Module Eigengene

Extract the leading principal component of a gene-by-sample module.

Description

A module eigengene summarizes coordinated gene profiles with a leading sample-space component.

A module eigengene summarizes coordinated gene profiles with a leading sample-space component. Centering each gene removes its average but does not equalize gene variances.^[1]

Extract the leading principal component of a gene-by-sample module.

When to use Module Eigengene

Use this tool to extract the leading principal component of a gene-by-sample module.

Prepare the upstream counts, expression values, sequences or record lists requested by this tool. Keep corresponding features in the same order and confirm count units, transformations and caller-selected criteria.

How to use Module Eigengene and understand its results

Prepare the upstream counts, expression values, sequences or record lists requested by this tool. Keep corresponding features in the same order and confirm count units, transformations and caller-selected criteria.
Enter expressionByGene, iterations using the field table and calculation rules, then run the tool.
Read eigengene, explainedVarianceFraction. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
expressionByGene	Expression by gene	list of list of number	Required
iterations	Iterations	number	Required

Enter nonempty gene rows with at least two aligned sample columns and iterations at least one. Center each row, build the Gram matrix and require positive trace. Start with vector [2,1,…], repeat the specified iteration bound, orient its first appreciable component positive and return scaled scores with eigenvalue/trace.

G=X_centeredᵀX_centered; power iterate v←Gv/|Gv|; eigengene=v sqrt(vᵀGv)

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
eigengene	Eigengene	list of number
explainedVarianceFraction	Explained variance fraction	number

json

{
  "input": {
    "expressionByGene": [
      [
        1,
        2,
        3
      ],
      [
        2,
        4,
        6
      ]
    ],
    "iterations": 20
  },
  "output": {
    "eigengene": [
      2.2360679774997894,
      0,
      -2.2360679774997894
    ],
    "explainedVarianceFraction": 0.9999999999999998
  }
}

Limitations and assumptions

No variance standardization or convergence test is applied. A fixed starting vector can miss a dominant eigenspace, and a zero multiplication stops early. Fractional iteration bounds use loop comparison and may round upward operationally.

Numeric inputs must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Matrices are limited to 128 rows and 128 entries per row; other lists are limited to 100,000 entries. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Hub Gene Rank: Rank module genes by supplied eigengene connectivity. For a complementary task, use Correlation Distance Matrix: Build a Pearson correlation-distance matrix for hierarchical clustering.

References

Module Eigengene — tool.io

Similar or alternative tools

Hub Gene Rank
Rank module genes by supplied eigengene connectivity.
Correlation Distance Matrix
Build a Pearson correlation-distance matrix for hierarchical clustering.