CoGAPS {CoGAPS} | R Documentation

## CoGAPS Matrix Factorization Algorithm

### Description

CoGAPS Matrix Factorization Algorithm

### Usage

    CoGAPS(D, S, nFactor = 7, nEquil = 1000, nSample = 1000,
      nOutputs = 1000, nSnapshots = 0, alphaA = 0.01, alphaP = 0.01,
      maxGibbmassA = 100, maxGibbmassP = 100, seed = -1, messages = TRUE,
      singleCellRNASeq = FALSE, whichMatrixFixed = "N",
      fixedPatterns = matrix(0), checkpointInterval = 0,
      checkpointFile = "gaps_checkpoint.out", ...)

### Arguments

`D` | data matrix
`S` | uncertainty matrix (std devs for chi-squared of Log Likelihood)
`nFactor` | number of patterns (basis vectors, metagenes), which must be greater than or equal to the number of rows of FP
`nEquil` | number of iterations for burn-in
`nSample` | number of iterations for sampling
`nOutputs` | how often to print status into R by iterations
`nSnapshots` | the number of individual samples to capture
`alphaA` | sparsity parameter for A domain
`alphaP` | sparsity parameter for P domain
`maxGibbmassA` | limit truncated normal to max size
`maxGibbmassP` | limit truncated normal to max size
`seed` | a positive seed is used as-is, while any negative seed tells the algorithm to pick a seed based on the current time
`messages` | display progress messages
`singleCellRNASeq` | indicates if the data is single cell RNA-seq data
`whichMatrixFixed` | character to indicate whether A or P matric contains the fixed patterns
`fixedPatterns` | matrix of fixed values in either A or P matrix
`checkpointInterval` | time (in seconds) between creating a checkpoint
`checkpointFile` | name of the checkpoint file
`...` | keeps backwards compatibility with arguments from older versions

### Details

calls the C++ MCMC code and performs Bayesian matrix factorization returning the two matrices that reconstruct the data matrix

### Value

list with A and P matrix estimates

### Examples

    data(SimpSim)
    result <- CoGAPS(SimpSim.D, SimpSim.S, nFactor=3, nOutputs=250)

[Package CoGAPS version 3.0.2 Index]