Sidechain.

Rung 1': per-gene log2FC pooled (inverse-variance) over K562 genome-wide + H1 2025 for the targets they cover, gene-wise positive-part shrinkage, re-anchored on each context's control profile; minimum-variance (even-spread) integer cells at control depth; 28 uncovered targets get the H1 mean shift.

Same transferred log2FCs as SER-1 (K562 genome-wide + H1, inverse-variance pooled, gene-wise shrinkage, alpha=1), emitted as independent Poisson cells at control depth instead of minimum-variance cells. Controlled experiment on emission dispersion.

SER-1 with the transferred log2FCs left unshrunk (no gene-wise positive-part shrinkage); minimum-variance cells. Probe: does shrinkage remove small true signals that cosine-PDS needs?

SER-2 = cross-line delta transfer, model 2 (named before the knob letters; shrinkage off, as in SER-1n). Borrows each gene's knockdown effect from K562, H1 and the challenge-panel slice of two genome-wide screens in colon and kidney lines — the first entry to cover all 300 targets — and re-anchors it on the new line's resting state. All models: saberhq.com/sidechain/models/ (card written here — this entry reached the board before cards shipped with every submission)

SER-3n = cross-line delta transfer, model 3 (n = no shrinkage). The same four sources as SER-2, with the two genome-wide screens read in full — every gene they measured rather than the challenge panel alone — re-anchored on the new line's resting state. All models: saberhq.com/sidechain/models/ (card written here — this entry reached the board before cards shipped with every submission)

SER-3fn = cross-line delta transfer, model 3 (f = floored source weights, n = no shrinkage). Borrows each gene's knockdown response from K562, H1 and two genome-wide screens in colon and kidney lines, trusts each source only as far as its cell counts justify, and re-anchors it on the new line's resting state. All models: saberhq.com/sidechain/models/

SER-3afn = cross-line delta transfer, model 3 (a = amplified transfer, f = floored source weights, n = no shrinkage). Borrows each gene's knockdown response from K562, H1 and two genome-wide screens in colon and kidney lines, and scales it up to reference strength, because those screens silenced their targets only partially. All models: saberhq.com/sidechain/models/

SER-4afn = cross-line delta transfer, model 4 (a = amplified transfer, f = floored source weights, n = no shrinkage). Borrows each gene's knockdown response from K562, H1 and two genome-wide screens in colon and kidney lines, and scales it up to reference strength, because those screens silenced their targets only partially. All models: saberhq.com/sidechain/models/

SER-5acfnt = weights each source's vote by how well it actually predicts a new cell line, not by how confident it sounds. All models: saberhq.com/sidechain/models/

PHE-2 = a neural network trained on two cancer cell lines, asked to predict each knockdown's cells directly from an unseen line's untouched controls. All models: saberhq.com/sidechain/models/

SER-6aefn = cross-line delta transfer, model 6 (a = amplified transfer, e = emission dial at lambda 0.5, f = floored source weights, n = no shrinkage). Borrows each gene's knockdown response from K562, H1 and two genome-wide screens in colon and kidney lines, scales it to reference strength, and emits cells with half the Poisson spread instead of identical ones -- the one change from SER-4afn. All models: saberhq.com/sidechain/models/