA20507_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051320/suppl/GSM5051320_NA20507_1_Red.idat.gz
GSM5051321 NA20507 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051321/suppl/GSM5051321_NA20507_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051321/suppl/GSM5051321_NA20507_2_Red.idat.gz
GSM5051322 NA20507 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051322/suppl/GSM5051322_NA20507_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051322/suppl/GSM5051322_NA20507_3_Red.idat.gz
GSM5051323 NA20513 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051323/suppl/GSM5051323_NA20513_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051323/suppl/GSM5051323_NA20513_1_Red.idat.gz
GSM5051324 NA20513 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051324/suppl/GSM5051324_NA20513_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051324/suppl/GSM5051324_NA20513_2_Red.idat.gz
GSM5051325 NA20513 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051325/suppl/GSM5051325_NA20513_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051325/suppl/GSM5051325_NA20513_3_Red.idat.gz
GSM5051326 NA20514 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051326/suppl/GSM5051326_NA20514_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051326/suppl/GSM5051326_NA20514_1_Red.idat.gz
GSM5051327 NA20514 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051327/suppl/GSM5051327_NA20514_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051327/suppl/GSM5051327_NA20514_2_Red.idat.gz
GSM5051328 NA20514 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051328/suppl/GSM5051328_NA20514_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051328/suppl/GSM5051328_NA20514_3_Red.idat.gz
GSM5051329 NA20798 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051329/suppl/GSM5051329_NA20798_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051329/suppl/GSM5051329_NA20798_1_Red.idat.gz
GSM5051330 NA20798 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051330/suppl/GSM5051330_NA20798_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051330/suppl/GSM5051330_NA20798_2_Red.idat.gz
GSM5051331 NA20798 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051331/suppl/GSM5051331_NA20798_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051331/suppl/GSM5051331_NA20798_3_Red.idat.gz
GSM5051332 NA20802 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051332/suppl/GSM5051332_NA20802_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051332/suppl/GSM5051332_NA20802_1_Red.idat.gz
GSM5051333 NA20802 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051333/suppl/GSM5051333_NA20802_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051333/suppl/GSM5051333_NA20802_2_Red.idat.gz
GSM5051334 NA20802 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051334/suppl/GSM5051334_NA20802_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051334/suppl/GSM5051334_NA20802_3_Red.idat.gz
GSM5051335 NA20803 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051335/suppl/GSM5051335_NA20803_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051335/suppl/GSM5051335_NA20803_1_Red.idat.gz
GSM5051336 NA20803 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051336/suppl/GSM5051336_NA20803_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051336/suppl/GSM5051336_NA20803_2_Red.idat.gz
GSM5051337 NA20803 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051337/suppl/GSM5051337_NA20803_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051337/suppl/GSM5051337_NA20803_3_Red.idat.gz
GSM5051338 NA20804 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051338/suppl/GSM5051338_NA20804_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051338/suppl/GSM5051338_NA20804_1_Red.idat.gz
GSM5051339 NA20804 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051339/suppl/GSM5051339_NA20804_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051339/suppl/GSM5051339_NA20804_2_Red.idat.gz
GSM5051340 NA20804 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051340/suppl/GSM5051340_NA20804_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051340/suppl/GSM5051340_NA20804_3_Red.idat.gz
GSM5051341 NA20805 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051341/suppl/GSM5051341_NA20805_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051341/suppl/GSM5051341_NA20805_1_Red.idat.gz
GSM5051342 NA20805 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051342/suppl/GSM5051342_NA20805_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051342/suppl/GSM5051342_NA20805_2_Red.idat.gz
GSM5051343 NA20805 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051343/suppl/GSM5051343_NA20805_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051343/suppl/GSM5051343_NA20805_3_Red.idat.gz
GSM5051344 NA20821 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051344/suppl/GSM5051344_NA20821_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051344/suppl/GSM5051344_NA20821_1_Red.idat.gz
GSM5051345 NA20821 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051345/suppl/GSM5051345_NA20821_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051345/suppl/GSM5051345_NA20821_2_Red.idat.gz
GSM5051346 NA20821 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051346/suppl/GSM5051346_NA20821_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051346/suppl/GSM5051346_NA20821_3_Red.idat.gz
GSM5051347 NA20822 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051347/suppl/GSM5051347_NA20822_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051347/suppl/GSM5051347_NA20822_1_Red.idat.gz
GSM5051348 NA20822 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051348/suppl/GSM5051348_NA20822_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051348/suppl/GSM5051348_NA20822_2_Red.idat.gz
GSM5051349 NA20822 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051349/suppl/GSM5051349_NA20822_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051349/suppl/GSM5051349_NA20822_3_Red.idat.gz
1 gsm individual replicate grn_url red_url
2 GSM5050990 HG00096 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050990/suppl/GSM5050990_HG00096_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050990/suppl/GSM5050990_HG00096_1_Red.idat.gz
3 GSM5050991 HG00096 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050991/suppl/GSM5050991_HG00096_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050991/suppl/GSM5050991_HG00096_2_Red.idat.gz
4 GSM5050992 HG00096 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050992/suppl/GSM5050992_HG00096_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050992/suppl/GSM5050992_HG00096_3_Red.idat.gz
5 GSM5050993 HG00101 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050993/suppl/GSM5050993_HG00101_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050993/suppl/GSM5050993_HG00101_1_Red.idat.gz
6 GSM5050994 HG00101 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050994/suppl/GSM5050994_HG00101_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050994/suppl/GSM5050994_HG00101_2_Red.idat.gz
7 GSM5050995 HG00101 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050995/suppl/GSM5050995_HG00101_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050995/suppl/GSM5050995_HG00101_3_Red.idat.gz
8 GSM5050996 HG00102 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050996/suppl/GSM5050996_HG00102_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050996/suppl/GSM5050996_HG00102_1_Red.idat.gz
9 GSM5050997 HG00102 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050997/suppl/GSM5050997_HG00102_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050997/suppl/GSM5050997_HG00102_2_Red.idat.gz
10 GSM5050998 HG00102 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050998/suppl/GSM5050998_HG00102_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050998/suppl/GSM5050998_HG00102_3_Red.idat.gz
11 GSM5050999 HG00105 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050999/suppl/GSM5050999_HG00105_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5050nnn/GSM5050999/suppl/GSM5050999_HG00105_1_Red.idat.gz
12 GSM5051000 HG00105 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051000/suppl/GSM5051000_HG00105_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051000/suppl/GSM5051000_HG00105_2_Red.idat.gz
13 GSM5051001 HG00105 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051001/suppl/GSM5051001_HG00105_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051001/suppl/GSM5051001_HG00105_3_Red.idat.gz
14 GSM5051002 HG00107 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051002/suppl/GSM5051002_HG00107_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051002/suppl/GSM5051002_HG00107_1_Red.idat.gz
15 GSM5051003 HG00107 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051003/suppl/GSM5051003_HG00107_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051003/suppl/GSM5051003_HG00107_2_Red.idat.gz
16 GSM5051004 HG00107 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051004/suppl/GSM5051004_HG00107_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051004/suppl/GSM5051004_HG00107_3_Red.idat.gz
17 GSM5051005 HG00108 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051005/suppl/GSM5051005_HG00108_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051005/suppl/GSM5051005_HG00108_1_Red.idat.gz
18 GSM5051006 HG00108 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051006/suppl/GSM5051006_HG00108_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051006/suppl/GSM5051006_HG00108_2_Red.idat.gz
19 GSM5051007 HG00108 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051007/suppl/GSM5051007_HG00108_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051007/suppl/GSM5051007_HG00108_3_Red.idat.gz
20 GSM5051008 HG00110 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051008/suppl/GSM5051008_HG00110_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051008/suppl/GSM5051008_HG00110_1_Red.idat.gz
21 GSM5051009 HG00110 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051009/suppl/GSM5051009_HG00110_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051009/suppl/GSM5051009_HG00110_2_Red.idat.gz
22 GSM5051010 HG00110 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051010/suppl/GSM5051010_HG00110_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051010/suppl/GSM5051010_HG00110_3_Red.idat.gz
23 GSM5051011 HG00111 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051011/suppl/GSM5051011_HG00111_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051011/suppl/GSM5051011_HG00111_1_Red.idat.gz
24 GSM5051012 HG00111 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051012/suppl/GSM5051012_HG00111_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051012/suppl/GSM5051012_HG00111_2_Red.idat.gz
25 GSM5051013 HG00111 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051013/suppl/GSM5051013_HG00111_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051013/suppl/GSM5051013_HG00111_3_Red.idat.gz
26 GSM5051014 HG00116 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051014/suppl/GSM5051014_HG00116_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051014/suppl/GSM5051014_HG00116_1_Red.idat.gz
27 GSM5051015 HG00116 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051015/suppl/GSM5051015_HG00116_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051015/suppl/GSM5051015_HG00116_2_Red.idat.gz
28 GSM5051016 HG00116 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051016/suppl/GSM5051016_HG00116_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051016/suppl/GSM5051016_HG00116_3_Red.idat.gz
29 GSM5051017 HG00119 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051017/suppl/GSM5051017_HG00119_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051017/suppl/GSM5051017_HG00119_1_Red.idat.gz
30 GSM5051018 HG00119 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051018/suppl/GSM5051018_HG00119_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051018/suppl/GSM5051018_HG00119_2_Red.idat.gz
31 GSM5051019 HG00119 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051019/suppl/GSM5051019_HG00119_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051019/suppl/GSM5051019_HG00119_3_Red.idat.gz
32 GSM5051020 HG00131 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051020/suppl/GSM5051020_HG00131_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051020/suppl/GSM5051020_HG00131_1_Red.idat.gz
33 GSM5051021 HG00131 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051021/suppl/GSM5051021_HG00131_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051021/suppl/GSM5051021_HG00131_2_Red.idat.gz
34 GSM5051022 HG00131 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051022/suppl/GSM5051022_HG00131_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051022/suppl/GSM5051022_HG00131_3_Red.idat.gz
35 GSM5051023 HG00132 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051023/suppl/GSM5051023_HG00132_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051023/suppl/GSM5051023_HG00132_1_Red.idat.gz
36 GSM5051024 HG00132 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051024/suppl/GSM5051024_HG00132_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051024/suppl/GSM5051024_HG00132_2_Red.idat.gz
37 GSM5051025 HG00132 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051025/suppl/GSM5051025_HG00132_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051025/suppl/GSM5051025_HG00132_3_Red.idat.gz
38 GSM5051026 HG00145 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051026/suppl/GSM5051026_HG00145_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051026/suppl/GSM5051026_HG00145_1_Red.idat.gz
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323 GSM5051311 NA20412 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051311/suppl/GSM5051311_NA20412_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051311/suppl/GSM5051311_NA20412_1_Red.idat.gz
324 GSM5051312 NA20412 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051312/suppl/GSM5051312_NA20412_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051312/suppl/GSM5051312_NA20412_2_Red.idat.gz
325 GSM5051313 NA20412 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051313/suppl/GSM5051313_NA20412_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051313/suppl/GSM5051313_NA20412_3_Red.idat.gz
326 GSM5051314 NA20502 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051314/suppl/GSM5051314_NA20502_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051314/suppl/GSM5051314_NA20502_1_Red.idat.gz
327 GSM5051315 NA20502 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051315/suppl/GSM5051315_NA20502_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051315/suppl/GSM5051315_NA20502_2_Red.idat.gz
328 GSM5051316 NA20502 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051316/suppl/GSM5051316_NA20502_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051316/suppl/GSM5051316_NA20502_3_Red.idat.gz
329 GSM5051317 NA20503 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051317/suppl/GSM5051317_NA20503_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051317/suppl/GSM5051317_NA20503_1_Red.idat.gz
330 GSM5051318 NA20503 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051318/suppl/GSM5051318_NA20503_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051318/suppl/GSM5051318_NA20503_2_Red.idat.gz
331 GSM5051319 NA20503 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051319/suppl/GSM5051319_NA20503_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051319/suppl/GSM5051319_NA20503_3_Red.idat.gz
332 GSM5051320 NA20507 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051320/suppl/GSM5051320_NA20507_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051320/suppl/GSM5051320_NA20507_1_Red.idat.gz
333 GSM5051321 NA20507 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051321/suppl/GSM5051321_NA20507_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051321/suppl/GSM5051321_NA20507_2_Red.idat.gz
334 GSM5051322 NA20507 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051322/suppl/GSM5051322_NA20507_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051322/suppl/GSM5051322_NA20507_3_Red.idat.gz
335 GSM5051323 NA20513 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051323/suppl/GSM5051323_NA20513_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051323/suppl/GSM5051323_NA20513_1_Red.idat.gz
336 GSM5051324 NA20513 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051324/suppl/GSM5051324_NA20513_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051324/suppl/GSM5051324_NA20513_2_Red.idat.gz
337 GSM5051325 NA20513 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051325/suppl/GSM5051325_NA20513_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051325/suppl/GSM5051325_NA20513_3_Red.idat.gz
338 GSM5051326 NA20514 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051326/suppl/GSM5051326_NA20514_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051326/suppl/GSM5051326_NA20514_1_Red.idat.gz
339 GSM5051327 NA20514 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051327/suppl/GSM5051327_NA20514_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051327/suppl/GSM5051327_NA20514_2_Red.idat.gz
340 GSM5051328 NA20514 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051328/suppl/GSM5051328_NA20514_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051328/suppl/GSM5051328_NA20514_3_Red.idat.gz
341 GSM5051329 NA20798 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051329/suppl/GSM5051329_NA20798_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051329/suppl/GSM5051329_NA20798_1_Red.idat.gz
342 GSM5051330 NA20798 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051330/suppl/GSM5051330_NA20798_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051330/suppl/GSM5051330_NA20798_2_Red.idat.gz
343 GSM5051331 NA20798 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051331/suppl/GSM5051331_NA20798_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051331/suppl/GSM5051331_NA20798_3_Red.idat.gz
344 GSM5051332 NA20802 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051332/suppl/GSM5051332_NA20802_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051332/suppl/GSM5051332_NA20802_1_Red.idat.gz
345 GSM5051333 NA20802 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051333/suppl/GSM5051333_NA20802_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051333/suppl/GSM5051333_NA20802_2_Red.idat.gz
346 GSM5051334 NA20802 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051334/suppl/GSM5051334_NA20802_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051334/suppl/GSM5051334_NA20802_3_Red.idat.gz
347 GSM5051335 NA20803 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051335/suppl/GSM5051335_NA20803_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051335/suppl/GSM5051335_NA20803_1_Red.idat.gz
348 GSM5051336 NA20803 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051336/suppl/GSM5051336_NA20803_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051336/suppl/GSM5051336_NA20803_2_Red.idat.gz
349 GSM5051337 NA20803 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051337/suppl/GSM5051337_NA20803_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051337/suppl/GSM5051337_NA20803_3_Red.idat.gz
350 GSM5051338 NA20804 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051338/suppl/GSM5051338_NA20804_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051338/suppl/GSM5051338_NA20804_1_Red.idat.gz
351 GSM5051339 NA20804 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051339/suppl/GSM5051339_NA20804_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051339/suppl/GSM5051339_NA20804_2_Red.idat.gz
352 GSM5051340 NA20804 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051340/suppl/GSM5051340_NA20804_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051340/suppl/GSM5051340_NA20804_3_Red.idat.gz
353 GSM5051341 NA20805 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051341/suppl/GSM5051341_NA20805_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051341/suppl/GSM5051341_NA20805_1_Red.idat.gz
354 GSM5051342 NA20805 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051342/suppl/GSM5051342_NA20805_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051342/suppl/GSM5051342_NA20805_2_Red.idat.gz
355 GSM5051343 NA20805 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051343/suppl/GSM5051343_NA20805_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051343/suppl/GSM5051343_NA20805_3_Red.idat.gz
356 GSM5051344 NA20821 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051344/suppl/GSM5051344_NA20821_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051344/suppl/GSM5051344_NA20821_1_Red.idat.gz
357 GSM5051345 NA20821 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051345/suppl/GSM5051345_NA20821_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051345/suppl/GSM5051345_NA20821_2_Red.idat.gz
358 GSM5051346 NA20821 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051346/suppl/GSM5051346_NA20821_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051346/suppl/GSM5051346_NA20821_3_Red.idat.gz
359 GSM5051347 NA20822 1 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051347/suppl/GSM5051347_NA20822_1_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051347/suppl/GSM5051347_NA20822_1_Red.idat.gz
360 GSM5051348 NA20822 2 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051348/suppl/GSM5051348_NA20822_2_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051348/suppl/GSM5051348_NA20822_2_Red.idat.gz
361 GSM5051349 NA20822 3 https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051349/suppl/GSM5051349_NA20822_3_Grn.idat.gz https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5051nnn/GSM5051349/suppl/GSM5051349_NA20822_3_Red.idat.gz
scripts/p8/prepare.py
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#!/usr/bin/env python3
"""P8: fetch the GSE165845 IDATs (120 1000 Genomes individuals × 3 replicates, GSA-MD v1) and lay them out as
``<barcode>/<barcode>_<RxxCxx>_{Grn,Red}.idat.gz`` symlinks, reading barcode and position from each IDAT header
(the GEO file names are ``GSM…_<individual>_<replicate>_{Grn,Red}.idat.gz``).
Writes ``<out>/samples_map.tsv`` (sample_id, gsm, individual, replicate).
python scripts/p8/prepare.py --tsv scripts/p8/gse165845_samples.tsv --dl DIR --out DIR [--only NA12878,HG00096]
"""
from __future__ import annotations
import argparse
import csv
import sys
import time
import urllib.request
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from simurg.formats.idat import read_idat
def fetch(url: str, dest: Path, tries: int = 5) -> Path:
if dest.exists() and dest.stat().st_size > 0:
return dest
tmp = dest.with_suffix(dest.suffix + ".part")
for i in range(tries):
try:
with urllib.request.urlopen(url, timeout=120) as r, open(tmp, "wb") as fh:
while chunk := r.read(1 << 20):
fh.write(chunk)
tmp.rename(dest)
return dest
except OSError as e:
print(f"retry {i + 1}/{tries} {url}: {e}", file=sys.stderr)
time.sleep(5 * (i + 1))
raise RuntimeError(f"download failed: {url}")
def main() -> int:
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--tsv", required=True, type=Path)
ap.add_argument("--dl", required=True, type=Path, help="download folder (GEO names)")
ap.add_argument("--out", required=True, type=Path, help="folder for the standard-name symlink tree")
ap.add_argument("--only", default="", help="comma-separated individuals (test subsets)")
ap.add_argument("--workers", type=int, default=16)
a = ap.parse_args()
rows = list(csv.DictReader(a.tsv.open(), delimiter="\t"))
if a.only:
keep = set(a.only.split(","))
rows = [r for r in rows if r["individual"] in keep]
a.dl.mkdir(parents=True, exist_ok=True)
jobs = [(r[k], a.dl / r[k].rsplit("/", 1)[1]) for r in rows for k in ("grn_url", "red_url")]
t0 = time.time()
with ThreadPoolExecutor(a.workers) as ex:
list(ex.map(lambda j: fetch(*j), jobs))
size = sum(p.stat().st_size for _, p in jobs)
print(f"{len(jobs)} IDATs, {size / 1e9:.1f} GB in {time.time() - t0:.0f} s")
a.out.mkdir(parents=True, exist_ok=True)
seen: dict[str, str] = {}
with open(a.out / "samples_map.tsv", "w") as fh:
fh.write("sample_id\tgsm\tindividual\treplicate\n")
for r in rows:
names = []
for ch, key in (("Grn", "grn_url"), ("Red", "red_url")):
src = a.dl / r[key].rsplit("/", 1)[1]
idat = read_idat(src, load_arrays=False)
names.append(f"{idat.barcode}_{idat.position}")
d = a.out / idat.barcode
d.mkdir(exist_ok=True)
link = d / f"{idat.barcode}_{idat.position}_{ch}.idat.gz"
if not link.exists():
link.symlink_to(src.resolve())
if names[0] != names[1]:
raise SystemExit(f"{r['gsm']}: Grn/Red headers disagree ({names})")
sid = names[0]
if sid in seen:
raise SystemExit(f"{r['gsm']}: {sid} already used by {seen[sid]}")
seen[sid] = r["gsm"]
fh.write(f"{sid}\t{r['gsm']}\t{r['individual']}\t{r['replicate']}\n")
print(f"{len(seen)} samples -> {a.out}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
scripts/p8/validate_p8.py
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#!/usr/bin/env python3
"""P8 scientific validation (plan §16.5) of SimurgArray calls on GSE165845 (GSA-MD v1, 120 1000 Genomes
individuals × 3 replicates) against the NYGC 30x WGS call set and GIAB HG001 v4.2.1.
Inputs are prepared by ``scripts/cloud/tasks/p8.sh``: every VCF/BCF is already split to biallelic records and
left-normalised against the same GRCh38 FASTA with Ensembl contig names (1…22, X):
--array cohort BCF from ``simurg gtc-to-vcf --merge`` (samples = <barcode>_<position>)
--truth NYGC records at the array positions, subset to the GSE165845 individuals
--giab GIAB HG001 records at the array positions; --giab-bed the v4.2.1 confident regions
--map samples_map.tsv from ``prepare.py``; --gt-dir the ``simurg genotype`` output folder
Metrics: genotype concordance, non-reference discordance (NRD), no-call rate, sensitivity / specificity /
precision / F1 per genotype class and per truth MAF bin, SNV vs indel; replicate concordance; systematic
site discordance with the allele-swap (strand/allele error) pattern; per-chip call rate and LogR deviation;
duplicate detection by the cohort relatedness step. A site absent from a WGS call set is taken as homozygous
reference (reported separately from sites present in both).
"""
from __future__ import annotations
import argparse
import csv
import json
import statistics
import subprocess
import sys
import time
from collections import defaultdict
from itertools import combinations
from pathlib import Path
import numpy as np
import numpy.typing as npt
CHROMS = [str(i) for i in range(1, 23)] + ["X"]
GT_CODE = {
"0/0": 0, "0|0": 0, "0": 0,
"0/1": 1, "1/0": 1, "0|1": 1, "1|0": 1,
"1/1": 2, "1|1": 2, "1": 2,
} # fmt: skip
MAF_BINS = [0.0, 0.001, 0.01, 0.05, 0.1, 0.2, 0.5000001]
MAF_LABELS = ["<0.1%", "0.1-1%", "1-5%", "5-10%", "10-20%", "20-50%"]
CLASSES = ["hom_ref", "het", "hom_alt"]
def query(bcftools: str, path: Path, fmt: str) -> tuple[list[str], subprocess.Popen[str]]:
samples = subprocess.run([bcftools, "query", "-l", str(path)], capture_output=True, text=True, check=True)
p = subprocess.Popen([bcftools, "query", "-f", fmt, str(path)], stdout=subprocess.PIPE, text=True, bufsize=1 << 20)
return samples.stdout.split(), p
def load(bcftools: str, path: Path, with_af: bool = False, chroms: list[str] | None = None) -> dict:
"""Records of chromosomes 1–22/X → keys, genotype matrix (int8, −1 = no call), AF (if asked)."""
fmt = "%CHROM\t%POS\t%REF\t%ALT" + ("\t%INFO/AF" if with_af else "") + "[\t%GT]\n"
samples, p = query(bcftools, path, fmt)
nfix = 5 if with_af else 4
keys: list[tuple[str, int, str, str]] = []
rows: list[npt.NDArray[np.int8]] = []
af: list[float] = []
index: dict[tuple[str, int, str, str], int] = {}
positions: set[tuple[str, int]] = set()
dups = 0
ok_chrom = set(chroms or CHROMS)
get = GT_CODE.get
assert p.stdout is not None
for line in p.stdout:
f = line.rstrip("\n").split("\t")
if f[0] not in ok_chrom:
continue
key = (f[0], int(f[1]), f[2], f[3])
if key in index:
dups += 1
continue
index[key] = len(keys)
keys.append(key)
positions.add((f[0], key[1]))
if with_af:
try:
af.append(float(f[4]))
except ValueError:
af.append(float("nan"))
rows.append(np.fromiter((get(g, -1) for g in f[nfix:]), np.int8, len(f) - nfix))
if p.wait() != 0:
raise SystemExit(f"bcftools query failed on {path}")
gt = np.vstack(rows) if rows else np.zeros((0, len(samples)), np.int8)
return {
"samples": samples, "keys": keys, "index": index, "positions": positions, "gt": gt,
"af": np.array(af), "duplicate_keys": dups,
} # fmt: skip
def is_snv(key: tuple[str, int, str, str]) -> bool:
return len(key[2]) == 1 and len(key[3]) == 1
def confusion(t: npt.NDArray[np.int8], a: npt.NDArray[np.int8], group: npt.NDArray[np.int64], ngroups: int):
"""Counts (group, truth 0..2, array −1..2) over every cell; ``group`` has the shape of ``t``."""
code = (t.astype(np.int64) * 4 + (a.astype(np.int64) + 1)) + 12 * group
return np.bincount(code.ravel(), minlength=12 * ngroups).reshape(ngroups, 3, 4)
def metrics(c: npt.NDArray[np.int64]) -> dict:
"""``c`` is (truth 0..2) × (array no-call, 0, 1, 2)."""
called = c[:, 1:]
n = int(called.sum())
nocall = int(c[:, 0].sum())
out: dict = {"n_called": n, "n_nocall": nocall}
if n == 0:
return out
trace = int(np.trace(called))
out["concordance"] = trace / n
out["nocall_rate"] = nocall / (n + nocall)
nr = n - int(called[0, 0])
out["nrd"] = (n - trace) / nr if nr else None
for k, name in enumerate(CLASSES):
tp = int(called[k, k])
fn = int(called[k].sum()) - tp
fp = int(called[:, k].sum()) - tp
tn = n - tp - fn - fp
sens = tp / (tp + fn) if tp + fn else None
prec = tp / (tp + fp) if tp + fp else None
out[name] = {
"n_truth": tp + fn,
"sensitivity": sens,
"precision": prec,
"specificity": tn / (tn + fp) if tn + fp else None,
"f1": 2 * sens * prec / (sens + prec) if sens and prec else None,
"nocall_rate": int(c[k, 0]) / (tp + fn + int(c[k, 0])) if tp + fn + int(c[k, 0]) else None,
}
# variant level (non-reference genotypes): a call is a true positive only with the exact genotype
tp = int(called[1, 1] + called[2, 2])
fn = int(called[1, 0] + called[2, 0] + called[1, 2] + called[2, 1])
fp = int(called[0, 1] + called[0, 2] + called[1, 2] + called[2, 1])
sens = tp / (tp + fn) if tp + fn else None
prec = tp / (tp + fp) if tp + fp else None
out["nonref"] = {
"sensitivity": sens,
"precision": prec,
"f1": 2 * sens * prec / (sens + prec) if sens and prec else None,
}
out["matrix_truth_by_array_nc012"] = c.tolist()
return out
def read_bed(path: Path) -> dict[str, tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]]:
iv: dict[str, list[tuple[int, int]]] = defaultdict(list)
with open(path) as fh:
for line in fh:
if line.startswith(("#", "track", "browser")):
continue
f = line.split("\t")
iv[f[0].removeprefix("chr")].append((int(f[1]), int(f[2])))
return {k: (np.array([s for s, _ in v]), np.array([e for _, e in v])) for k, v in iv.items() for v in [sorted(v)]}
def in_bed(bed, chrom: str, pos: int) -> bool:
if chrom not in bed:
return False
s, e = bed[chrom]
i = int(np.searchsorted(s, pos - 1, side="right")) - 1 # BED is 0-based half-open; pos is 1-based
return i >= 0 and pos - 1 < e[i]
def against(arr: dict, tru: dict, cols: list[int], tcols: list[int], restrict=None) -> dict:
"""Array samples ``cols`` vs truth columns ``tcols``: per-sample, per MAF bin × variant type, per site."""
keys = arr["keys"]
n = len(keys)
rows_t = np.full(n, -1, np.int64)
status = np.zeros(n, np.int8) # 0 matched, 1 absent (→ hom ref), 2 allele mismatch, 3 outside region
for i, k in enumerate(keys):
if restrict is not None and not restrict(k):
status[i] = 3
continue
j = tru["index"].get(k)
if j is not None:
rows_t[i] = j
elif (k[0], k[1]) in tru["positions"]:
status[i] = 2
else:
status[i] = 1
use = np.flatnonzero(status <= 1)
A = arr["gt"][np.ix_(use, cols)]
T = np.zeros_like(A)
m = rows_t[use] >= 0
T[m] = tru["gt"][np.ix_(rows_t[use][m], tcols)]
T[T < 0] = -9 # truth no-call → excluded below
snv = np.array([is_snv(keys[i]) for i in use])
if len(tru["af"]):
af = np.where(m, tru["af"][np.maximum(rows_t[use], 0)], 0.0)
af = np.nan_to_num(af, nan=0.0)
maf = np.minimum(af, 1 - af)
mbin = np.where(m, np.digitize(maf, MAF_BINS) - 1, len(MAF_LABELS)) # last bin = absent from truth
else:
mbin = np.where(m, 0, len(MAF_LABELS))
matched_flag = m.astype(np.int64)
S = len(cols)
nb = len(MAF_LABELS) + 1
per_sample = np.zeros((S, 3, 4), np.int64)
by_group = np.zeros((2 * 2 * nb, 3, 4), np.int64) # matched? × snv? × bin
site_disc = np.zeros(len(use), np.int64)
site_called = np.zeros(len(use), np.int64)
site_swap = np.zeros(len(use), np.int64)
step = 50_000
for s0 in range(0, len(use), step):
a = A[s0 : s0 + step]
t = T[s0 : s0 + step]
valid = t >= 0
tt = np.where(valid, t, 0)
sample_g = np.broadcast_to(np.arange(S), a.shape)
per_sample += confusion(tt[valid], a[valid], sample_g[valid], S)
g = (matched_flag[s0 : s0 + step] * 2 + snv[s0 : s0 + step]) * nb + mbin[s0 : s0 + step]
gg = np.broadcast_to(g[:, None], a.shape)
by_group += confusion(tt[valid], a[valid], gg[valid], 4 * nb)
both = valid & (a >= 0)
site_called[s0 : s0 + step] = both.sum(1)
site_disc[s0 : s0 + step] = (both & (a != t)).sum(1)
site_swap[s0 : s0 + step] = (both & (t != 1) & (a == 2 - t)).sum(1)
def pooled(sel) -> dict:
return metrics(by_group[np.atleast_1d(sel)].sum(0))
idx = np.arange(4 * nb).reshape(2, 2, nb) # [matched][snv][bin]
res: dict = {
"sites": {
"array_records": n,
"matched": int((status == 0).sum()),
"absent_in_truth_as_hom_ref": int((status == 1).sum()),
"allele_mismatch_excluded": int((status == 2).sum()),
"outside_region": int((status == 3).sum()),
},
"all": pooled(idx.ravel()),
"matched_only": pooled(idx[1].ravel()),
"snv": pooled(idx[:, 1].ravel()),
"indel": pooled(idx[:, 0].ravel()),
"by_maf": {lab: pooled(idx[1, :, b].ravel()) for b, lab in enumerate(MAF_LABELS)},
"by_maf_snv": {lab: pooled(idx[1, 1, b]) for b, lab in enumerate(MAF_LABELS)},
"absent_in_truth": pooled(idx[:, :, nb - 1].ravel()),
}
ps = [metrics(per_sample[i]) for i in range(S)]
res["per_sample"] = ps
conc = [p["concordance"] for p in ps if "concordance" in p]
nrd = [p["nrd"] for p in ps if p.get("nrd") is not None]
res["per_sample_summary"] = {
"n": len(conc),
"concordance_median": statistics.median(conc) if conc else None,
"concordance_min": min(conc) if conc else None,
"nrd_median": statistics.median(nrd) if nrd else None,
"nrd_max": max(nrd) if nrd else None,
}
# systematic discordance: sites wrong in at least half of the called samples (≥ 10 called)
frac = np.divide(site_disc, site_called, out=np.zeros(len(use)), where=site_called > 0)
sysm = (site_called >= 10) & (frac >= 0.5)
swap = sysm & (site_swap >= 0.8 * np.maximum(site_disc, 1))
ambiguous = np.array([{keys[i][2], keys[i][3]} in ({"A", "T"}, {"C", "G"}) for i in use])
evaluable = int((site_called >= 10).sum())
res["site_errors"] = {
"evaluable_sites": evaluable,
"systematic_discordant_sites": int(sysm.sum()),
"systematic_rate": sysm.sum() / evaluable if evaluable else None,
"allele_swap_sites": int(swap.sum()),
"allele_swap_rate": swap.sum() / evaluable if evaluable else None,
"allele_swap_at_cg_ambiguous": int((swap & ambiguous).sum()),
"systematic_indels": int((sysm & ~snv).sum()),
"systematic_absent_in_truth": int((sysm & ~m).sum()),
}
res["_systematic_rows"] = [
(keys[use[i]], int(site_called[i]), int(site_disc[i]), int(site_swap[i]), bool(m[i]))
for i in np.flatnonzero(sysm)
]
return res
def replicate_concordance(arr: dict, groups: dict[str, list[int]]) -> dict:
gt = arr["gt"]
pairs = []
for ind, cols in sorted(groups.items()):
for x, y in combinations(cols, 2):
a, b = gt[:, x], gt[:, y]
both = (a >= 0) & (b >= 0)
one = (a >= 0) ^ (b >= 0)
nb = int(both.sum())
eq = int((both & (a == b)).sum())
nonref = both & ((a > 0) | (b > 0))
pairs.append(
{
"individual": ind,
"a": arr["samples"][x],
"b": arr["samples"][y],
"both_called": nb,
"concordance": eq / nb if nb else None,
"nonref_discordance": int((nonref & (a != b)).sum()) / int(nonref.sum()) if nonref.any() else None,
"one_side_nocall": int(one.sum()),
}
)
c = [p["concordance"] for p in pairs if p["concordance"] is not None]
d = [p["nonref_discordance"] for p in pairs if p["nonref_discordance"] is not None]
tot_both = sum(p["both_called"] for p in pairs)
tot_eq = sum(round(p["concordance"] * p["both_called"]) for p in pairs if p["concordance"] is not None)
return {
"n_pairs": len(pairs),
"pooled_concordance": tot_eq / tot_both if tot_both else None,
"median_concordance": statistics.median(c) if c else None,
"min_concordance": min(c) if c else None,
"median_nonref_discordance": statistics.median(d) if d else None,
"pairs": pairs,
}
def batch_table(gt_dir: Path, sample_map: dict[str, dict], per_sample_conc: dict[str, float]) -> dict:
path = gt_dir / "sample_summary.tsv"
if not path.exists():
return {}
chips: dict[str, list[dict]] = defaultdict(list)
for r in csv.DictReader(path.open(), delimiter="\t"):
chips[r["sample_folder"] or r["sample_id"].split("_")[0]].append(r)
def f(x: str) -> float | None:
try:
return float(x)
except ValueError:
return None
table = []
for chip, rs in sorted(chips.items()):
cr = [v for r in rs if (v := f(r["call_rate"])) is not None]
ld = [v for r in rs if (v := f(r["logr_dev"])) is not None]
cc = [per_sample_conc[r["sample_id"]] for r in rs if r["sample_id"] in per_sample_conc]
table.append(
{
"chip": chip,
"n": len(rs),
"call_rate_mean": statistics.fmean(cr) if cr else None,
"call_rate_min": min(cr) if cr else None,
"logr_dev_median": statistics.median(ld) if ld else None,
"wgs_concordance_mean": statistics.fmean(cc) if cc else None,
"status_fail": sum(r["status"] != "OK" for r in rs),
}
)
means = [t["call_rate_mean"] for t in table if t["call_rate_mean"] is not None]
med = statistics.median(means)
mad = statistics.median(abs(x - med) for x in means) or 1e-9
for t in table:
t["call_rate_outlier"] = t["call_rate_mean"] is not None and abs(t["call_rate_mean"] - med) > 3 * 1.4826 * mad
all_cr = [f(r["call_rate"]) for rs in chips.values() for r in rs]
all_ld = [f(r["logr_dev"]) for rs in chips.values() for r in rs]
return {
"n_chips": len(table),
"n_samples": sum(t["n"] for t in table),
"call_rate_median": statistics.median(x for x in all_cr if x is not None),
"logr_dev_median": statistics.median(x for x in all_ld if x is not None),
"chips_call_rate_outliers": [t["chip"] for t in table if t["call_rate_outlier"]],
"chips": table,
}
def duplicate_check(gt_dir: Path, sample_map: dict[str, dict]) -> dict:
path = gt_dir / "relatedness.tsv"
if not path.exists():
return {}
ind = {s: m["individual"] for s, m in sample_map.items()}
found: set[frozenset[str]] = set()
false_dup = []
for r in csv.DictReader(path.open(), delimiter="\t"):
a, b = r["sample_a"], r["sample_b"]
if r["degree"] == "duplicate":
if ind.get(a) == ind.get(b):
found.add(frozenset((a, b)))
else:
false_dup.append((a, b, r["kinship"]))
groups: dict[str, list[str]] = defaultdict(list)
for s, i in ind.items():
groups[i].append(s)
expected = {frozenset(p) for g in groups.values() for p in combinations(g, 2)}
return {
"replicate_pairs": len(expected),
"detected_as_duplicate": len(found & expected),
"missed": sorted(" ".join(sorted(p)) for p in expected - found),
"duplicates_between_different_individuals": false_dup,
}
def main() -> int:
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--array", required=True, type=Path)
ap.add_argument("--truth", required=True, type=Path)
ap.add_argument("--giab", type=Path)
ap.add_argument("--giab-bed", type=Path)
ap.add_argument("--giab-sample", default="NA12878")
ap.add_argument("--map", required=True, type=Path)
ap.add_argument("--gt-dir", type=Path)
ap.add_argument("--bcftools", default="bcftools")
ap.add_argument("--chroms", default="", help="comma-separated subset of 1..22,X (tests)")
ap.add_argument("--out", required=True, type=Path)
a = ap.parse_args()
t0 = time.time()
a.out.mkdir(parents=True, exist_ok=True)
smap = {r["sample_id"]: r for r in csv.DictReader(a.map.open(), delimiter="\t")}
chroms = a.chroms.split(",") if a.chroms else None
arr = load(a.bcftools, a.array, chroms=chroms)
tru = load(a.bcftools, a.truth, with_af=True)
print(f"array {arr['gt'].shape}, truth {tru['gt'].shape} ({time.time() - t0:.0f} s)", file=sys.stderr)
tcol = {s: i for i, s in enumerate(tru["samples"])}
cols, tcols, missing = [], [], []
for i, s in enumerate(arr["samples"]):
ind = smap.get(s, {}).get("individual")
if ind in tcol:
cols.append(i)
tcols.append(tcol[ind])
else:
missing.append(s)
summary: dict = {
"array_samples": len(arr["samples"]),
"individuals": len({smap[s]["individual"] for s in arr["samples"] if s in smap}),
"samples_without_truth": missing,
"array_duplicate_keys": arr["duplicate_keys"],
}
wgs = against(arr, tru, cols, tcols)
sysrows = wgs.pop("_systematic_rows")
per = wgs.pop("per_sample")
per_conc = {}
with open(a.out / "per_sample.tsv", "w") as fh:
fh.write("sample_id\tindividual\treplicate\tn_called\tn_nocall\tconcordance\tnrd\n")
for c, p in zip(cols, per, strict=True):
s = arr["samples"][c]
per_conc[s] = p.get("concordance")
fh.write(
f"{s}\t{smap[s]['individual']}\t{smap[s]['replicate']}\t{p['n_called']}\t{p['n_nocall']}\t"
f"{p.get('concordance', '')}\t{p.get('nrd', '')}\n"
)
with open(a.out / "systematic_sites.tsv", "w") as fh:
fh.write("chrom\tpos\tref\talt\tcalled\tdiscordant\tallele_swap\tin_truth\n")
for k, nc, nd, ns, inb in sysrows:
fh.write(f"{k[0]}\t{k[1]}\t{k[2]}\t{k[3]}\t{nc}\t{nd}\t{ns}\t{int(inb)}\n")
summary["nygc_30x"] = wgs
groups: dict[str, list[int]] = defaultdict(list)
for i, s in enumerate(arr["samples"]):
if s in smap:
groups[smap[s]["individual"]].append(i)
rep = replicate_concordance(arr, groups)
with open(a.out / "replicate_pairs.tsv", "w") as fh:
fh.write("individual\ta\tb\tboth_called\tconcordance\tnonref_discordance\tone_side_nocall\n")
for p in rep.pop("pairs"):
fh.write("\t".join(str(p[k]) for k in p) + "\n")
summary["replicates"] = rep
if a.giab and a.giab_bed:
gia = load(a.bcftools, a.giab)
bed = read_bed(a.giab_bed)
gcols = [i for i, s in enumerate(arr["samples"]) if smap.get(s, {}).get("individual") == a.giab_sample]
if gcols and gia["samples"]:
g = against(arr, gia, gcols, [0] * len(gcols), restrict=lambda k: k[0] != "X" and in_bed(bed, k[0], k[1]))
g.pop("by_maf"), g.pop("by_maf_snv")
g["systematic_sites"] = [f"{k[0]}:{k[1]}:{k[2]}:{k[3]}" for k, *_ in g.pop("_systematic_rows")]
g["per_replicate"] = [
{"sample": arr["samples"][c], **{k: p.get(k) for k in ("n_called", "concordance", "nrd")}}
for c, p in zip(gcols, g.pop("per_sample"), strict=True)
]
summary["giab_hg001"] = g
if a.gt_dir:
summary["batch"] = batch_table(a.gt_dir, smap, per_conc)
summary["duplicate_detection"] = duplicate_check(a.gt_dir, smap)
summary["seconds"] = round(time.time() - t0, 1)
(a.out / "summary.json").write_text(json.dumps(summary, indent=1, default=float))
w = summary["nygc_30x"]
print(
f"NYGC: concordance {w['all'].get('concordance'):.5f} NRD {w['all'].get('nrd'):.5f} "
f"no-call {w['all'].get('nocall_rate'):.5f} replicate {rep['pooled_concordance']:.5f}"
)
if "giab_hg001" in summary:
gg = summary["giab_hg001"]["all"]
print(f"GIAB HG001: concordance {gg.get('concordance'):.5f} NRD {gg.get('nrd')}")
return 0
if __name__ == "__main__":
raise SystemExit(main())