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     Quick Analysis Plan


    Clonal architecture timeline plan (evidence-based)
    1) Convert each review claim into a quantitative “event” (SNV/CNA/SV appearance, expansion, selection) with uncertainty. 2) Fit event order/branching under constraints using CCF/sAGP-based clonality models like CHAT for bulk . 3) For true temporal genotyping, leverage longitudinal single-cell multi-omics such as SPLONGGET timepoints D0→Q3 to reconstruct genotype→regulatory→phenotype trajectories .



     Long Analysis Plan


    Analyze Data: Construct Clonal Architecture Timeline from Review Claims
    Goal: transform qualitative “review claims” into a quantitative clonal architecture timeline with explicit uncertainties, using evidence from (i) longitudinal multi-omics genotyping (e.g., SPLONGGET) and (ii) bulk subclonality inference frameworks that output cancer cell fraction (CCF) and segmental aneuploidy (sAGP) .
    Decision points (skeptical gate)
    • If your input is only “review claims” (no raw VAF/CCF/CNA/SV tables), you cannot uniquely reconstruct event order—only produce a constrained posterior over plausible timelines with wide uncertainty.
    • If you do have raw genomic measurements, prioritize direct temporal genotyping (e.g., longitudinal single-cell/CNV/SNV/SV calls) over inferred ordering from cross-sectional clonality.
    • If you need an inference engine for bulk multi-region data, select a model that outputs cell fractions (CCF) and sAGP (e.g., CHAT) .
    Visualization first: example longitudinal backbone from SPLONGGET excerpt
    Using the provided timepoint tumor/SNV/SV counts from the SPLONGGET longitudinal case study , this figure demonstrates a timeline canvas for later event-order insertion.
    Core pipeline (from claim → event graph → timeline)
    The pipeline below is designed to work in two modes depending on your inputs: (A) raw-data mode or (B) claim-only mode.
    Mode A — if you have raw numbers (recommended)
    1. Extract quantitative observations per sample/region/timepoint:
      • SNVs with VAF/CCF, CNAs with sCNA bins, SVs with breakpoints or presence/absence.
      • Copy-number segmentation outputs (e.g., CBS-like bins) because CHAT’s framework uses sCNA segmentation into regions and binning .
    2. Infer clonal fractions (CCF) and sAGP (bulk) or per-cell genotypes (single-cell):
      • Use CHAT-style inference for bulk SNVs+SNP-array CNAs to obtain cancer cell fractions per mutation and multimodal clonality .
      • For longitudinal single-cell multi-omics, directly genotype at timepoints and track genotype emergence across D0/Q1/Q2/Q3 .
    3. Turn CCF/genotype sets into discrete “clone states”:
      • Define clone identity by a thresholded signature vector of mutations/CNAs/SVs (with posterior probabilities if available).
      • Represent each clone state as a node in a directed acyclic graph (DAG) consistent with observed appearances and disappearances.
    4. Infer event order under constraints:
      • Constraints: if variant A is observed in a timepoint, its ancestor events must occur no later than that timepoint.
      • Constraints: ordering must respect CCF hierarchy when available (founder clones have higher or earlier CCF for many frameworks, but allow exceptions).
      • For lineage-timed data, you can allow “punctuated” emergence, but must encode uncertainty because some architectures are multimodal and scenario-dependent .
    5. Output the clonal architecture timeline:
      • A timeline plot: x-axis timepoints; stacked bars for inferred clone cellular fraction; arrows for inferred transitions.
      • A second plot showing genotypic event bars (SNV/CNA/SV emergence with confidence intervals).
    Mode B — if input is claim-only (no raw variant tables)
    • Convert each claim into a structured event: (entity, event type, timepoint ordering relation, directionality, qualitative strength).
    • Use the closest available “quantitative anchors” from the provided literature excerpts when they exist (e.g., SPLONGGET gives explicit counts per timepoint; use those to calibrate timeline axes) .
    • When claims contradict, represent them as competing graph hypotheses; do not collapse into a single trajectory without showing uncertainty.
    • Explicitly label the output as hypothesis-level rather than inferred genotypic chronology.
    Epistemic humility + failure modes to check
    • Model identifiability: CHAT assumes regional two-population mixing; some sCNA regions may be unfit, limiting sAGP estimation for those regions .
    • Temporal confounding: in longitudinal studies, growth rate changes and sampling shifts can mimic “clonal emergence” signals; you must check whether variants track cell-fraction changes rather than only raw counts.
    • Selection vs detection: increased mutation counts at later timepoints can reflect both true evolutionary dynamics and technical changes (coverage/depth). SPLONGGET excerpt provides totals but you should still normalize by coverage when available .
    Suggested deliverables (what you should code/produce)
    • Event schema (JSON/TSV): for each event type (SNV/CNA/SV/regulatory), store timepoint bounds and confidence.
    • Clone DAG builder: nodes = clone states; edges = allowed parent→child transitions; prune using observed constraints.
    • Timeline plot(s): (1) clone fraction over time, (2) variant/regulatory event bars over time.
    • Uncertainty reporting: show credible intervals or posterior edge probabilities; do not output a single “true” order unless data is directly time-resolved.


    Feedback:    

    Updated: March 29, 2026

     Top Data Sources ExportMCP


     Analysis Wizard



    It will parse your claim table into time-stamped mutation/CNA/SV events, fit a constrained clone DAG, then generate a timeline figure with posterior uncertainty using longitudinal genotyping counts as numeric anchors.



     Hypothesis Graveyard


    A single deterministic clone ordering from narrative claims alone is unlikely to be identifiable; contradictions and missing measurements will collapse into arbitrary choices without uncertainty propagation.


    Assuming regional two-way mixing always holds in bulk subclonality inference will likely produce false event orders in highly complex mixtures where CHAT reports limited identifiability for some sCNA regions.

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