PCA Flutter

Description

Interactive Principal Component Analysis operator for Tercen, implemented in Flutter. Replaces the ShinyR implementation at pca_shiny.

Usage

Input projection	.
y-axis	numeric measurement values
row	variables (features/spots) — one row per variable
column	observations (samples) — one column per sample
colors	(optional) categorical factor used as the default Color By in the viewer

The input is interpreted as an observations × variables matrix: each column becomes a point projected onto the principal components, and each row becomes a loading vector.

Output relations	.
PC1..PCn	score for each observation on each principal component
X1..Xn	loading for each variable on each principal component
Operator view	interactive 3D scores, biplot, pairs, and scree plots

Scores and loadings are returned together as a single flat cross-tab (one row per observation × variable pair), matching the output format of pca_shiny.

Factors (operator properties)

These are set on the operator before the app opens. They control the computation itself — the viewer controls only change what's displayed.

Factor	Default	Description
Scale Spots	No	If Yes, each variable is standardised (zero mean, unit variance) before PCA. Use when variables are on different scales. If No, PCA runs on centered-only data (covariance PCA).
Number of Components	5	Maximum number of principal components to compute and return. Capped at min(nObs − 1, nVars).
Subtract component	0	If non-zero, the specified component is subtracted from the data before PCA is re-run. Useful for isolating structure orthogonal to a dominant signal (e.g. removing a batch effect captured by PC1). 0 disables this step.

Details

PCA is computed once at app startup using the factors above. All interactive controls in the viewer (view mode, color-by, label-by, 3D axis selection, biplot thresholds, number of components to display) affect only rendering — they do not change the underlying computation. To change the computation, update the operator properties and re-run the step.

The implementation uses the dual PCA / Gram-matrix formulation (Householder tridiagonalisation + QL iteration for the eigendecomposition), which is efficient for the common Tercen case of many variables and relatively few observations.

See Also

pca_shiny — original ShinyR implementation