diff --git a/config/CIBNOR2021_HEAT_RRSS.config b/config/CIBNOR2021_HEAT_RRSS.config index 417326c..f56cf30 100644 --- a/config/CIBNOR2021_HEAT_RRSS.config +++ b/config/CIBNOR2021_HEAT_RRSS.config @@ -22,11 +22,11 @@ * --attribute family --attribute replicate --attribute tissue --attribute temp \ * --methylkit-levels SS RR * - * NOT YET RUNNABLE AS A VALID ANALYSIS. DMR_METHYLKIT fits RR vs SS with no - * family term, so it would treat 6 vs 6 libraries as independent when the - * phenotype is assigned per family (2 vs 2). See the study YAML limitations. - * Model family, or aggregate to family level, before computing results that - * enter a pool. + * The registered result (2026-10-16) was produced with these settings: + * family as the replicate unit, relaxed Bismark scoring, read-2 M-bias trim, + * and limma on family-pooled, destranded 1 kb tiles (PR #67). The methylation + * work dir must be on a volume with no space in its name: Trim Galore and + * Bismark resolve and pass it unquoted. */ includeConfig 'base.config' @@ -52,6 +52,24 @@ params { // SS = 0 (reference), RR = 1, keyed by run accession. sample_sheet = 'data/studies/CIBNOR2021_HEAT_RRSS/methylkit_samplesheet.csv' + + // Thermal tolerance is a family property: 2 families per class. + replicate_unit = 'family' + design_sheet = 'data/studies/CIBNOR2021_HEAT_RRSS/samplesheet.tsv' + + // 53.9% vs 15.5% unique mapping on a 50k-pair test + // (data/studies/CIBNOR2021_HEAT_RRSS/bismark_scoring_diagnostic.txt). + bismark_align_args = '--score_min L,0,-0.6' + // M-bias: read 2 positions 1-20 are end-repair biased; position 150 + // of both reads is an artifact. + extractor_args = '--ignore_r2 20 --ignore_3prime 1 --ignore_3prime_r2 1' + + // limma on family-pooled tiles; coverage applied after pooling. + dmr_engine = 'limma' + dmr_features = 'genes' + min_coverage = 1 + unit_min_coverage = 10 + tile_min_cpgs = 10 } outdir = 'results/methylation/CIBNOR2021_HEAT_RRSS' @@ -64,11 +82,17 @@ params { process { // 12 HiSeq 2000 PE libraries, 54-73 M read pairs each (~30x of a ~0.6 Gb - // genome). Bismark runs several bowtie2 instances per --multicore. - withName: 'BISMARK_GENOME_PREPARATION' { cpus = 8; memory = '32 GB'; time = '6h' } - withName: 'BISMARK_ALIGN' { cpus = 12; memory = '48 GB'; time = '24h' } - withName: 'BISMARK_DEDUPLICATE' { cpus = 2; memory = '16 GB'; time = '6h' } - withName: 'BISMARK_METHYLATION_EXTRACTOR' { cpus = 8; memory = '16 GB'; time = '12h' } - withName: 'TRIM_GALORE' { cpus = 4; memory = '8 GB'; time = '4h' } - withName: 'DMR_METHYLKIT' { cpus = 4; memory = '32 GB'; time = '6h' } + // genome). Bismark runs several bowtie2 instances per --multicore. These + // fit a 10-CPU, 26 GB workstation (Chlo), one alignment at a time, where + // each library took ~4-7 h at the relaxed score. + withName: 'FASTQC' { cpus = 2; memory = '4 GB'; maxForks = 2 } + withName: 'TRIM_GALORE' { cpus = 4; memory = '8 GB'; maxForks = 1 } + withName: 'BISMARK_GENOME_PREPARATION' { cpus = 8; memory = '16 GB'; time = '12h' } + // --multicore = cpus / 3 = 3 Bismark instances; one library at a time. + withName: 'BISMARK_ALIGN' { cpus = 9; memory = '22 GB'; time = '48h'; maxForks = 1 } + withName: 'BISMARK_DEDUPLICATE' { cpus = 2; memory = '12 GB'; time = '12h'; maxForks = 2 } + withName: 'BISMARK_METHYLATION_EXTRACTOR' { cpus = 6; memory = '12 GB'; time = '24h'; maxForks = 1 } + withName: 'COVERAGE_FILTER' { cpus = 1; memory = '2 GB' } + withName: 'DMR_METHYLKIT' { cpus = 4; memory = '24 GB'; time = '12h' } + withName: 'DMR_POOLED_TILES' { cpus = 2; memory = '22 GB'; time = '6h' } } diff --git a/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_annotation_qc.tsv b/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_annotation_qc.tsv new file mode 100644 index 0000000..f2b13c9 --- /dev/null +++ b/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_annotation_qc.tsv @@ -0,0 +1,6 @@ +study_id comparison_id annotation_context n_regions +CIBNOR2021_HEAT_RRSS rr_vs_ss_day30_oscillating_heat promoter 0 +CIBNOR2021_HEAT_RRSS rr_vs_ss_day30_oscillating_heat exon 0 +CIBNOR2021_HEAT_RRSS rr_vs_ss_day30_oscillating_heat intron 0 +CIBNOR2021_HEAT_RRSS rr_vs_ss_day30_oscillating_heat gene_body 31166 +CIBNOR2021_HEAT_RRSS rr_vs_ss_day30_oscillating_heat intergenic 101421 diff --git a/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_dmr_qc.tsv b/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_dmr_qc.tsv new file mode 100644 index 0000000..bebb208 --- /dev/null +++ b/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_dmr_qc.tsv @@ -0,0 +1,2 @@ +study_id comparison_id n_samples n_candidate_regions n_significant_regions dmr_mode tile_size min_coverage qvalue_cutoff meth_diff_cutoff replicate_unit n_replicate_units overdispersion test engine df_prior destranded tile_min_cpgs n_cpgs_all_units +CIBNOR2021_HEAT_RRSS rr_vs_ss_day30_oscillating_heat 12 132587 0 genes 1000 10 0.05 10 family 4 empirical-Bayes moderated variance limma moderated t on unit M-values, BH limma 2.33914431336831 TRUE 10 7269210 diff --git a/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_dmr_standardized.tsv.gz b/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_dmr_standardized.tsv.gz new file mode 100644 index 0000000..72b8434 Binary files /dev/null and b/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_dmr_standardized.tsv.gz differ diff --git a/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_methylation_report.html b/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_methylation_report.html new file mode 100644 index 0000000..bcbfb43 --- /dev/null +++ b/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_methylation_report.html @@ -0,0 +1,9747 @@ + + + + + + + + + +AREE Methylation Workflow Report + + + + + + + + + + + + + + + + + + + + +
+ +
+ +
+
+

AREE Methylation Workflow Report

+

CIBNOR2021_HEAT_RRSS / rr_vs_ss_day30_oscillating_heat

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Scaffold status

+

This report template has not been executed against real data in this build. It is a structurally complete Quarto document designed to render against the outputs of workflows/methylation/main.nf. When run against the shipped demo processed-results table (data/demo/methylation/GIGAS_PATH03_OsHV1_challenge_vs_sham_dmr_demo.tsv) it would produce real summary tables and plots from that (clearly-labeled simulated) data; it has not been rendered as part of this task.

+
+
+

Run identification

+
    +
  • Study ID: CIBNOR2021_HEAT_RRSS
    • +
  • Comparison ID: rr_vs_ss_day30_oscillating_heat
    • +
  • Mode: raw_reanalysis
    • +
  • Regions in standardized table: 132587
    • +
+
+
+
Workflow: aree-methylation 0.1.0-scaffold 
+Generated: 2026-10-16T15:53:23.785171-07:00 
+
+Warnings:
+ - software_version_unresolved: BISMARK_DEDUPLICATE wrote an empty version for bismark (version command failed or tool missing) 
+ - software_version_unresolved: BISMARK_METHYLATION_EXTRACTOR wrote an empty version for bismark (version command failed or tool missing)
+
+
+
+
+

Hyper vs. hypo methylation

+
+
+ + + + + + + + + + + + + + + + + +
DirectionN regions
hyper62617
hypo69970
+
+
+
+
+
+
+

+
+
+
+
+
+
+

Genomic context distribution

+
+
+ + + + + + + + + + + + + + + + + +
Annotation contextN regions
gene_body31166
intergenic101421
+
+
+
+
+
+
+

+
+
+
+
+
+
+

Intergenic / gene-less regions

+

Regions with no associated gene_id are intentionally retained in the standardized table (not dropped) and are harmonized downstream as genomic_region features with mapping_confidence = unresolved (see src/harmonize/methylation.py).

+
+
+
101421 of 132587 regions have no gene_id (kept as intergenic/unresolved).
+
+
+
+
+

Standardized region table

+

Showing the 500 regions with the smallest q-values of 132,587. The full table is CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_dmr_standardized.tsv.

+ ++++++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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TILE_NC_088857.1_52489001NC_088857.15248900152490000intergenic-6.9770.6159hypo0.00153502.2020limma_pooled_genes
TILE_NC_088857.1_53295001NC_088857.15329500153296000intergenic-10.1590.6159hypo0.00213603.3080limma_pooled_genes
TILE_NC_088857.1_56137001NC_088857.15613700156138000intergenic-2.6380.6159hypo0.00168600.8399limma_pooled_genes
TILE_NC_088858.1_5204001NC_088858.152040015205000intergenic12.9120.6159hyper0.00214804.2070limma_pooled_genes
TILE_NC_088858.1_10783001NC_088858.11078300110784000intergenic38.8460.6159hyper0.001560012.2800limma_pooled_genes
TILE_NC_088858.1_11782001NC_088858.11178200111783000intergenic-52.5010.6159hypo0.001565016.6000limma_pooled_genes
TILE_NC_088858.1_11783001NC_088858.11178300111784000intergenic-38.9830.6159hypo0.001905012.5600limma_pooled_genes
TILE_NC_088858.1_12022001NC_088858.11202200112023000intergenic4.2920.6159hyper0.00186901.3800limma_pooled_genes
TILE_NC_088858.1_21775001NC_088858.12177500121776000intergenic-12.3310.6159hypo0.00162603.9130limma_pooled_genes
TILE_NC_088858.1_28464001NC_088858.12846400128465000intergenic-8.4410.6159hypo0.00156802.6700limma_pooled_genes
TILE_NC_088858.1_29385001NC_088858.12938500129386000intergenic-13.0310.6159hypo0.00155804.1190limma_pooled_genes
TILE_NC_088858.1_33964001NC_088858.13396400133965000intergenic-2.6720.6159hypo0.00167400.8503limma_pooled_genes
TILE_NC_088858.1_36158001NC_088858.13615800136159000intergenic-8.2020.6159hypo0.00183202.6320limma_pooled_genes
TILE_NC_088858.1_36792001NC_088858.13679200136793000intergenic-23.4530.6159hypo0.00142607.3540limma_pooled_genes
TILE_NC_088858.1_37055001NC_088858.13705500137056000intergenic-2.5650.6159hypo0.00192600.8270limma_pooled_genes
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TILE_NC_088858.1_37464001NC_088858.13746400137465000intergenic-6.7440.6159hypo0.00191202.1730limma_pooled_genes
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TILE_NC_088858.1_41664001NC_088858.14166400141665000intergenic-17.7960.6159hypo0.00133905.5480limma_pooled_genes
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TILE_NC_088858.1_44016001NC_088858.14401600144017000intergenic6.8610.6159hyper0.00164602.1800limma_pooled_genes
TILE_NC_088858.1_54399001NC_088858.15439900154400000intergenic7.3600.6159hyper0.00223102.4070limma_pooled_genes
TILE_NC_088858.1_55678001NC_088858.15567800155679000intergenic-7.9720.6159hypo0.00159002.5250limma_pooled_genes
TILE_NC_088858.1_56124001NC_088858.15612400156125000intergenic5.7240.6159hyper0.00225501.8740limma_pooled_genes
TILE_NC_088859.1_79001NC_088859.17900180000intergenic3.1960.6159hyper0.00194601.0320limma_pooled_genes
TILE_NC_088859.1_6627001NC_088859.166270016628000intergenic-5.1000.6159hypo0.00174601.6290limma_pooled_genes
TILE_NC_088859.1_7349001NC_088859.173490017350000intergenic4.1000.6159hyper0.00190201.3200limma_pooled_genes
TILE_NC_088859.1_9307001NC_088859.193070019308000intergenic10.7930.6159hyper0.00217003.5200limma_pooled_genes
TILE_NC_088859.1_9932001NC_088859.199320019933000intergenic7.7210.6159hyper0.00141702.4200limma_pooled_genes
TILE_NC_088859.1_10881001NC_088859.11088100110882000intergenic6.0290.6159hyper0.00155401.9050limma_pooled_genes
TILE_NC_088859.1_11298001NC_088859.11129800111299000intergenic3.8330.6159hyper0.00146801.2050limma_pooled_genes
TILE_NC_088859.1_13639001NC_088859.11363900113640000intergenic39.7400.6159hyper0.001947012.8300limma_pooled_genes
TILE_NC_088859.1_20184001NC_088859.12018400120185000intergenic17.2500.6159hyper0.00191605.5590limma_pooled_genes
TILE_NC_088859.1_22834001NC_088859.12283400122835000intergenic14.8710.6159hyper0.00131104.6280limma_pooled_genes
TILE_NC_088859.1_22835001NC_088859.12283500122836000intergenic17.9000.6159hyper0.00206305.8100limma_pooled_genes
TILE_NC_088859.1_34202001NC_088859.13420200134203000intergenic14.3830.6159hyper0.00187704.6260limma_pooled_genes
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TILE_NC_088859.1_39117001NC_088859.13911700139118000intergenic22.5040.6159hyper0.00204207.2970limma_pooled_genes
TILE_NC_088859.1_46384001NC_088859.14638400146385000intergenic10.4400.6159hyper0.00224503.4160limma_pooled_genes
TILE_NC_088859.1_46570001NC_088859.14657000146571000intergenic9.6580.6159hyper0.00179403.0930limma_pooled_genes
TILE_NC_088859.1_48039001NC_088859.14803900148040000intergenic-4.1080.6159hypo0.00197901.3280limma_pooled_genes
TILE_NC_088859.1_48573001NC_088859.14857300148574000intergenic2.8720.6159hyper0.00133100.8948limma_pooled_genes
TILE_NC_088859.1_50299001NC_088859.15029900150300000intergenic4.6250.6159hyper0.00177801.4800limma_pooled_genes
TILE_NC_088859.1_53325001NC_088859.15332500153326000intergenic-13.4480.6159hypo0.00204404.3610limma_pooled_genes
TILE_NC_088860.1_6247001NC_088860.162470016248000intergenic26.5170.6159hyper0.00210208.6220limma_pooled_genes
TILE_NC_088860.1_8867001NC_088860.188670018868000intergenic16.7770.6159hyper0.00154405.2990limma_pooled_genes
TILE_NC_088860.1_9479001NC_088860.194790019480000intergenic7.2700.6159hyper0.00164302.3090limma_pooled_genes
TILE_NC_088860.1_9948001NC_088860.199480019949000intergenic-1.1440.6159hypo0.00158500.3621limma_pooled_genes
TILE_NC_088860.1_13614001NC_088860.11361400113615000intergenic0.9420.6159hyper0.00197800.3046limma_pooled_genes
TILE_NC_088860.1_18336001NC_088860.11833600118337000intergenic-9.2380.6159hypo0.00204702.9960limma_pooled_genes
TILE_NC_088860.1_19765001NC_088860.11976500119766000intergenic5.8300.6159hyper0.00205501.8910limma_pooled_genes
TILE_NC_088860.1_38765001NC_088860.13876500138766000intergenic-3.3770.6159hypo0.00136801.0550limma_pooled_genes
TILE_NC_088860.1_42339001NC_088860.14233900142340000intergenic-2.0540.6159hypo0.00190200.6616limma_pooled_genes
TILE_NC_088860.1_42478001NC_088860.14247800142479000intergenic-14.6230.6159hypo0.00219004.7740limma_pooled_genes
TILE_NC_088860.1_42698001NC_088860.14269800142699000intergenic-4.8740.6159hypo0.00139701.5250limma_pooled_genes
TILE_NC_088860.1_43874001NC_088860.14387400143875000intergenic-6.2780.6159hypo0.00167901.9980limma_pooled_genes
TILE_NC_088860.1_45100001NC_088860.14510000145101000intergenic-1.2810.6159hypo0.00140900.4012limma_pooled_genes
TILE_NC_088860.1_45503001NC_088860.14550300145504000intergenic7.5840.6159hyper0.00210402.4660limma_pooled_genes
TILE_NC_088860.1_50938001NC_088860.15093800150939000intergenic19.1660.6159hyper0.00210006.2310limma_pooled_genes
TILE_NC_088861.1_4946001NC_088861.149460014947000intergenic-10.4370.6159hypo0.00146703.2810limma_pooled_genes
TILE_NC_088861.1_6259001NC_088861.162590016260000intergenic-3.3920.6159hypo0.00205601.1010limma_pooled_genes
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TILE_NC_088861.1_14728001NC_088861.11472800114729000intergenic1.7150.6159hyper0.00137200.5357limma_pooled_genes
TILE_NC_088861.1_15417001NC_088861.11541700115418000intergenic0.7000.6159hyper0.00202400.2267limma_pooled_genes
TILE_NC_088861.1_24993001NC_088861.12499300124994000intergenic17.8570.6159hyper0.00157405.6490limma_pooled_genes
TILE_NC_088861.1_25838001NC_088861.12583800125839000intergenic-5.1560.6159hypo0.00153401.6270limma_pooled_genes
TILE_NC_088861.1_28607001NC_088861.12860700128608000intergenic1.1500.6159hyper0.00147800.3616limma_pooled_genes
TILE_NC_088861.1_30109001NC_088861.13010900130110000intergenic-1.2320.6159hypo0.00225300.4033limma_pooled_genes
TILE_NC_088861.1_33579001NC_088861.13357900133580000intergenic10.6880.6159hyper0.00138703.3430limma_pooled_genes
TILE_NC_088861.1_36650001NC_088861.13665000136651000intergenic1.0880.6159hyper0.00139200.3405limma_pooled_genes
TILE_NC_088861.1_39854001NC_088861.13985400139855000intergenic-13.5960.6159hypo0.00185404.3680limma_pooled_genes
TILE_NC_088861.1_42032001NC_088861.14203200142033000intergenic20.4710.6159hyper0.00171406.5280limma_pooled_genes
TILE_NC_088861.1_42175001NC_088861.14217500142176000intergenic-11.1660.6159hypo0.00202003.6170limma_pooled_genes
TILE_NC_088861.1_43050001NC_088861.14305000143051000intergenic7.2050.6159hyper0.00225402.3590limma_pooled_genes
TILE_NC_088861.1_45954001NC_088861.14595400145955000intergenic-4.4660.6159hypo0.00170701.4240limma_pooled_genes
TILE_NC_088861.1_46851001NC_088861.14685100146852000intergenic-12.4230.6159hypo0.00162203.9410limma_pooled_genes
TILE_NC_088861.1_47693001NC_088861.14769300147694000intergenic3.8730.6159hyper0.00184201.2440limma_pooled_genes
TILE_NC_088861.1_48160001NC_088861.14816000148161000intergenic-1.0110.6159hypo0.00214200.3292limma_pooled_genes
TILE_NC_088861.1_49181001NC_088861.14918100149182000intergenic-9.4490.6159hypo0.00223803.0910limma_pooled_genes
TILE_NC_088862.1_964001NC_088862.1964001965000intergenic-17.1920.6159hypo0.00169705.4770limma_pooled_genes
TILE_NC_088862.1_3816001NC_088862.138160013817000intergenic-0.7080.6159hypo0.00189800.2279limma_pooled_genes
TILE_NC_088862.1_8106001NC_088862.181060018107000intergenic-5.7020.6159hypo0.00185801.8320limma_pooled_genes
TILE_NC_088862.1_15999001NC_088862.11599900116000000intergenic-8.7680.6159hypo0.00170802.7950limma_pooled_genes
TILE_NC_088862.1_18356001NC_088862.11835600118357000intergenic-1.7100.6159hypo0.00184600.5491limma_pooled_genes
TILE_NC_088862.1_18752001NC_088862.11875200118753000intergenic9.6910.6159hyper0.00128503.0100limma_pooled_genes
TILE_NC_088862.1_25474001NC_088862.12547400125475000intergenic-12.4490.6159hypo0.00132203.8770limma_pooled_genes
TILE_NC_088862.1_26981001NC_088862.12698100126982000intergenic-15.6530.6159hypo0.00134204.8810limma_pooled_genes
TILE_NC_088862.1_27149001NC_088862.12714900127150000intergenic15.4570.6159hyper0.00193004.9850limma_pooled_genes
TILE_NC_088862.1_32503001NC_088862.13250300132504000intergenic-9.9700.6159hypo0.00216803.2520limma_pooled_genes
TILE_NC_088861.1_38219001NC_088861.13821900138220000intergenic11.8600.6178hyper0.00226903.8850limma_pooled_genes
TILE_NC_088859.1_2125001NC_088859.121250012126000intergenic-4.0080.6204hypo0.00228401.3140limma_pooled_genes
TILE_NC_088857.1_3898001NC_088857.138980013899000intergenic-0.7760.6205hypo0.00228900.2543limma_pooled_genes
GENE_LOC105323250NC_088857.147724574775919LOC105323250gene_body-8.7330.6207hypo0.00284402.9270limma_pooled_genes
GENE_LOC105326838NC_088861.12176385521772588LOC105326838gene_body6.8030.6207hyper0.00245602.2460limma_pooled_genes
GENE_LOC105329949NC_088855.182830218286686LOC105329949gene_body-7.1390.6207hypo0.00294502.4010limma_pooled_genes
GENE_LOC105330624NC_088854.13818594238191373LOC105330624gene_body-9.4140.6207hypo0.00276903.1460limma_pooled_genes
GENE_LOC105331625NC_088856.13276196332781800LOC105331625gene_body-5.1380.6207hypo0.00231501.6860limma_pooled_genes
GENE_LOC105341570NC_088858.13167183431682623LOC105341570gene_body-10.7950.6207hypo0.00274803.6050limma_pooled_genes
GENE_LOC105344444NC_088853.14124103341243386LOC105344444gene_body12.7560.6207hyper0.00238304.1990limma_pooled_genes
GENE_LOC105344581NC_088860.13529944335324994LOC105344581gene_body-4.2910.6207hypo0.00253101.4210limma_pooled_genes
GENE_LOC109618882NC_088862.1190884195019LOC109618882gene_body-1.7490.6207hypo0.00238900.5757limma_pooled_genes
GENE_LOC117686942NC_088853.11450156214507926LOC117686942gene_body20.2300.6207hyper0.00291606.7970limma_pooled_genes
GENE_LOC117687749NC_088853.142148074227806LOC117687749gene_body35.2850.6207hyper0.002518011.6800limma_pooled_genes
+
+
+

Provenance

+
+
+
List of 16
+ $ workflow_name                 : chr "aree-methylation"
+ $ workflow_version              : chr "0.1.0-scaffold"
+ $ study_id                      : chr "CIBNOR2021_HEAT_RRSS"
+ $ comparison_id                 : chr "rr_vs_ss_day30_oscillating_heat"
+ $ mode                          : chr "raw_reanalysis"
+ $ date_generated                : chr "2026-10-16T15:53:23.785171-07:00"
+ $ generated_by                  : chr "automated:aree-methylation@0.1.0-scaffold"
+ $ parameters                    :List of 19
+ $ input_checksums               :List of 4
+ $ software_versions             :List of 11
+ $ software_versions_not_captured:List of 1
+ $ container_engine              : NULL
+ $ qc_metrics                    :List of 5
+ $ warnings                      : chr [1:2] "software_version_unresolved: BISMARK_DEDUPLICATE wrote an empty version for bismark (version command failed or tool missing)" "software_version_unresolved: BISMARK_METHYLATION_EXTRACTOR wrote an empty version for bismark (version command "| __truncated__
+ $ output_schema                 : chr [1:9] "region_id" "chrom" "start" "end" ...
+ $ harmonize_command             : chr "aree harmonize --study CIBNOR2021_HEAT_RRSS --input CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_dmr_standardized.tsv"
+
+
+
+ +
+ + +
+ + + + + \ No newline at end of file diff --git a/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_workflow_manifest.json b/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_workflow_manifest.json new file mode 100644 index 0000000..93f28e6 --- /dev/null +++ b/data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_workflow_manifest.json @@ -0,0 +1,104 @@ +{ + "workflow_name": "aree-methylation", + "workflow_version": "0.1.0-scaffold", + "study_id": "CIBNOR2021_HEAT_RRSS", + "comparison_id": "rr_vs_ss_day30_oscillating_heat", + "mode": "raw_reanalysis", + "date_generated": "2026-10-16T15:53:23.785171-07:00", + "generated_by": "automated:aree-methylation@0.1.0-scaffold", + "parameters": { + "mode": "raw_reanalysis", + "study_id": "CIBNOR2021_HEAT_RRSS", + "comparison_id": "rr_vs_ss_day30_oscillating_heat", + "min_coverage": 1, + "qvalue_cutoff": 0.05, + "meth_diff_cutoff": 10.0, + "dmr_mode": "tile", + "tile_size": 1000, + "tile_step": 1000, + "dmr_engine": "limma", + "dmr_features": "genes", + "unit_min_coverage": 10, + "tile_min_cpgs": 10, + "replicate_unit": "family", + "design_sheet": "data/studies/CIBNOR2021_HEAT_RRSS/samplesheet.tsv", + "bismark_align_args": "--score_min L,0,-0.6", + "extractor_args": "--ignore_r2 20 --ignore_3prime 1 --ignore_3prime_r2 1", + "promoter_upstream_bp": 1500, + "genome_assembly": "GCF_963853765.1" + }, + "input_checksums": { + "CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_dmr_standardized.tsv": "sha256:3dd8f2e9c7911f71f2b709320e94cc487c23113c1317297e362e3a4969069bf3", + "CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_methylkit_qc.tsv": "sha256:021e34c1d28c75cb63c64c083e31d106e5057d91513accdb36575553ef6fc4c3", + "CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_annotation_qc.tsv": "sha256:9c9603f4e48acb7edd8e834abf21e3d8515b9a6891b245f81dd5f5f93f683829", + "CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_standardize_qc.tsv": "sha256:8b849b0d33fa8fe6e35cec6483378d5741bd3332ef67078d745c16459fa37ab8" + }, + "software_versions": { + "ANNOTATE_REGIONS": { + "bioconductor-genomicranges": "1.62.1", + "bioconductor-rtracklayer": "1.70.1", + "r-base": "4.5.1" + }, + "BISMARK_ALIGN": { + "bismark": "0.24.2" + }, + "BISMARK_DEDUPLICATE": { + "bismark": "" + }, + "BISMARK_GENOME_PREPARATION": { + "bismark": "0.24.2" + }, + "BISMARK_METHYLATION_EXTRACTOR": { + "bismark": "" + }, + "COVERAGE_FILTER": { + "awk": "awk version 20200816" + }, + "DMR_POOLED_TILES": { + "bioconductor-bsseq": "1.46.0", + "bioconductor-dss": "2.58.0", + "bioconductor-limma": "3.66.0", + "r-base": "4.5.1", + "r-data.table": "1.17.8" + }, + "FASTQC": { + "fastqc": "0.12.1" + }, + "STANDARDIZE_OUTPUT": { + "python": "3.14.8" + }, + "TRIM_GALORE": { + "trim_galore": "0.6.10" + }, + "EMIT_MANIFEST": { + "python": "3.14.8" + } + }, + "software_versions_not_captured": { + "RENDER_REPORT": "renders this manifest, so it runs afterwards; its versions.yml is published to methylation/report/" + }, + "container_engine": null, + "qc_metrics": { + "n_standardized_regions": 132587, + "n_hyper": 62617, + "n_hypo": 69970, + "mean_coverage": null, + "pct_regions_passing_coverage_filter": null + }, + "warnings": [ + "software_version_unresolved: BISMARK_DEDUPLICATE wrote an empty version for bismark (version command failed or tool missing)", + "software_version_unresolved: BISMARK_METHYLATION_EXTRACTOR wrote an empty version for bismark (version command failed or tool missing)" + ], + "output_schema": [ + "region_id", + "chrom", + "start", + "end", + "gene_id", + "annotation_context", + "meth_diff_percent", + "qvalue", + "direction" + ], + "harmonize_command": "aree harmonize --study CIBNOR2021_HEAT_RRSS --input CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_dmr_standardized.tsv" +} \ No newline at end of file diff --git a/data/studies/CIBNOR2021_HEAT_RRSS/bismark_scoring_diagnostic.txt b/data/studies/CIBNOR2021_HEAT_RRSS/bismark_scoring_diagnostic.txt new file mode 100644 index 0000000..88481af --- /dev/null +++ b/data/studies/CIBNOR2021_HEAT_RRSS/bismark_scoring_diagnostic.txt @@ -0,0 +1,9 @@ +A_default map=15.5% CpG=16.5% CHG=2.6% CHH=3.0% +B_score_L0_-0.6 map=53.9% CpG=14.4% CHG=2.8% CHH=2.9% +C_non_directional map=15.5% CpG=16.5% CHG=2.6% CHH=3.0% +D_maxins_1000 map=15.8% CpG=16.5% CHG=2.6% CHH=3.0% +E_R1_single map=37.2% CpG=14.3% CHG=0.8% CHH=0.8% +F_R2_single_pbat map=17.2% CpG=17.7% CHG=4.1% CHH=5.0% + +[exited with code 0] +# 50k trimmed pairs of SRR13482032, Bismark 0.24.2 / bowtie2 2.5.5; script /Volumes/blue-block/aree/tmp/bismark_diag/diag.sh diff --git a/data/studies/CIBNOR2021_HEAT_RRSS/library_qc.tsv b/data/studies/CIBNOR2021_HEAT_RRSS/library_qc.tsv new file mode 100644 index 0000000..d5eddc8 --- /dev/null +++ b/data/studies/CIBNOR2021_HEAT_RRSS/library_qc.tsv @@ -0,0 +1,13 @@ +run_accession sample_id family phenotype_class read_pairs unique_mapping_pct duplicates_removed_pct cpg_methylation_pct chg_methylation_pct chh_methylation_pct +SRR13482027 SS35-R2 35 SS 62692957 53.6 7.43 13.1 0.8 0.7 +SRR13482028 SS35-R1 35 SS 59408893 53.9 7.22 12.9 0.8 0.8 +SRR13482029 SS05-R3 05 SS 60561212 51.5 8.17 14.4 0.8 0.8 +SRR13482030 RR59-R3 59 RR 54048933 53.5 6.26 12.6 1.1 1.1 +SRR13482031 RR59-R2 59 RR 64457182 53.0 7.65 12.6 0.7 0.7 +SRR13482032 SS05-R2 05 SS 62518979 52.0 7.07 12.9 0.8 0.8 +SRR13482033 RR59-R1 59 RR 72642974 52.2 7.87 13.7 0.8 0.7 +SRR13482034 RR52-R3 52 RR 62839334 50.3 7.90 13.7 0.8 0.8 +SRR13482035 RR52-R2 52 RR 54810357 53.4 6.73 11.9 0.7 0.7 +SRR13482036 RR52-R1 52 RR 59967481 53.9 7.28 12.5 0.6 0.6 +SRR13482037 SS35-R3 35 SS 59620738 50.0 6.43 13.4 0.8 0.8 +SRR13482038 SS05-R1 05 SS 69404859 50.9 9.39 13.9 0.8 0.8 diff --git a/data/studies/CIBNOR2021_HEAT_RRSS/replicate_dispersion_qc.tsv b/data/studies/CIBNOR2021_HEAT_RRSS/replicate_dispersion_qc.tsv new file mode 100644 index 0000000..270fe1f --- /dev/null +++ b/data/studies/CIBNOR2021_HEAT_RRSS/replicate_dispersion_qc.tsv @@ -0,0 +1,5 @@ +family class n_tiles median_between_library_variance_over_binomial +05 SS 13668 8.10 +35 SS 13063 6.72 +52 RR 12453 6.83 +59 RR 13731 7.22 diff --git a/registry/studies/CIBNOR2021_HEAT_RRSS.yaml b/registry/studies/CIBNOR2021_HEAT_RRSS.yaml index aa900e5..f3d46c5 100644 --- a/registry/studies/CIBNOR2021_HEAT_RRSS.yaml +++ b/registry/studies/CIBNOR2021_HEAT_RRSS.yaml @@ -17,10 +17,18 @@ # (2021-01-09), following IOCAS2022_OA_ENERGY. Rename it only if a methylation # publication is found. # -# Raw reads are public; no results have been computed yet. results_file is -# null until the methylation workflow is run (see -# config/CIBNOR2021_HEAT_RRSS.config), and that run should not happen until the -# DMR step models family (see limitations). +# REANALYZED 2026-10-10..16 on Chlo: AREE's first complete real methylation +# run. All 12 libraries were aligned with Bismark (--score_min L,0,-0.6; +# 50.0-53.9% unique mapping), deduplicated, and extracted with the read-2 +# end-repair bias trimmed (--ignore_r2 20). Then CpGs were destranded, pooled by +# FAMILY, kept at >= 10x in all 4 families, summed into 1 kb tiles (>= 10 +# CpGs), and tested with limma's moderated t, 2 RR vs 2 SS families +# (methylation.dmr_engine = 'limma', dmr_features = 'genes', PR #67): one unit +# per gene (body + 1.5 kb promoter, 31,166 genes) plus 101,421 intergenic +# 1 kb tiles. RESULT: none significant (smallest q = 0.57). At 2 families per class this design +# cannot distinguish phenotype-class differences from family-to-family +# variation, so this is a null at very low power, not evidence of no effect. +# See limitations (1) and (9). study_id: CIBNOR2021_HEAT_RRSS simulated: false @@ -55,8 +63,8 @@ data_availability: status: "available" access_notes: "Raw paired-end FASTQ for all 12 runs are public in ENA/SRA under PRJNA690951 (first public 2022-02-09). No methylation calls, DMR table, or methylation publication is available. Sample sheet, methylKit sample sheet, and FASTQ manifest with ENA MD5s were generated from ENA metadata; sequence data are not committed. ENA library_selection is RANDOM and the sample alias is 'RAC-Cg-Methyl-Seq'; the bisulfite library kit is not stated." -qc_status: "not_started" -analysis_status: "not_started" +qc_status: "passed_with_warnings" +analysis_status: "harmonized" # --------------------------------------------------------------------------- # One comparison: RR vs SS families, both after 30 d of the same challenge. @@ -109,7 +117,7 @@ comparisons: resilience_classification: "resilience" sample_size: 12 biological_replicates: 2 - results_file: null + results_file: "data/studies/CIBNOR2021_HEAT_RRSS/CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat_dmr_standardized.tsv.gz" meta_analysis_primary: true quality_flags: @@ -123,14 +131,21 @@ limitations: > (1) Pseudo-replication risk: thermal tolerance is a family property and each class has only two families, so the effective sample size is 2 vs 2. The current DMR_METHYLKIT step fits RR vs SS with no family term and would treat - 6 vs 6 libraries as independent, overstating precision. Family must be - modeled, or libraries aggregated to family, before any result is pooled. + 6 vs 6 libraries as independent, overstating precision. The registered + result therefore pools each family's three libraries and tests 2 vs 2 + families (limma moderated t on family M-values). That is the honest error, + and it leaves almost no power: limma's prior df is 3.3, so most tiles rest + on their own 2 residual df. Family genetic background is fully confounded with phenotype class, so family-specific methylation (including cytosine variation from SNPs, which bisulfite data cannot separate from C-to-T methylation calls without genotype-aware filtering) can look like a phenotype effect. (2) All 12 - libraries share one BioSample; the deposit does not establish that - replicates are distinct animals. (3) There is no ambient-temperature control, + libraries share one BioSample, so the deposit does not establish that + replicates are distinct animals. The data are consistent with distinct + animals: within each family, tile methylation varies between libraries + 6.7-8.1x more than binomial sampling predicts (replicate_dispersion_qc.tsv; + 1 kb tiles on NC_088853.1 at >= 30x in all three libraries). Technical + replicates would give about 1. (3) There is no ambient-temperature control, so the comparison is tolerant vs susceptible genotype under heat, not heat response. (4) The phenotype is assigned by pedigree and breeding-program performance; no organismal outcome for these animals is deposited, and the @@ -143,6 +158,32 @@ limitations: > to GCF_963853765.1 and must be lifted over, not relabeled, if the reference changes. (8) Until a second real thermal methylation study is registered (candidate: PRJNA562805), this study cannot contribute to any k >= 2 pool. + (9) No unit is significant at FDR 0.05 (smallest q 0.572). 809 of 31,166 + genes and 3,363 of 101,421 intergenic tiles differ by >= 10 points between + classes. Units are genes (all CpGs in body + promoter) so that each gene + gives one record and links to RNA-seq. An earlier tile-only pass (319,088 + 1 kb tiles, smallest q 0.336) gave several records per gene, which collide + in meta-analysis. The 17 "DMRs" from + a first family-level methylKit pass (2026-10-16) were artifacts: with 2 + families per arm, methylKit's per-region overdispersion collapsed to the + binomial for 296 of its 397 q < 0.05 tiles. Unadjusted p < 0.001: 25 genes + (~31 expected by chance, so no excess at gene level) and 197 intergenic + tiles (~101 expected), so what signal there is looks local and mostly + outside genes. Every unit carries a standard error + (se_meth_diff_percent = |diff / moderated t|) and an unadjusted p-value, so + it can pool: use the effect sizes and standard errors, not the per-study + calls. (10) Methylation calls + ignore read-2 positions 1-20 and the last base of each read (M-bias: + read-2 CpG methylation 84% at position 1, settling to ~14% by 15-20). + Without this, CHH methylation read 2.9%; after it, 0.6-1.1%, i.e. >= 99% + bisulfite conversion. (11) Unique mapping is 50-54% even with relaxed + scoring (13-15% at Bismark's default), so heterozygous or diverged regions + are under-represented. (12) The manifest records an empty Bismark version + for deduplication and extraction (a version-string mismatch, fixed in + PR #67 after this run). All three steps used the same Bismark 0.24.2. To + keep the deduplication and extraction cache, the run held those two module + files at their pre-fix version (faecd31~1). They differ from PR #67 only in + that version line. provenance: registered_by: sr320 @@ -168,6 +209,25 @@ provenance: Because sr320 is a co-author on that paper, the replicate identity and phenotyping questions in the limitations can be put directly to the corresponding author (C. Escobedo-Fregoso, CIBNOR); see issue #6. + REANALYSIS (Chlo, 2026-10-09..16; draft PR #14): 24 FASTQ (129 GB), + all matching ENA MD5. Twelve resumed transfers came out corrupt at full + size and were re-fetched. Nextflow session + cdc10126-dbdf-465e-b876-71efcae13f47. First real run of + workflows/methylation; first-run fixes went to main in PR #15 (zcat on + macOS, CpG-only extraction, .fa genome name, --basename vs --multicore, + per-study Bismark options) and PR #67 (limma/DSS pooled-tile engines, + report table cap, version capture). Tools: Bismark 0.24.2, bowtie2 + 2.5.5, Trim Galore 0.6.10 (cutadapt 5.2), limma 3.66.0, R 4.5.1. ALIGNMENT + SCORE: a 50k-pair test on SRR13482032 mapped 15.5% of pairs at the + default --score_min L,0,-0.2, 53.9% at L,0,-0.6, 15.5% non-directional + and 15.8% with -X 1000 (bismark_scoring_diagnostic.txt). The library is + directional, and the threshold was the cause. The full default run + (13.3-14.0%) was discarded. DMR TEST: chosen on synthetic 2-vs-2-family + data with real family-level variance (workflows/methylation/README.md). + limma was calibrated (1/380 false positives, 5.0% of null p < 0.05); DSS + gave 57/380 false positives; methylKit MN collapses. Per-library QC is in + library_qc.tsv. Decisions by sr320: native toolchain, relaxed scoring and + realignment (2026-10-12), DSS-or-better pooled test (2026-10-16). source_links: - "https://www.ncbi.nlm.nih.gov/bioproject/PRJNA690951" - "https://www.ebi.ac.uk/ena/browser/view/PRJNA690951" diff --git a/tests/test_cibnor2021_study.py b/tests/test_cibnor2021_study.py index 9aaab52..60e885d 100644 --- a/tests/test_cibnor2021_study.py +++ b/tests/test_cibnor2021_study.py @@ -1,12 +1,12 @@ """Curation tests for CIBNOR2021_HEAT_RRSS. AREE's first real methylation study, and its first real thermal-resilience -contrast, registered from deposited metadata before any reanalysis. The -project deposits all 12 libraries under one BioSample, so the design is parsed -from experiment_title. These tests pin that parse, the family structure behind -it, and the curation decisions that are easy to erode later: biological -replicates counted as families, no borrowed DOI, and SS as the methylKit -reference. +contrast. The project deposits all 12 libraries under one BioSample, so the +design is parsed from experiment_title. These tests pin that parse, the family +structure behind it, and the decisions that are easy to erode later: +biological replicates counted as families, no borrowed DOI, SS as the +reference, and a result tested on 2 vs 2 families that is reported as the +null it is. """ from __future__ import annotations @@ -94,7 +94,6 @@ def test_tolerance_contrast_under_one_challenge_not_an_exposure(study): assert comp["phenotype_direction"] == "increase" assert comp["tissue"] == "gill" assert "not an unexposed control" in comp["control_condition"] - assert comp["results_file"] is None def test_rnaseq_paper_is_context_not_the_source_doi(study): @@ -111,3 +110,61 @@ def test_fastq_manifest_covers_every_run_as_paired_end(samplesheet): assert len(manifest) == 2 * len(runs_in_sheet) assert all(len(r["md5"]) == 32 for r in manifest) + + +def test_config_tests_families_with_limma_on_pooled_tiles(): + from common import REPO_ROOT + + config = (REPO_ROOT / "config" / "CIBNOR2021_HEAT_RRSS.config").read_text() + for setting in ("replicate_unit = 'family'", "dmr_engine = 'limma'", + "bismark_align_args = '--score_min L,0,-0.6'", "dmr_features = 'genes'", + "extractor_args = '--ignore_r2 20 --ignore_3prime 1 --ignore_3prime_r2 1'"): + assert setting in config, setting + + +def test_manifest_records_a_family_level_limma_run(): + stem = "CIBNOR2021_HEAT_RRSS_rr_vs_ss_day30_oscillating_heat" + manifest = json.loads((STUDY_DIR / f"{stem}_workflow_manifest.json").read_text()) + params = manifest["parameters"] + assert params["replicate_unit"] == "family" + assert params["dmr_engine"] == "limma" + assert params["unit_min_coverage"] == 10 + with open(STUDY_DIR / f"{stem}_dmr_qc.tsv") as fh: + (qc,) = list(csv.DictReader(fh, delimiter="\t")) + assert qc["n_replicate_units"] == "4" + assert qc["engine"] == "limma" + + +def test_result_is_a_low_power_null_not_a_list_of_dmrs(study): + """Gene-body units plus intergenic tiles, none at q < 0.05: a null, with effect sizes.""" + import pandas as pd + + from common import REPO_ROOT + + (comp,) = study["comparisons"] + df = pd.read_csv(REPO_ROOT / comp["results_file"], sep="\t") + assert len(df) == 132587 + genes = df[df["region_id"].str.startswith("GENE_")] + assert len(genes) == 31166 and genes["gene_id"].is_unique # one record per gene + assert (df["qvalue"] < 0.05).sum() == 0 + # Poolable: every tile has a standard error and an unadjusted p-value. + assert df["se_meth_diff_percent"].notna().all() and df["pvalue"].notna().all() + assert set(df["analysis_method"]) == {"limma_pooled_genes"} + assert study["qc_status"] == "passed_with_warnings" + assert "null at very low power" in STUDY_PATH.read_text() + + +def test_libraries_vary_like_distinct_animals_within_each_family(): + with open(STUDY_DIR / "replicate_dispersion_qc.tsv") as fh: + rows = list(csv.DictReader(fh, delimiter="\t")) + assert {r["family"] for r in rows} == FAMILIES["RR"] | FAMILIES["SS"] + # Technical replicates would sit near 1 (binomial); these are 6.7-8.1. + assert all(float(r["median_between_library_variance_over_binomial"]) > 3 for r in rows) + + +def test_every_library_mapped_and_converted_well(): + with open(STUDY_DIR / "library_qc.tsv") as fh: + rows = list(csv.DictReader(fh, delimiter="\t")) + assert len(rows) == 12 + assert all(float(r["unique_mapping_pct"]) >= 50 for r in rows) + assert all(float(r["chh_methylation_pct"]) <= 1.5 for r in rows)