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    Smart-Seq2 preimplantation PCA: intra-vs-inter embryo correlation

    Data needed to draw the required graphs: I do not have the actual PCA scores (PC1/PC2/… per embryo) or the intra/inter grouping labels in the research payload you provided. Without those raw PCA coordinates, I cannot compute correlations or generate truthful Plotly figures (and I will not fabricate data).

    What to upload (so I can generate the graphs immediately)

    • PCA scores table (wide or long): one row per embryo, with columns like embryo_id, PC1, PC2, PC3, ....
    • intra vs inter labels for each embryo (or at least a rule to derive them): e.g., columns like group_type in {intra, inter} or pair_id with within-pair vs across-pair pairing logic.
    • Replicate/embryo correspondence info if you mean β€œintra” = within-same-embryo (technical replicates) vs β€œinter” = different embryos, or if β€œintra/inter” = within-same stage vs between stages, etc.

    Once you provide PCA scores, these graphs will be produced

    1. PC1 intra-vs-inter correlation: correlation of PC1 across embryos within intra groups vs across inter groups (with 95% CI if bootstrap is permitted by your data scale).
    2. PC2 intra-vs-inter correlation (and PCs you include).
    3. Correlation-by-PC heatmap: diagonal blocks comparing intra vs inter correlation strength per PC.
    4. Consistency scatter plots: e.g., mean PC1 per embryo-pair (intra) vs mean PC1 per matched inter pairing.
    Critical checks: I will verify that the β€œintra” and β€œinter” definitions are consistent (otherwise correlation comparisons can be invalid), that embryo counts per group are sufficient, and that any correlation is not driven by a small number of embryos.
    Upload the PCA-score table + intra/inter labels; the agent will compute intra-vs-inter correlations and render Plotly figures.

    Epistemic humility (why graphs are blocked)

    Correlation plots require numerical PCA coordinates per embryo (or a valid distance metric derived from the same coordinates). Since those raw numbers were not included in the provided inputs, any computed correlation would be unsupported or fabricated.


    Feedback:    

    Updated: April 14, 2026

     Top Data Sources ExportMCP


     Analysis Wizard



    It will load a PCA-scores table and intra/inter labels, compute correlations per PC within intra and inter groups, estimate confidence intervals, and render Plotly correlation/heatmap figures for Smart-Seq2 embryos.



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