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     Quick Analysis Plan


    Recompute your DEG & LAD enrichment checks (rigorously)
    I’ll outline a reproducible pipeline to (1) call differential expression from GSE283514, (2) define LADs from your CUT&Tag H3K9ac experiment, and (3) recompute enrichment statistics (overlap + permutation/FDR) with transparent QC, thresholds, and sensitivity analyses.



     Long Analysis Plan


    BIOINFORMATICS PLAN β€” Recompute DEG & LAD enrichment checks
    Goal: reproduce (and verify) DEG and LAD enrichment results derived from GSE283514 and a H3K9ac CUT dataset, using explicit QC, explicit thresholds, and enrichment with permutation-based nulls (so the result is not an artifact of gene/peak size, genomic mappability, or promoter/feature definitions).
    0) Assumptions audit (do not proceed blindly)
    • Clarify: is GSE283514 an expression (RNA-seq/microarray) study with defined groups/contrasts, and what are the exact sample-to-condition mappings?
    • Clarify: what exactly are β€œLADs” in your workflowβ€”are LADs directly provided (e.g., lamina-associated BEDs), or are they inferred from CUT&Tag peaks (H3K9ac peaks transformed into LAD-like regions)? These are not equivalent, and changing the LAD definition can flip enrichment.
    • Clarify: what enrichment check is being recomputedβ€”(a) overlap between DEGs and LADs, (b) GO/pathway enrichment, (c) rank-based GSEA-style enrichment, or (d) fold-change enrichment of mark occupancy in LADs?
    1) Data ingestion + strict QC (reproducibility first)
    1.1 Download & verify metadata
    • Fetch raw/count/signal objects from GSE283514 and confirm library layout, strandedness, replicate count, and reference genome build.
    • Fetch CUT&Tag data for H3K9ac CUT (FASTQ/BAM/peak calls, plus input/control if available). Confirm paired-end/single-end, blacklist usage, effective genome size, and whether you have matched controls.
    1.2 Expression QC (GSE283514)
    • Compute library size distributions; check low-complexity samples; verify that replicates cluster by condition (not by run).
    • If counts are available: filter genes consistently (document exact thresholds). If raw FASTQ: run an alignment/count workflow and document all parameters.
    1.3 CUT&Tag QC (H3K9ac)
    • Assess FRiP, duplication rate, strand/bias, and enrichment in known active regions (without β€œcherry-picking”).
    • If peak calling is needed: re-call peaks using the same or improved standard settings, and check replicate concordance (e.g., overlap/I&U distribution).
    2) Recompute DEG (exactly) β€” with contrast-by-contrast transparency
    2.1 Define contrasts exactly
    • List every intended contrast (e.g., condition A vs B; treated vs control; timepoint differences). Use the same contrast names as the original paper/analysis you’re re-checking.
    • Record: whether the original DEG list used raw p-values, adjusted p-values (FDR), and the exact cutoffs (e.g., FDR<0.05 and |log2FC|>1). Do not assume cutoffs.
    2.2 Differential expression model
    • Use a single, documented count-based DE approach consistent with RNA-seq counts (or the appropriate method for microarray/normalized inputs). Record dispersion handling, covariates, and batch variables if they exist.
    • Produce: (a) volcano plot, (b) ranked list by statistic (for rank-based enrichment), (c) DEG table with all key columns.
    2.3 Sensitivity sweep (to detect fragile enrichment)
    • Recompute DEG under multiple threshold sets: e.g., FDR<0.05 vs FDR<0.1, and |log2FC|>0 vs |log2FC|>1.
    • Track how DEG set size changes; enrichment should be stable if it’s real, and unstable if it’s threshold-driven.
    3) Recompute LAD (definition-critical) β€” from H3K9ac CUT or provided LADs
    3.1 LAD definition check
    • Case A: If LADs are provided as BED coordinates: load them and do no inference.
    • Case B: If LADs are inferred from CUT&Tag/H3K9ac peaks: define the inference rule explicitly (e.g., β€œhighest density bins near lamina”, β€œH3K9ac domain boundaries mapped to LADs”, β€œoverlap with precomputed LAD borders”). Any ambiguity here invalidates comparisons.
    3.2 Map LADs to genes (the other critical mapping)
    • Choose gene↔LAD mapping: (a) promoter overlap only (e.g., TSS window), (b) gene body overlap, or (c) distance-based nearest feature.
    • Document gene annotation build and TSS window size if used.
    3.3 LAD QC
    • Compute LAD length distribution, genomic coverage, and gene counts per LAD set.
    • Check whether inferred LADs are biased toward high-coverage regions (e.g., GC-rich or mappability-rich) which can inflate overlap enrichment.
    4) Recompute enrichment checks (robust statistics, not just overlap counts)
    4.1 Basic overlap (transparent baseline)
    • Let DEGs = set of significant genes from the DEG contrast; let LAD-genes = genes mapped to LADs.
    • Compute overlap size and hypergeometric/Fisher exact p-value using a clearly defined universe (e.g., all tested genes in the DE model, not β€œall genome genes”).
    4.2 Permutation nulls (to prevent size/mappability artifacts)
    • Permutation scheme 1: sample size-preserving shuffleβ€”randomly select gene sets of equal size from the DE universe; recompute overlap distribution.
    • Permutation scheme 2: degree/feature-matched shuffleβ€”match genes by expression baseline or by gene length/GC/mappability bins (if those covariates are available).
    • Report empirical p-values and FDR across contrasts/threshold sweeps.
    4.3 Rank-based enrichment (avoids hard DEG cutoff fragility)
    • Use a ranked gene statistic (e.g., signed log2FC or DE test statistic). Compute enrichment of LAD-genes using an ES-style method (or equivalent). Compare with DEG-threshold overlap.
    • If rank enrichment is strong but DEG overlap is weak, that indicates thresholding loss.
    4.4 Directionality (UP/DOWN separate)
    • Compute enrichment for UP-DEGs separately from DOWN-DEGs. Report both.
    5) Visualizations (Plotly) β€” show robustness & where it breaks
    These charts will update once the actual DEG/LAD tables are computed.
    6) Deliverables (what the recomputation will output)
    • DEG tables per contrast: gene_id, log2FC, p-value, FDR, test statistic, plus the exact filtering metadata.
    • LAD gene mapping table: gene_id, LAD_id (or LAD set name), mapping method (promoter/body/nearest), and the LAD definition parameters.
    • Enrichment results table: overlap counts, universe size, Fisher/hypergeometric p-value, empirical permutation p-value, and q-value.
    • Reproducibility manifest: all parameter values (thresholds, window sizes, reference builds, random seeds, permutation count, peak calling settings).
    7) Critical blindspots to test (common failure modes)
    • Mismatch of genomic coordinate build between DEG annotations and LAD/peak coordinates.
    • Universe definition errors (using β€œall genes” instead of β€œall genes tested” can distort p-values).
    • LAD definition inferred from H3K9ac CUT may conflate β€œactive chromatin” with β€œlamina association”; enrichment might reflect activation rather than nuclear position.
    • Threshold fragility: if enrichment vanishes under modest DEG cutoff changes, the original claim may be threshold-dependent.
    Buttons: run/iterate


    Feedback:    

    Updated: April 16, 2026

     Top Data Sources ExportMCP


     Analysis Wizard



    It will download GSE283514 expression data, compute DE gene tables for each contrast, map LAD-linked genes from H3K9ac-based LAD definition, then recompute overlap and permutation-based enrichment with sensitivity plots.



     Hypothesis Graveyard


    Any enrichment that collapses under modest DEG threshold changes and under degree-/length-matched permutations is likely threshold/bias-driven rather than robust biology.


    If enrichment disappears when using a stricter gene↔LAD mapping (promoters only vs gene-body), then the original result is probably driven by broad overlap with large genomic regions.

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