HubG25: Admixture JS

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add dist colno

off0.5x1x1.5x2x

Mode

Custom population name

Type a population name to fetch its coordinates from your SOURCE/TARGET data, or paste a G25 line. Set a weight (%), then add or subtract — one population at a time. Additive weights must sum to 100%; subtractive weights must total under 100%. Weights and signs stay editable in the list below.

Population

Weight %

Additive total: 0% (need 100%)

Output

Ghost Generator solves T = Σ(addᵢ·Aᵢ) − Σ(subⱼ·Sⱼ) for the residual population, rescaling by 1/(1 − Σsubⱼ) so the mixture coefficients sum to 1. Weighted Average Mode builds sample-size–weighted means labelled Name_(n=N).

01 · Target

One line: label, then comma-separated coordinates.

02 · Sources

One population per line. At least two. Click Load sources to refresh the checklist.

03 · Bootstrap replicates

Dimensions resampled with replacement; each replicate is refit.

Fit is the NNLS solution on all dimensions. SD is the spread across bootstrap resamples. Z = Fit ÷ SD — high Z means load-bearing, low Z means noise.

Stability results

Paste a target and sources, load the checklist, then run. Z ≥ 3 is stable; 1–3 provisional; < 1 noise.

Disclaimer: this tab's baseline Fit % is the exact NNLS least-squares solution on all G25 dimensions. Single and Multi admixture instead use a fast, stochastic Monte Carlo optimizer (their cycles setting) that approximates that same solution rather than solving it directly, so their weights and distance can come out slightly different — usually by a fraction of a percent. Raising cycles in Single/Multi narrows the gap at the cost of speed.