RunPagerank

Full name SMRUCC.genomics.Analysis.HTS.FELLA.Core.RunPagerank Assembly SMRUCC.genomics.Analysis.HTS.FELLA Members 3

Personalized PageRank analysis for FELLA.

The PageRank method uses the personalized PageRank algorithm to propagate importance from input compounds through the KEGG network.

Mathematical formulation: M = transition matrix (column-stochastic) d = damping factor (default 0.85) p = personalization vector (uniform over input compounds) PR = (1-d) (I - dM)^{-1} * p

The damping factor d controls the trade-off between:

Following the network structure (d close to 1)
Restarting from input compounds (d close to 0)

P-score computation follows the same approach as diffusion: z_i = (pr_i - mu_i) / sigma_i p_i = Phi(z_i)

01 Syntax

SMRUCC.genomics.Analysis.HTS.FELLA.Core.RunPagerank

02 Methods

Name	Overloads	Summary
Run	1	Run PageRank analysis with specified approximation method.
ComputePScoresNormality	1	Compute p-scores using the normality approximation.
ComputePScoresSimulation	1	Compute p-scores using Monte Carlo simulation.

03 Members

method Run #

Run(FellaUser, FellaData, ApproximationMethod, Int32)

Run PageRank analysis with specified approximation method.

Parameters

Name	Type	Description
`user`	FellaUser	FELLA user object with defined input compounds
`data`	FellaData	FELLA data object with precomputed PageRank matrix
`approx`	ApproximationMethod	Approximation method for p-value computation
`nSim`	`Int32`	Number of Monte Carlo iterations (only used if approx = Simulation)

Returns

Enrichment result with PageRank scores and p-scores for all nodes

method ComputePScoresNormality #

ComputePScoresNormality(Double(), FellaData, EnrichmentResult, Int32)

Compute p-scores using the normality approximation.

method ComputePScoresSimulation #

ComputePScoresSimulation(Double(), Double(), FellaData, EnrichmentResult, Int32, Int32)

Compute p-scores using Monte Carlo simulation.